phd healthcare analytics

• Academic Programs

PhD in Health Sciences Informatics Program

The PhD is a campus based program only.

Directed by Hadi Kharrazi, MD, PhD, the program offers the opportunity to participate in ground breaking research projects in clinical informatics at one of the world’s finest medical schools. In keeping with the tradition of the Johns Hopkins University and the Johns Hopkins Hospital, the program seeks excellence and commitment in its students to further the prevention and management of disease through the continued exploration and development of health IT. Division resources include a highly collaborative clinical faculty committed to research at the patient, provider and system levels. The admissions process will be highly selective and finely calibrated to complement the expertise of faculty mentors.

Areas of research:

• Clinical Decision Support
• Global Health Informatics
• Health Information Exchange (HIE)
• Human Computer Interaction
• Multi-Center Real World Data
• Patient Quality & Safety
• Population Health Analytics
• Precision Medicine Analytics
• Standard Terminologies
• Telemedicine
• Translational Bioinformatics

Vivien Thomas Scholars Initiative

As diverse PhD students at Johns Hopkins, Vivien Thomas scholars will receive the academic and financial support needed to ensure their success, including up to six years of full tuition support, a stipend, health insurance and other benefits, along with significant mentorship, research, professional development and community-building opportunities.

Click here to read more.

Application Requirements for the PhD in Health Sciences Informatics

Applicants with the following degrees and qualifications will be considered:

• BA or BS, or
• BA or BS, and a minimum of five years professional experience in a relevant field, or
• MA, MLS, MD or other PhD, with no further requirements.

"Relevant fields" include medicine, dentistry, veterinary science, nursing, ancillary clinical sciences, public health, librarianship, biomedical basic science, bioengineering and pharmaceutical sciences and computer and information science. An undergraduate minor or major in information or computer science is highly desirable.

The Application Process

Applications for the class entering in academic year 2024-2025 will be accepted starting in September 1, 2023 through December 15, 2023. (The application is made available through the Johns Hopkins School of Medicine here. )

Please note that paper applications are no longer accepted. The supporting documents listed below must be received by the SOM admissions office by December 15, 2023 . Applications will not be reviewed until they are complete and we have all supporting letters and documentation. 

• Curriculum vitae
• Three letters of recommendation
• Official transcript of school record
• Certification of terminal degree
• Statement of Purpose
• You may also submit a portfolio of published research, or samples of website or system development to support your application if you wish.

This program does not require the GRE.

Important Transcript Information

It is the policy of the School of Medicine Registrar that new students have a complete set of original transcripts on file prior to matriculation showing the degree awarded and date. An official transcript is one that is addressed to the Office of Graduate Student Affairs and sent directly from the granting institution to Johns Hopkins University School of Medicine, Office of Graduate Student Affairs, 1830 East Monument Street, Ste. 620, Baltimore, MD 21287. The transcript envelope must be sealed and stamped on arrival at the OGSA office. Transcripts addressed to the student can not be accepted even if they are sent to the OGSA address above.

Program Description

Individuals wishing to prepare themselves for careers as independent researchers in health sciences informatics, with applications experience in informatics across the entire health/healthcare life cycle, should apply for admission to the doctoral program. The following are specific requirements:

• A student should plan and successfully complete a coherent program of study including the core curriculum, Oral Examination, and additional requirements of the Research Master’s program. In addition, doctoral candidates are expected to take at least two more advanced courses. In the first year, two or three research rotations are strongly encouraged. The Master’s requirements, as well as the Oral Examination, should be completed by the end of the second year in the program. Doctoral students routinely will not be receiving a Masters degree on their way to the PhD; particular exceptions will be decided on a case-by-case basis. Doctoral students are generally advanced to PhD candidacy after passing the Oral Examination. A student’s academic advisor has primary responsibility for the adequacy of the program, which is regularly reviewed by the Doctoral Study Committee (DSC) of the Health Sciences Informatics (HSI) program.
• The student must have a minimum of two consecutive semesters (four quarters) of full time enrollment and resident on campus as a graduate student
• To remain in the PhD program, each student must receive no less than an B in core courses, must attain a grade point average (GPA) as outlined above, and must pass a comprehensive exam covering introductory level graduate material in any curriculum category in which he or she fails to attain a GPA of 3.0. The student must fulfill these requirements and apply for admission to candidacy for the PhD by the end of six quarters of study (excluding summers). In addition, reasonable progress in the student’s research activities is expected of all doctoral candidates.
• During the third year of training, generally in the Winter Quarter, each doctoral student is required to present a pre-proposal seminar that describes evolving research plans and allows program faculty to assure that the student is making good progress toward the definition of a doctoral dissertation topic. By the end of nine quarters (excluding summers), each student must orally present a thesis proposal to a dissertation committee that generally includes at least one member of the Graduate Study Committee of the Health Sciences Informatics program. The committee determines whether the student’s general knowledge of the field, and the details of the planned thesis, are sufficient to justify proceeding with the dissertation.
• As part of the training for the PhD, each student is required to be a teaching assistant for two courses approved by the DHSI Executive Committee; one should be completed in the first two years of study.
• The most important requirement for the PhD degree is the dissertation. Prior to the oral dissertation proposal and defense, each student must secure the agreement of a member of the program faculty to act as dissertation advisor. The University Preliminary Oral Exam (UPO) committee must consist of five faculty members, two of whom to be from outside the program, with the chair of the UPO committee coming from outside the program. The Thesis Committee comprises the principal advisor, who must be an active member of the HSI program faculty, and other, approved non HSI faculty members. Thesis committees must meet formally at least annually. Upon completion of the thesis research, each student must then prepare a formal written thesis, based on guidelines provide by the Doctor of Philosophy Board of the University.
• No oral examination is required upon completion of the dissertation. The oral defense of the dissertation proposal satisfies the University oral examination requirement.
• The student is expected to demonstrate the ability to present scholarly material orally and present his or her research in a lecture at a formal seminar, lecture, or scientific conference.
• The dissertation must be accepted by a reading committee composed of the principal dissertation advisor, a member of the program faculty, and a third member chosen from anywhere within the University. All University guidelines for thesis preparation and final graduation must be met.
• The Executive Committee documents that all Divisional or committee requirements have been met.

Program Handbook

Details about our program's policies are provided in our handbook here .

In addition, mentoring advising and resources are provided in this appendix .

An annual discussion and planning form is provided here for your reference.

Course Offerings

The proposed curriculum is founded on four high-level principles:

• Balance between theory and research, and between breadth and depth of knowledge: By providing a mix of research and practical experiences and a mix of curricular requirements.
• Student-oriented curriculum design: By creating the curriculum around student needs, background, and goals, and aiming at long-term competence using a combination of broadly-applicable methodological knowledge, and a strong emphasis on self-learning skills.
• Teaching and research excellence: By placing emphasis on student and teaching quality rather than quantity, by concentrating on targeted areas of biomedical informatics, and by close student guidance and supervision.
• Developing leadership: By modeling professional behavior locally and nationally.

The Health Sciences Informatics Doctoral Curriculum integrates knowledge and skills from:

• Foundations of biomedical informatics: Includes the lifecycle of information systems, decision support.
• Information and computer science: E.g. computer organization, computability, complexity, operating systems, networks, compilers and formal languages, data bases, software engineering, programming languages, design and analysis of algorithms, data structures.
• Research methodology: Includes research design, epidemiology, and systems evaluation; mathematics for computer science (discrete mathematics, probability theory), mathematical statistics, applied statistics, mathematics for statistics (linear algebra, sampling theory, statistical inference theory, probability).
• Implementation sciences: Methods from the social sciences (e.g., organizational behavior and management, evaluation, ethics, health policy, communication, cognitive learning sciences, psychology, and sociological knowledge and methods.) Health economics, evidence-based practice, safety, quality.
• Specific informatics domains: Clinical informatics, public health informatics.
• Practical experience: Experience in informatics research, experience with health information technology.

To achieve in-depth learning of the above knowledge and skills we adopt a student-oriented curriculum design, whereby we identify “teaching or learning processes,” that is, structured activities geared towards learning (i.e., courses/projects/assignments, seminars, examinations, defenses, theses, teaching requirements, directed study, research, service, internships). These processes were selected, adapted, or created in order to meet a set of pre-specified learning objectives that were identified by the faculty as being important for graduates to master.

The requirements are:

• 35 quarter credits/17.5 semester credits Core Courses (9 courses + research seminar 8 quarters)
• 48 quarter credits/24 semester credits Electives (may include optional practicum/research)
• 6 quarter credits/3 semester credits ME 250.855 practicum/ research rotation
• 36 quarter credits/18 semester credits ME 250.854 Mentored Research
• 125 TOTAL quarter credits/62.5 semester credits

Students are required to be trained in HIPAA and IRB submission, and to take the Course of Research Ethics.

IRB Compliance Training:

https://www.hopkinsmedicine.org/institutional_review_board/training_req…

Health Sciences Informatics, PhD

School of medicine.

The Ph.D. in Health Sciences Informatics offers the opportunity to participate in ground-breaking research projects in clinical informatics and data science at one of the world’s finest biomedical research institutions. In keeping with the traditions of the Johns Hopkins University and the Johns Hopkins Hospital, the Ph.D. program seeks excellence and commitment in its students to further the prevention and management of disease through the continued exploration and development of health informatics, health IT, and data science. Resources include a highly collaborative clinical faculty committed to research at the patient, provider, and system levels. The admissions process will be highly selective and finely calibrated to complement the expertise of faculty mentors.    

Areas of research:

• Clinical Decision Support
• Global Health Informatics
• Health Information Exchange (HIE)
• Human Computer Interaction
• Multi-Center Real World Data
• Patient Quality & Safety
• Population Health Analytics
• Precision Medicine Analytics
• Standard Terminologies
• Telemedicine
• Translational Bioinformatics

Individuals wishing to prepare themselves for careers as independent researchers in health sciences informatics, with applications experience in informatics across the entire health/healthcare life cycle, should apply for admission to the doctoral program.

Admission Criteria

Applicants with the following types of degrees and qualifications will be considered:

• BA or BS, with relevant technical and quantitative competencies and a record of scientific accomplishment as an undergraduate; 
• BA or BS, with relevant technical and quantitative competencies and a minimum of five years professional experience in a relevant field (e.g., biomedical research, data science, public health, etc.); or
• MA, MS, MPH, MLIS, MD, PhD, or other terminal degree, with relevant technical and quantitative competencies

Relevant fields include: medicine, dentistry, veterinary science, nursing, ancillary clinical sciences, public health, librarianship, biomedical science, bioengineering and pharmaceutical sciences, and computer and information science. An undergraduate minor or major in information or computer science is highly desirable.

The application is made available online through Johns Hopkins School of Medicine's website . Please note that paper applications are no longer accepted. The supporting documents listed below must be received by the SOM admissions office by December 15 of the following year. Applications will not be reviewed until they are complete and we have all supporting letters and documentation.

• Curriculum Vitae (including list of peer-reviewed publications and scientific presentations)
• Three Letters of Recommendation
• Statement of Purpose
• Official Transcripts from undergraduate and any graduate studies
• Certification of terminal degree
• You are also encouraged to submit a portfolio of published research, writing samples, and/or samples of website or system development

Please track submission of supporting documentation through the SLATE admissions portal.

If you have questions about your qualifications for this program, please contact [email protected]

Program Requirements

The PhD curriculum will be highly customized based on the student's background and needs. Specific courses and milestones will be developed in partnership with the student's advisor and the PhD Program Director.

The proposed curriculum is founded on four high-level principles:

• Achieving a balance between theory and research, and between breadth and depth of knowledge
• Creating a curriculum around student needs, background, and goals
• Teaching and research excellence
• Modeling professional behavior locally and nationally.

Individualized curriculum plans will be developed to build proficiencies in the following areas:

• Foundations of biomedical informatics: e.g., lifecycle of information systems, decision support
• Information and computer science: e.g., software engineering, programming languages, design and analysis of algorithms, data structures.
• Research methodology: research design, epidemiology, and systems evaluation; mathematics for computer science (discrete mathematics, probability theory), mathematical statistics, applied statistics, mathematics for statistics (linear algebra, sampling theory, statistical inference theory, probability); ethnographic methods.
• Implementation sciences: methods from the social sciences (e.g., organizational behavior and management, evaluation, ethics, health policy, communication, cognitive learning sciences, psychology, and sociological knowledge and methods), health economics, evidence-based practice, safety, quality.
• Specific informatics domains: clinical informatics, public health informatics, analytics
• Practical experience: experience in informatics research, experience with health information technology.

Basic Requirements & Credit Distribution

• 15 "core" quarter credits (5 courses)
• 8 quarter credits of Student Seminar & Grand Rounds
• 60 elective quarter credits
• 6 quarter credits practicum/research rotation
• 36 mentored research quarter credits (12 in year 1, 24 in year 2)
• Research Ethics

Master of Science in Health Data Science

Leverage your skills in statistics, computer science & software engineering and begin your career in the booming field of health data science

The Master of Science (SM) in Health Data Science is designed to provide rigorous quantitative training and essential statistical and computing skills needed to manage and analyze health science data to address important questions in public health and biomedical sciences.

The 16-month program blends strong statistical and computational training to solve emerging problems in public health and the biomedical sciences. This training will enable students to manage and analyze massive, noisy data sets and learn how to interpret their findings. The program will provide training in three principal pillars of health data science: statistics, computing, and health sciences.

Students in the program will learn to:

• Wrangle and transform data to perform meaningful analyses
• Visualize and interpret data and effectively communicate results and findings
• Apply statistical methods to draw scientific conclusions from data
• Utilize statistical models and machine learning
• Apply methods for big data to reveal patterns, trends, and associations
• Employ high-performance scientific computing and software engineering
• Collaborate with a team on a semester-long, data driven research project

The SM in Health Data Science is designed to be a terminal professional degree, giving students essential skills for the job market. At the same time, it provides a strong foundation for students interested in obtaining a PhD in biostatistics or other quantitative or computational science with an emphasis in data science and its applications in health science.

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Learn how to apply to the Mailman School of Public Health. 

Master of Science

Public health data science.

Data science is an emerging and dynamic discipline that draws strength from many domains. Although the definition of this field is still evolving, hallmarks of data science include: the principled visualization and analysis of data; the recognition of research reproducibility and replicability; the need to communicate and disseminate results effectively; and an emphasis on substantive collaborative engagement in interdisciplinary research.

The Public Health Data Science (PHDS) track retains the core training in biostatistical theory, methods, and applications, but adds a distinct emphasis on modern approaches to statistical learning, reproducible and transparent code, and data management. The length of the 36-credit program varies with the background, training, and experience of individual students. Most complete the program within two years (four semesters) and begin their studies in the fall semester.

In addition to fulfilling their course work, all PHDS students complete a one-term practicum and capstone experience. The practicum experience is an important element of the PHDS training, as it provides students with the opportunity to apply knowledge learned in the classroom to real-world situations and offers a taste of future career opportunities.

Admissions Information

Applicants should have some background in college mathematics, including at least a year of calculus. A semester of linear/matrix algebra is highly encouraged. Students with strong scores on the quantitative section of the GRE are given first preference. As with all Biostatistics programs, the most important ingredients for the MS/PHDS are a facility for quantitative reasoning and a true enjoyment of working with data.

Search the Columbia Directory to find current students in the program.

View competencies, course requirements, sample schedules, and more in our Academics section.

Paul McCullough

• Health Informatics

Informatics is the study of the practical use of information and information technologies in healthcare. The  Health Informatics  master's track trains in the effective use of informatics to organize, coordinate, manage and deliver high quality care. Students will learn about healthcare organization and delivery, standards and interoperability, health data management, environmental health informatics, clinical informatics, biostatistics, healthcare transformation, and more.

We prepare students for a wide range of careers — including those in healthcare delivery organizations, insurance, and consulting. Potential job opportunities include a variety of positions in data analysis, policy analysis, health information technology, process improvement, quality assessment, team management, organizational strategy, and leadership.

A concentration in health informatics from Weill Cornell Medicine includes a curriculum that provides a strong foundation in healthcare research methods with specialized training in health information systems, impact assessment, data analytics and computational techniques. Each student acquires hands-on experience through a faculty-mentored research project that begins in the first term and culminates in a capstone/portfolio final project.

This degree track has close ties to other departments within Weill Cornell Medicine and Cornell University, Cornell Tech, and NewYork-Presbyterian Hospital. Full-time students can complete the program in 12 months, and part-time students in 18-24 months.

Students come from backgrounds in healthcare, biomedical sciences, and computer science. Their diversity creates a unique, collaborative learning environment.

Collaboration

Being in New York City is a huge asset for our program. Local institutions collaborating with Weill Cornell Medicine include NewYork-Presbyterian Hospital, Memorial Sloan Kettering Cancer Center, the Hospital for Special Surgery, The Rockefeller University, the State Department of Health, the New York City Department of Health and Mental Hygiene, and more.

Students innovate through cutting edge topics in healthcare such as big data, data mining, and analytics. This track offers a blend of healthcare system knowledge and key technical skills.

Opportunities

Our alumni hold positions in data and policy analysis, health information technology, process improvement, quality assessment, and more. Many alumni pursue doctoral studies.

Our faculty are nationally recognized experts in a wide range of fields including health informatics, biostatistics, epidemiology, comparative effectiveness, and health outcomes. They have an enormous reservoir of both theoretical knowledge and practical experience in many different aspects of healthcare, as well as personal networks of connections with academia and industry.

The culminating capstone aims to help students discover and develop new and effective ways of managing and working together with stakeholders in healthcare while gaining real-world experience.

Our faculty are nationally recognized experts in a wide range of fields including informatics, biostatistics, computational methods, comparative effectiveness, and health policy, and our NYC location allows for collaboration between local healthcare industry experts.

Student Handbook

To view the student handbook, click here .

Master's Tracks

• Biostatistics and Data Science
• Health Policy and Economics

Apply to Weill Cornell Medicine Graduate School

Contact information.

Chair's Office 402 East 67th Street New York, NY 10065 (646) 962-8009

425 East 61st Street New York, NY 10065 (646) 962-9409

DiscoverDataScience.org

Ph.D. in Health Informatics – Guide to Choosing a Great Program

By Kat Campise, Data Scientist, Ph.D.

Undoubtedly, everyone in the U.S. will access the health care system at some point in their lives. Moreover, we’re in an era where the large population of Baby Boomers and elder Gen X’ers are increasing their health care utilization. This translates into a greater need for massive data and information collection, storage, and dissemination on the part of health care providers, agencies, and organizations.

A Ph.D. in Health Informatics is designed for students looking for a career in research or academia.

For those hoping to a pursue a career in industry, a master’s in health informatics is probably a better fit.

Furthermore, the Federal government has established regulatory controls over data privacy and limits who can access medical records (i.e., HIPAA ), and there may be additional state laws that require adherence as well. As a consequence, those who handle or manage medical data need to have specific knowledge, skills, and abilities in HIPPA compliant database and information systems.

On an additional note, HealthTech and InsurTech are industries that will experience substantial growth as electronic medical records become the norm. The continued uptick in medical and general health wearables, which will transmit real-time or batched data, will also push more data into health care databases.

For these reasons, the need for health informatics professionals is growing at a pace that is faster than average when compared to other professions. Although the demand for health informatics isn’t as widely advertised as data science or software engineering , the career opportunity does exist for those who are interested in helping to improve the health care system from a technological and informational perspective.

Health Informaticians: What Do They Do, Exactly?

While each employer may have a different title within the framework of health informatics, there are essentially two primary roles: health information technician (HIT) and the health information manager (HIM). Both positions have important responsibilities that help health care providers and the overall health care system to deliver high-quality health care.

Health Information Technicians

Per the Bureau of Labor and Statistics Occupational Outlook Handbook, health care technicians “ organize and manage health information data .” Health information technology professionals may assist in building the information system, documenting patient data, determine the best method for managing information transmitted by each health care stakeholder (e.g., doctors, nurses, pharmacists, patients, etc.), and ensure the accuracy of patient/provider data as it funnels through the information system. Typically, health information technician is an entry-level position that frequently requires at least an associate’s degree or certification in health information technology.

Health Information Managers

Health information managers oversee the HITs (specifically) and the health information management department (in general). Moreso than health technicians, health information managers are likely to assist with the design and implementation of health information systems. They have the added responsibility of managing budgets which means that candidates for this position will need to have the knowledge and practical experience within the business side of health care. Familiarity or expertise with the existing database and information systems utilized within the health care field is also an employer expectation. Many employers require either a Registered Health Information Administrator (RHIA) or a Registered Health Information Technician (HRIT) certification along with a Bachelor’s Degree in Health Information Technology and 5 years experience in an HIM capacity. A Ph.D. in Health Informatics may override the certification requirements and place employee candidates at the top of the application pile. One pro tip is that, if you’re determined to enter the health informatics career path, networking with those already in the field tends to yield higher responses during your job search. The upside of a Ph.D. is that most programs require submitting academic papers and presenting research at industry conferences. This is a prime opportunity to make connections with potential employers.

4 Steps to Choosing an On-Campus Ph.D. in Health Informatics

Ph.D. programs are arduous, and a Ph.D. in Health Informatics is no exception. Everyone who is considering the completion of a Ph.D. needs to understand that reality before they commit the next 4 to 7 years saturating themselves with research and writing. There are many hoops to jump through at each stage of the Ph.D. journey. Since Ph.D. level education is geared towards churning out academics, meaning that graduates stay in academia as a career, many Ph.D. graduates have a challenging time trying to transition from academia to their target industry. You’ll still need to market yourself to employers just like everyone else. Having a Ph.D. doesn’t magically bring employers to your door, but it can signal that you’ve attained in-depth expertise in the field. It will largely be up to you to clearly communicate the value you can provide to the organization, and how having a Ph.D. helps support that value.

Step 1: Determine your location and time availability

Even in the age of online degrees, Ph.D. programs continue to be primarily campus-based. Added to this is the fact that not all universities carry the same Ph.D. programs, which holds true for a Ph.D. in Health Informatics. Therefore, you’ll need to do some research to determine whether or not any of the local universities offer this degree program. If not, and you’re specifically focused on health informatics, then it’s highly likely that you’ll be faced with the possibility of moving to another location to complete the degree. Likewise, most Ph.D.s are full-time undertakings. Not only should you factor in the total time from start to completion (e.g., 4 to 7 years), but also whether or not you can manage both the Ph.D. requirements and a job. Funneling down a bit further, daily or weekly travel time between home, school, and work (if you do need to also maintain a job), tend to cut into study time. Granted, if reliable public transportation is available, then you can definitely utilize the time for additional study.

Step 2: Review the curriculum

Is health informatics of deep interest to you? You’ll be performing a profound analytical dive into the subject over an extended period, and there may be several different program tracks to choose from. For example, the University of Minnesota’s on-campus Ph.D. in Health Informatics offers four different concentration options: Clinical Informatics, Data Science and Informatics for Learning Health Systems, Translational Bioinformatics, and Precision and Personalized Medicine Informatics. Reviewing the curriculum of each along with comparing and contrasting the course completion requirements will help for identifying any knowledge gaps that may be the cause for additional “catching up” either through self-study or taking additional courses. Returning to Minnesota’s Ph.D. tracks, both the Bioinformatics and Data Science sub-disciplines have machine learning coursework. If you’ve not yet been exposed to machine learning in any capacity, even though you’re interested in developing that skill, then you’ll need to spend more time and money to achieve a certain proficiency level. Accordingly, your interest level and the available curriculum are substantial factors in successfully attaining a Ph.D.

Step 3: Perform a cost-benefit analysis

There is a cost trade-off to examine when assessing a decision to invest copious amounts of time and money into continuing formal education. As discussed above, you’ll be focussing a massive amount of energy into navigating the Ph.D. demands: attending courses, performing qualitative and quantitative research, writing extensive academic papers, attending conferences, and preparing a dissertation (which frequently involves assembling a committee). If you’re also employed during this time, especially full-time, it’s very easy to become drained by a lack of work-school-life balance. Also, you’ll be applying your knowledge within an academic context, which may not directly apply to the work performed at your job. Or, if you’re able to focus on the Ph.D. in Health Informatics on a full-time basis, there is a high probability that you’ll lose earnings potential while you’re finishing the degree. Then, there are the actual costs of the Ph.D. program itself. If you’re entering a university where you’re an established resident of the state (this is true mainly for state schools), then tuition will be lower. However, tuition and fees even for state residents can range from just below $16,000 to over $35,000 , including tuition, fees, books, and living expenses.

Step 4: Analyze the admission requirements

Overwhelmingly, one of the main admission requirements for a Ph.D. in Health Informatics (and just about any other Ph.D. program) will be attaining a minimum score on the GRE. From there, the necessary undergraduate or master’s degree and GPA average will tend to diverge depending on the university. For instance, if the Ph.D. in Data Science and Informatics for Learning Health Systems from the University of Minnesota is your goal, then you’ll need at least a 3.5 GPA, a minimum of two courses in a life or health science (6 semester credits), and the completion of the following college math courses: calculus, linear algebra, statistics . All of the above is only half of the admission requirements for most Ph.D. programs. You’ll also likely be tasked with completing a personal statement that describes your research interests, letters of recommendation from supervisors who have direct knowledge of prior academic work (usually 2 to 3 in number), and an interview; sometimes, the interview can be conducted via web conferencing, but many universities require an in-person interview. All of this equates to more time and money invested even before you are admitted.

School Listings

University of Arkansas at Little Rock – Little Rock, Arkansas Ph.D. in Bioinformatics

Delivery Method: Campus GRE Required: Required 2020-2021 Tuition: $320 per credit (resident), $725 per credit (non resident)

View Course Offerings

• Related Programs

Health Analytics

About the research center, we work to improve the health of individuals and the health care system through data-driven methods and understanding of health processes..

Our work builds upon the work of teams of Columbia researchers in medicine, biology, public health, informatics, computer science, applied mathematics, and statistics. The Health Analytics Center is located at the Columbia University Medical Center.

Research Highlights

J. Michael Schmidt (Neurology)

Clinicians in the Neuro-ICU may be confronted daily by over 200 time-related variables for each patient; yet we know from cognitive science that people are only able to understand the relatedness of two variables without help. We are investigating how to help clinicians make sense of real-time streams of physiological data as well as of their relationships and trends. The objective of this project is to demonstrate that interactive data visualizations designed to transform and consolidate complex multimodal physiological data into integrated interactive displays will reduce clinician cognitive load and will result in reductions in medical error and improvements in patient care, safety, and efficiency. This project is a collaboration with the Draper Laboratory and funded by the DoD Telemedicine and Advanced Technology Research Center (TATRC) and the Dana Foundation.

Noémie Elhadad (Biomedical Informatics), Chris Wiggins (Applied Mathematics and Applied Physics)

Physicians treating patients in the clinic, on the floor, or in the emergency room are faced with an overwhelming amount of complex information about their patients, with little time to review it. HARVEST is an interactive patient record summarization system, which aims to support physicians in their information workflow. It extracts content from the patient notes, where key clinical information resides, aggregates and presents information through time. HARVEST is currently deployed at NewYork-Presbyterian Hospital. It relies on a distributed platform for processing data as they get pushed into the electronic health record. We are now investigating summarization models of patient records that identify their co-morbidities and their status through time, by modeling all observations in the record, from the notes to laboratory test measurements and other structured information like billing codes. 

Sean Luo (Physicians and Surgeons, Psychiatry), Min Qian (Public Health, Biostatistics), Kara Rudolph (Public Health, Epidemiology)

Pharmacologic treatment of opioid use disorder is complicated by the likely absence of a one-sizefits-all best approach; rather, “optimal” dose and dose adjustment are hypothesized to depend on person-level factors, including factors that change over time, reflecting how well the individual is responding to treatment. This team will use harmonized data from multiple existing clinical trials with natural variability in medication dose adjustments over time to 1) learn optimal dosing strategies, and 2) estimate the extent to which such optimal dosing strategies could reduce risk of treatment dropout and relapse.

Billy Caceres (Nursing), Ipek Ensari (DSI), Kasey Jackman (Nursing)

This pilot study will use data science techniques to leverage ecological momentary assessment and consumer sleep technology to phenotype sleep health profiles in Black and Latinx sexual and gender minority adults. The investigators will use 30 days of daily electronic diaries and actigraphy to examine the associations of daily exposure to minority stressors (such as experiences of discrimination and anticipated discrimination) with sleep health among Black and Latinx sexual and gender minority adults.

Aviv Landau (DSI), Desmond Patton (DSI and Social Work), Maxim Topaz (Nursing)

Child abuse and neglect is a social problem that has reached epidemic proportions. The broad adoption of electronic health records in clinical settings offers a new avenue for addressing this epidemic. This team will develop an innovative artificial intelligence system to detect and assess risk for child abuse and neglect within hospital settings that would prioritize the prevention and reduction of bias against Black and Latinx communities

Itsik Pe’er (Engineering, Computer Science), Anne-Catrin Uhlemann (Physicians and Surgeons, Medicine)

This project will develop methods for temporal analysis of gut microbiome compositions to better define the risk of infections in liver transplant recipients. The project team will integrate existing coarse resolution data with newly collected deep metagenomics and metabolomics data.

Piero Dalerba (Physicians and Surgeons, Pathology and Cell Biology), Jiahnhua Hu (Public Health, Biostatistics), Mary Beth Terry (Public Health, Epidemiology), Wan Yang (Public Health, Epidemiology)

Using multiple nationally representative large-scale exposure and cancer incidence datasets, this project will build a novel model-inference system to study the dynamics of colorectal cancer, test a range of risk mechanisms over the life course, and identify key risk factors underlying the recent increase in young onset colorectal cancer incidence in the United States to support more effective early prevention. This project is jointly funded with Cancer Dynamics.

Elham Azizi (Engineering, Biomedical Engineering and Cancer Dynamics), Jellert Gaublomme (Arts and Sciences, Biological Sciences), Brent Stockwell (Arts and Sciences, Biological Sciences)

This project will leverage machine learning techniques to combine two types of single-cell data modalities with the goal of achieving a more comprehensive characterization of heterogeneous cell states in the tumor microenvironment. Specifically, the team will develop probabilistic models to elucidate the role of intercellular interactions in driving susceptibility of treatment-resistant mesenchymal tumor cells to a newly discovered ferroptotic vulnerability, which could offer a therapeutic avenue to prevent survival of these cancer cells that are prone to metastasis. This project is jointly funded with Cancer Dynamics.

Sergey Kalachikov (Engineering, Chemical Engineering), Rene Hen (Physicians and Surgeons, Neuroscience)

This team will incorporate data on antidepressant resistance and drug response profiles, their own behavioral and RNA sequence data, and publicly available large-scale data sets to help identify candidate genes that implicate specific morphological changes in the brain. The long term aim of this research is to reveal specific gene pathways and regulatory networks associated with treatment-resistant Major Depressive Disorder.

Marianthi-Anna Kioumourtzoglou (Public Health, Environmental Health Sciences), John Paisley (Engineering, Electrical Engineering), Kai Ruggeri (Public Health, Health Policy and Management)

Personalized approaches to behavioral interventions, known as nudges, may improve access to health care in low-income communities. Using health, environment, transportation, and financial data, this project will build smart nudges that adapt to individual needs by using innovative methods in machine learning and data science.

Roxana Geambasu (Engineering, Computer Science), Daniel Hsu (Engineering, Computer Science), Nicholas Tatonetti (Physicians and Surgeons, Biomedical Informatics)

Today, virtually every clinic and hospital–small or large–collects clinical information about their patients and aims to use these data to predict disease trajectories and discover new treatments. Unfortunately, these datasets, which vary vastly in size and type of information they contain, are almost always siloed behind institutional walls because of privacy concerns. This limits the scope and rigor of the research that can be done on these datasets. We are building an infrastructure system for sharing privacy-preserving machine learning models of large-scale, dynamic, clinical datasets. The system will enable medical researchers in small clinics or pharmaceutical companies to incorporate multitask feature models learned from big clinical datasets, such as New York Presbyterian’s Clinical Data Warehouse, to bootstrap their own machine learning models on top of their (potentially much smaller) clinical datasets. The multitask feature models protect the privacy of individual records in the large datasets through a rigorous method called differential privacy. We anticipate the system will vastly improve the pace of innovation in clinical data research while alleviating the privacy concerns.

David Blei (Arts and Sciences, Statistics; and Engineering, Computer Science), Anna Lasorella (Physicians and Surgeons, Pediatrics), Raul Rabadan (Physicians and Surgeons, Systems Biology), Wesley Tansey (Physicians and Surgeons, Systems Biology)

Precision medicine aims to find the right drug, for the right patient, at the right moment and at the right dose. This aim is particularly relevant in cancer, where standard therapies elicit very different responses across patients. This project’s goal is to model, predict, and target therapeutic sensitivity and resistance of cancer. The project will work to integrate Bayesian modeling with recently developed variational inference and deep learning methods, and apply them to large scale genomic and drug sensitivity data across many cancer types. The project will leverage the strong expertise of two leading teams in computational genomics and machine learning together with experimental labs across the Medical and Morningside campuses.

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Advances in computational speed and data availability, and the development of novel data analysis methods, have birthed a new field: data science. This new field requires a new type of researcher and actor: the rigorously trained, cross-disciplinary, and ethically responsible data scientist. Launched in Fall 2017, the pioneering CDS PhD Data Science program seeks to produce such researchers who are fluent in the emerging field of data science, and to develop a native environment for their education and training. The CDS PhD Data Science program has rapidly received widespread recognition and is considered among the top and most selective data science doctoral programs in the world. It has recently been recognized by the NSF through an NRT training grant.

The CDS PhD program model rigorously trains data scientists of the future who (1) develop methodology and harness statistical tools to find answers to questions that transcend the boundaries of traditional academic disciplines; (2) clearly communicate to extract crisp questions from big, heterogeneous, uncertain data; (3) effectively translate fundamental research insights into data science practice in the sciences, medicine, industry, and government; and (4) are aware of the ethical implications of their work.

Our programmatic mission is to nurture this new generation of data scientists, by designing and building a data science environment where methodological innovations are developed and translated successfully to domain applications, both scientific and social. Our vision is that combining fundamental research on the principles of data science with translational projects involving domain experts creates a virtuous cycle: Advances in data science methodology transform the process of discovery in the sciences, and enable effective data-driven governance in the public sector. At the same time, the demands of real-world translational projects will catalyze the creation of new data science methodologies. An essential ingredient of such methodologies is that they embed ethics and responsibility by design.

These objectives will be achieved by a combination of an innovative core curriculum, a novel data assistantship mechanism that provides training of skills transfer through rotations and internships, and communication and entrepreneurship modules. Students will be exposed to a wider range of fields than in more standard PhD programs while working with our interdisciplinary faculty. In particular, we are proud to offer a medical track for students eager to explore data science as applied to healthcare or to develop novel theoretical models stemming from medical questions.

In short, the CDS PhD Data Science program prepares students to become leaders in data science research and prepares them for outstanding careers in academia or industry. Successful candidates are guaranteed financial support in the form of tuition and a competitive stipend in the fall and spring semesters for up to five years.* We invite you to learn more through our webpage or by contacting  [email protected] .

*The Ph.D. program also offers students the opportunity to pursue their study and research with Data Science faculty based at NYU Shanghai. With this opportunity, students generally complete their coursework in New York City before moving full-time to Shanghai for their research. For more information, please visit the NYU Shanghai Ph.D. page .

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What is Health Care Analytics?

How the Data Revolution is Transforming Health Care

Health data analytics and health informatics, how health care analytics improves patient care, career opportunities in healthcare analytics, how health care analytics connects to him, certification and education: health care analytics and informatics, health care analytics faqs.

Can big data save lives? Advocates of the emerging field of health care analytics certainly believe it can.

Big data analytics is transforming the way organizations do business in dozens of industries. But the impact may ultimately be most profound in the field of medicine, where advanced health care analytics holds the potential to revolutionize patient care.

Big data is already having positive effects on many areas of health care, including:

• Advancements in telemedicine
• Enhanced patient engagement
• Wearables that provide real-time alerts
• Disease prevention/population health
• Improving/refining treatment standards
• Potential to help cure diseases
• Improved staffing efficiency
• Prevention of opioid abuse

As the nation’s top technology innovators partner with health care organizations to leverage valuable insights from the immense amount of health care data being generated and collected each day, the search is on for new ways to transform rapidly expanding databases into improved outcomes for patients and entire populations.

The transition from old-fashioned paper records to electronic health records (EHRs) for patients is one of the most significant factors contributing to today’s avalanche of health care data. However, there are many additional sources as well. These include:

• Clinical data from Computerized Physician Order Entry (CPOE)
• Real-time data from the new generation of wearable medical devices
• Machine-generated/sensor data, such as from monitoring vital signs
• Insurance claims, billing and cost data
• Patient/disease registries
• Research and development data
• Clinical trials data
• Health surveys
• Prescription data

In addition to leveraging health care analytics to improve clinical care, researchers are also focused on finding ways to deliver care more cost-effectively through data-driven analysis of financial systems, supply chains, fraud and human resources staffing.

One example of utilizing health care analytics to improve care while saving money comes from Minnesota, where Allina Health System realized more than $45 million in performance improvement savings over five years by using data to adjust its approach to cardiovascular care across its 13 hospitals and 82 clinics.

Going forward, it is projected that health care analytics will have a significant impact in many other important areas, including:

• Epidemiology: Health care analytics professionals are experimenting with data visualization to identify and more quickly control disease outbreaks.
• Clinical trials:  Health data analytics is expected to help researchers go to market faster with important new drugs.
• Genomics:  Advanced understanding of how diseases affect different people will enable medical researchers to develop personalized medicine based on individual DNA makeups.
• Social factors: Heightened ability to analyze data about the social determinants of health (such as where patients live, work and shop, what they eat, conditions related to their environment, etc.) opens new possibilities to better predict disease trends and to develop health and disease prevention programs.

The ongoing challenge for those working in health care analytics is to drill down to derive actionable insights from the ever-deepening sea of health care data.

So how does the vitally important field of health care analytics connect to the broader discipline of health informatics?

Noting that “often the two disciplines are erroneously seen as one and the same,” the American Health Information Management Association (AHIMA) asserts that “data analytics and informatics are both essential for the success of health care organizations,” and explains the differences between them as follows:

Health data analytics refers to analysis of the data using quantitative and qualitative techniques to be able to explore for trends and patterns in the data — to “acquire, manage, analyze, interpret and transform data into accurate, consistent and timely information.”

Health informatics refers to “a collaborative activity that involves people, processes and technologies” to use the information derived from data analytics to “improve the delivery of health care services and improve patient outcomes.”

In practice, health care analytics is being used to improve many areas of clinical care, including patient management, disease prevention and adherence to clinical protocols, to name a few. And while this might sound nearly identical to health informatics, an AHIMA article titled “Data Analytics and Informatics are Two Separate Disciplines,” explains that data analytics involves the actual analysis of the data, while informatics is the application of that information. In terms of what this means for the professionals who work in these two closely related disciplines, the AHIMA article further explains that:

Health informatics professionals apply their knowledge of information systems, databases and information technology to help design effective technology systems that gather, store, interpret and manage the data that is generated in the process of providing health care to patients.

Health data analysts find ways to capture and use the data that is acquired by health information technology systems, within the health system or from external sources, and then display it in meaningful ways through graphs, charts, etc. to help demonstrate how an organization can improve clinical care and decision-making.

AHIMA depicts the “Informatics vs. Analytics” relationship in this infographic.

The power of health care analytics is exemplified in real patient scenarios every day. Here are just some of the many ways it has recently been used in health care practices.

Streamlining PTSD diagnosis Post-traumatic stress disorder (PTSD) impacts nearly 8 million Americans, but reaching that diagnosis and beginning life-changing treatment can be a time-consuming process; and time, unfortunately, is not something many PTSD sufferers have to waste. Using machine learning, some physicians have been able to streamline the diagnosis process by eliminating some of the screening questions.

A team from the VA Boston Healthcare System and the Boston University School of Public Health built a machine learning model that learned how effectively different terms and questions in the diagnostic process accurately predicted PTSD diagnosis. This enabled the team to identify which items had weak associations and could be cut, while maintaining at least 90% accuracy.

Assisting with the COVID-19 pandemic fight Tufts Medical Center in Boston, Massachusetts, used artificial intelligence (AI) to streamline their COVID-19 testing program to speed up the results process and deliver better treatment and optimal patient outcomes. This leading hospital group used an AI platform to automate high-volume, labor-intensive data entry and patient screening tasks . In using AI to improve efficiency, Tufts estimates they will improve care delivery by making the in-person testing process up to 7x faster, saving 86% of patient testing time that’s inflated by manual patient data entry.

Improving X-ray/imaging screening Traditionally, a radiologist or other physician would have to read medical images to determine diagnosis. But today, AI and machine learning are able to scan and analyze X-rays and other imaging results and determine likely results in a fraction of the time.

Powered by hundreds of thousands of X-rays and other diagnostic results, machine learning programs are able to compare individual images to previous scans, thereby determining the most likely diagnosis. A study published in the U.S. National Library of Medicine highlighted many applications of this practice in today’s fight against cancer.

• Lung cancer is one of the most common and deadly tumors. Lung cancer screening can help identify pulmonary nodules, with early detection being lifesaving in many patients. Artificial intelligence can help in automatically identifying these nodules and categorizing them as benign or malignant.
• Expert interpretation of screening mammography is technically challenging. AI can assist in the interpretation, in part by identifying and characterizing microcalcifications (small deposits of calcium in the breast).

Combating the opioid epidemic The opioid epidemic has been an ongoing fight for many regions throughout the country, but today, health care analytics tools are supporting care providers in that battle. Analytics technologies are being used to reassess prescribing practices to determine the most effective treatments. Additionally, data-driven insights are helping providers apply effective health management strategies to specific patient scenarios.

For example, the Rhode Island Quality Institute developed a d ashboard tool that primary care physicians and opioid treatment centers could use to access and share vital information for patient care. Using this tool and its corresponding data providers recorded a 16% reduction in patients making return visits to the emergency department within 30 days.

Because the fields of health care analytics and health informatics are still relatively new, demand is high for qualified professionals who possess the necessary skills. This talent shortage has created a world of opportunity, particularly for those who have clinical/patient care experience or work in the related field of health information management (HIM).

In an article titled “ Data Scientist Shortage Creates Competitive Job Market for Analytics, Informatics ,” Journal of AHIMA Associate Editor Mary Butler wrote that HIM professionals who are “adding data analysis skills to their toolboxes are doing themselves a favor in terms of job security.” She described a graduate student earning an advanced degree in statistics weighing four competing job offers before choosing one with a six-figure salary at an analytics firm focused on health care companies and hospitals.

Health Care Analytics Jobs A LinkedIn search for jobs in health care analytics reveals thousands of results, including such job titles as:

• Analytics specialist
• Senior analyst
• Analytics manager
• Strategic consultant
• Associate director, research and analytics
• Senior health care analyst

This includes a variety of employers ranging from health care organizations and providers to technology companies, universities and government agencies.

Health Data Analyst Responsibilities One employment website that is seeking health data analysts lists some of the key job responsibilities as follows:

• Compiling and organizing health care data
• Analyzing data to assist in delivering optimal health care management and decision making
• Using health care data to achieve administrative needs and goals
• Understanding data storage and data sharing methods
• Investigating data to find patterns and trends
• Understanding health care business operations
• Utilizing different data sources for analyses
• Converting data into usable information that is easy to understand
• Developing reports and presentations
• Communicating analytic insights to stakeholders

The field of health information management is undergoing a transformation as advanced technology fuels the continued growth of health analytics and informatics.

“Health information management professionals are working at the nexus of the analytics and informatics fields,” said Stephanie Crabb, co-founder and principal at health care innovator Immersive, writing about an AHIMA Data Institute conference where analytics and informatics professionals compared notes about advancing in their careers after gaining invaluable experience in HIM roles.

Opportunities for HIM professionals to advance into health analytics and informatics roles are also emphasized in an AHIMA report titled “ HIM Reimagined .”

[RELATED] What is a Health Data Analyst? >>

Among other initiatives, AHIMA’s HIM Reimagined report discusses supporting educational opportunities that “align HIM professional skills with future workforce needs in areas like data analytics, informatics and information governance.”

For example, the Master’s in Health Care Informatics program at the University of San Diego integrates health care technology, clinical analytics, leadership and business knowledge and skills to prepare graduates for a range of careers in both health data analytics and health informatics. Offered online and on-campus with flexible class schedules for busy working professionals, the program gives students the option of choosing specialized coursework in Health Care Analytics.

Educational programs like this are invaluable in helping professionals develop and hone sought-after advanced data and analytics skills while instilling a deep understanding of the intricate complexities of health data. This unique combination of skills is what makes health analytics professionals so highly sought after.

Upon completing formal health informatics degree programs, many professionals complete certification programs that demonstrate their aptitude in certain specialties within the field. For example, the Certified Health Data Analyst (CHDA) designation provides employers some assurance of the professional competency of applicants who are competing for positions in health data analytics.

Q: What is health data analytics? A: Health data analytics refers to the analysis of the data using quantitative and qualitative techniques to be able to explore trends and patterns in the data — to “acquire, manage, analyze, interpret and transform data into accurate, consistent and timely information.”

Q: How can analytics help health care? A: Health data analytics is being used to improve many facets of clinical care by leveraging real patient data into improved diagnosis, treatments and research that ultimately improve patient care and outcomes. Just in the last few years, there have been countless examples of how health care analytics improves patient care , from supporting the battle with COVID-19 to streamlining PTSD treatment and more.

Q: How can data analytics drive health care interventions? A: Providers can now utilize data analytics to develop strategies for identifying the most high-risk and rising-risk patients who will require intervention. With an analytics system that is constantly monitoring medical records, claims data, prescription information and other data inputs for incidents that correlate with increased risk, such as multiple admissions or certain diagnoses, health care providers can be alerted to patients who require the most immediate care.

Q: What is predictive analytics in health care? A: Philips Health, a global health technology leader, characterized their goal for predictive analytics in health care as follows: “Predictive analytics aims to alert clinicians and caregivers of the likelihood of events and outcomes before they occur, helping them to prevent as much as cure health issues. Driven by the rise of Artificial Intelligence (AI) and the Internet of Things (IoT), we now have algorithms that can be fed with historical as well as real-time data to make meaningful predictions.”

Q: How can health leaders benefit from predictive analytics? A: Just as predictive analytics is helping clinicians prevent and better treat medical ailments and issues at the patient level, it can also help health care leaders take steps to improve treatments and outcomes among their patient populations. The same algorithms that help make predictions about individual treatments can be applied on a larger scale to communities and population health initiatives.

Q: What is population health analytics? A: Population health analytics is the act of applying quantitative methods and technology to gain new insights into population health with the goal of ultimately applying these insights to improve the health of large groups.

Q: What is the difference between health analytics and biostatistics? A: Health analytics is the analysis of relevant health care data to improve diagnosis, treatments and outcomes, while biostatistics are the statistical methods that should be used to guide the analysis of health data.

Q: What is the difference between health analytics and health informatics? A: While they are similar in focus, health care data analytics involves the process of analyzing the data while health informatics is the application of that information.

Q: What can you do with a master’s in health analytics? A: Because both data analytics and health care are career fields with high demand, people with a master’s degree in health analytics are well-positioned for career growth. Frequent job postings in health analytics include analytics specialist, senior analyst, analytics manager, strategic consultant, associate director, research and analytics, and senior health care analyst.

Q: What does a health care analyst do? A: While the exact job functions of a healthcare analyst will vary from organization to organization, the primary responsibilities generally include:

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Ph.D. in Analytics

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The core mission of the Mendoza PhD in Analytics is to develop thought-leaders in the analytics space that are engaged in impactful, cutting-edge scholarly research that considers the ethical dimension of data and its usage. Graduates of the PhD program are well-positioned to attain academic jobs at top business schools, where they can pursue successful careers in data analytics intensive domains such as business analytics, data science, information systems, operations, and computational social science, conducting research that is impactful and supports human flourishing.

Why Attain a PhD in Analytics?

The PhD degree is intended for those interested in the pursuit of knowledge – creating knowledge through research and disseminating new knowledge to students in the classroom. The field of analytics is without question one that is having a profound impact on business and society. There is a need for new professors capable of pursuing knowledge related to themes such as leadership in an AI-enabled world , ethical human-centered analytics , impactful computational social science , and next generation digital experimentation . These are just a few examples – we encourage our doctoral students to pursue whatever topics they’re passionate about and support them throughout their journey.

Why Notre Dame?

The Department of IT, Analytics, and Operations ( ITAO) is one of the premiere analytics departments, with world-class faculty, cutting-edge research labs, unparalleled industry connections, and access to a large network of Notre Dame alumni that are eager to support analytics thought-leadership.

Faculty Productivity and Reputation

The ITAO department encompasses a diverse set of faculty with significant research capabilities and extensive editorial board experience. ITAO faculty members currently serve in 10+ editorial roles at major journals related to analytics, information systems, and operations; and others have served in similar positions at quality journals previously. In recent years, ITAO faculty have won research awards at top journals and associations such as AIS, INFORMS, POM Society, and the IEEE.

Research Labs and Centers

The ITAO department has multiple analytics-focused research labs, including the Gaming Analytics Lab ( GAMA ) and the Human-centered Analytics Lab ( HAL ). Department faculty are also actively involved with the Notre Dame Technology Ethics Center ( ND-TEC ) and the Lucy Family Institute for Data and Society. Additionally, the Mendoza College of Business has a full-time dedicated data science team that supports data acquisition, collection, and wrangling as part of the Mendoza Behavioral Lab ( MBL ).

Partnerships with Industry

Our faculty routinely collaborate with various industry partners and federal agencies, including Electronic Arts, Ubisoft, eBay, Oracle, and NASA. The department is also actively involved with Notre Dame California ( ND California ), iNDustry Labs , and the Applied Analytics and Emerging Technology Lab (AeTL).

Cutting-edge Curriculum

It is essential that Ph.D. programs equip their graduates with the thorough, current training demanded by today’s market. Our analytics PhD program is well-positioned to produce “T-shaped” scholars that receive a foundation comprising select theories and ethics coursework, and depth via analytics methods courses and seminars. We see an opportunity to develop multi-dimensional scholars well-versed in contemporary analytics methods while also being adept at framing problems, thinking critically about the logic and flow between a problem and proposed solution, and capable of extrapolating their work to the bigger picture.

Institutional Prestige

Notre Dame is a Top 20 US News university with an international reputation and brand. A PhD from Notre Dame therefore sets our graduates up for success in academia at elite private schools or flagship state universities. Some of our graduates are also well-positioned for industry-oriented research roles.

The IT, Analytics, and Operations (ITAO) faculty use contemporary analytics methods such as machine learning, econometrics, statistics, and analytical modeling to study an array of research topics including ethics and privacy, health, sports and gaming, AI business applications, digital experimentation methods, and e-commerce:

• Ahmed Abbasi (AI, machine learning, text analytics, user modeling)
• Corey Angst (health analytics, ethics, privacy, security)
• Nicholas Berente (digital innovation, managing AI, institutional change)
• Francis Bilson Darku (sequential analysis, nonparametric statistics, econometrics)
• Jeff Cai (statistical learning, network analysis, data science)
• Sarv Devaraj (business analytics, healthcare management, supply chains)
• Rob Easley (economic modeling, Internet auctions, e-commerce)
• Ken Kelley (psychometrics, statistical methods, human-centered analytics)
• John Lalor (machine learning, natural language processing)
• Junghee Lee (innovation/technology in supply chains, healthcare operations)
• Kirsten Martin (technology ethics, privacy, business responsibility)
• Alfonso Pedraza-Martinez (humanitarian operations, disaster management, analytical modeling)
• Xinxue (Shawn) Qu (innovation diffusion, data management, predictive analytics)
• Sriram Somanchi (machine learning, event and pattern detection)
• Yoon Seock Son (econometrics, mobile strategy, AI business strategies)
• Daewon Sun (pricing strategies, resource management, economics of IS)
• Margaret Traeger (computational social science, social networks, health analytics)
• Katie Wowak (supply chains, traceability in global networks)
• Yang Yang (machine learning, network analysis, computational social science)
• Zifeng Zhao (statistical methods, large-scale forecasting, risk monitoring)

Program Structure

The program is designed to be five-years, full-time, in-residence. Click below for a year-by-year breakdown of how the program is structured.

In the first year, you will learn foundational theories, concepts, and methods related to analytics. ITAO seminar courses will include Human-centered AI, Philosophy of Science, and Computational Social Science. Methods related coursework will include classes related to machine learning, data science, statistics, and/or econometrics. Based on prior coursework, some students might be able to opt out of certain courses. In consultation with the program director, you will form a plan of study for methods courses and electives that align with your research interests.

At the beginning of the first year, you will also be assigned a faculty mentor that will guide your efforts related to the first-year research paper – the purpose of the first-year paper is to demonstrate the potential to produce high-quality scholarly manuscripts.

In year 2, you will continue to broaden and deepen your understanding of the analytics space with ITAO seminars related to Human-centered Statistics, Mathematical Modeling for Consumer Analytics, Operations and Prescriptive Analytics, and Data and Technology Ethics. At the end of the second year, you will have an examination requirement (in the form of an exam or paper). This examination will test your knowledge of ITAO seminar courses taken over the first two years. Your second-year faculty mentor will offer guidance on the paper.

You will wrap up any remaining coursework and turn your attention to pushing research projects towards publication.

In addition to managing your research portfolio, you’ll focus on finalizing your dissertation topic and defending your proposal.

The final year will involve interviewing for open positions, completing dissertation chapters, and having your final defense. And then, onward and upward into your exciting new career!

Marialena Bevilacqua received a BA in Math with a minor in Statistics from the College of Holy Cross in Massachusetts, where she was class president and captain of the field hockey and lacrosse teams. She attained an MS in Business Analytics from Georgetown University. Marialena was a brand operations analyst and manager plus “rookie of the year” at Thrasio.

Ryan Cook received a BS in Analytics with a minor in Philosophy from Notre Dame, and an MS in Computer Science from the University of Pennsylvania. He worked as a research scientist in Notre Dame’s Human-centered Analytics Lab and Center for Computer Assisted Synthesis, supporting projects related to NLP and network analysis. Ryan was also previously an analyst at EY in Chicago.

Kezia Oketch attained a BS in Computer Science from Spelman College and an MS in Engineering, Science, and Technology Entrepreneurship from Notre Dame. She was a Gold Scholar at the Grace Hopper Conference and co-founded a research startup focused on technology-based solutions to the cancer crisis in Kenya. Kezia was also a software engineer at an Ohio-based tech company.

Sunan Qian double majored in Economics and Math, and minored in French, at Mount Holyoke College. She received an MS in Finance with a minor in Quantitative Methods from Carnegie Mellon University – her thesis explored the impact of environmental regulation on firms’ carbon emissions and market value. Sunan was a digital consultant for Accenture in Tokyo.

Will Stamey was a double major in Economics and Math at Baylor University, with a minor in Philosophy. He was a Baylor Fellow and Crane Scholar, and completed the health economics sequence. Will’s honors thesis explored the impact of online education on academic outcomes. He was also a researcher at the Colorado Summer Institute in Biostatistics.

Xinyuan Zhang completed her undergraduate coursework from the University of Sydney, where she double majored in Finance and Statistics and researched sentiment analysis in the Computing Finance Lab. Xinyuan received an MS in Statistics from UCLA – her thesis explored preference models for two-sided platforms. She was also a researcher in the Trusted AI Systems Lab at Nankai University.

As Director of the Analytics PhD program, I’m happy to answer any questions you might have about our program (or a PhD in general). Feel free to email me at [email protected].  I look forward to connecting with you.

Our application deadline for the Fall 2024 incoming cohort is January 7, 2024 . You can apply using the “APPLY” button in the side menu (also appearing in the menu at the top of the page).

Ahmed Abbasi

Frequently Asked Questions

All students who are admitted to the program will be given a full tuition waiver. So the program is essentially tuition-free, with the only direct costs being miscellaneous university fees. In addition, all PhD students are paid a stipend of $40,000 a year. That stipend serves as compensation for your research activities (and for the teaching you would do in years three and four).

We require either the GRE or the GMAT, and have no preference between the two. If you’ve previously taken one of those tests, we require a score that is less than five years old. Unfortunately, the admissions committee will not waive the GRE/GMAT requirement under any circumstances.

It’s hard to say, as that is a function of a given application cycle, along with the rest of an applicant’s admissions portfolio. Most years, however, verbal and quantitative percentiles in the 80’s or above will be needed to advance to the short list.

Yes, if English is not your native language, or if English was not your language of college instruction. We accept either the TOEFL or the IELTS. If you’ve previously taken one of those tests, we require a score that is less than two years old.

You’ll fill out an online application form that will be linked on this site. And you’ll provide your resume, a statement of purpose/intent, three letters of recommendation, and unofficial transcripts of college (and any masters) degrees.

No. This sort of degree is best thought of as a research apprenticeship—where you are learning research skills in collaboration with faculty. That sort of collaboration requires a full-time, five-year, in-residence commitment.

Yes. While the program will prepare graduates to work in teaching institutions, government, and industry, the priority will be to prepare students for faculty roles so that they can be thought-leaders involved in teaching the next generation of analytics students and working to advance analytics-oriented research. Typically 80-90% of PhDs in Analytics take academic positions, while 10-20% pursue careers in industry (e.g., Silicon Valley, Wall Street, Think Tanks, etc.).

No. This is—first and foremost—a research degree. Teaching is part of the degree, as teaching is an important part of a professor’s career. But, if teaching or administration are your main focus, you might do a search for teaching-oriented PhD programs or Doctor of Business Administration (DBA) programs, which are sometimes also called Executive Doctorate programs.

Big Data Analytics (PhD)

Program at a glance.

• In State Tuition
• Out of State Tuition

Learn more about the cost to attend UCF.

Big Data Analytics will train researchers with a statistics background to analyze massive, structured or unstructured data to uncover hidden patterns, unknown correlations and other useful information that can be used to make better decisions.

The program will provide a strong foundation in the major methodologies associated with Big Data Analytics such as predictive analytics, data mining, text analytics and statistical analysis with an interdisciplinary component that combines the strength of statistics and computer science. It will focus on statistical computing, statistical data mining and their application to business, social, and health problems complemented with ongoing industrial collaborations. The scope of this program is specialized to prepare data scientists and data analysts who will work with very large data sets using both conventional and newly developed statistical methods.

The Ph.D. in Big Data Analytics requires 72 hours beyond an earned Bachelor's degree. Required coursework includes 42 credit hours of courses, 15 credit hours of restricted elective coursework, and 15 credit hours of dissertation research.

Total Credit Hours Required: 72 Credit Hours Minimum beyond the Bachelor's Degree

Program Tracks/Options

• Statistics Track

Application Deadlines

• International

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Program Prerequisites

Students must have the following background and courses completed before applying to the Big Data Analytics PhD program. These courses are: MAC 2311C: Calculus with Analytic Geometry I, MAC 2312: Calculus with Analytic Geometry II, MAC 2313: Calculus with Analytic Geometry III, MAS 3105: Matrix and Linear Algebra or MAS 3106: Linear Algebra , COP 3503C - Computer Science II. These pre-required courses are basic undergraduate courses from the Math and Computer Science departments. Students without background in COP 3503C can still apply for admission but they will need to take that course sometime after admission in the PhD program. COP 3503C serves as pre-requisite for COP 5711, which is required for the qualifying exam.

Degree Requirements

• All Ph.D. students must have an approved Plan of Study (POS) developed by the student and advisor that lists the specific courses to be taken as part of the degree. Students must maintain a minimum GPA of 3.0 in their POS, as well as a "B" (3.0) in all courses completed toward the degree and since admission to the program.

Required Courses

• STA5104 - Advanced Computer Processing of Statistical Data (3)
• STA5703 - Data Mining Methodology I (3)
• STA6106 - Statistical Computing I (3)
• STA6236 - Regression Analysis (3)
• STA6238 - Logistic Regression (3)
• STA6326 - Theoretical Statistics I (3)
• STA6327 - Theoretical Statistics II (3)
• STA6329 - Statistical Applications of Matrix Algebra (3)
• STA6704 - Data Mining Methodology II (3)
• STA7722 - Statistical Learning Theory (3)
• STA7734 - Statistical Asymptotic Theory in Big Data (3)
• STA6714 - Data Preparation (3)
• CNT5805 - Network Science (3)
• COP5711 - Parallel and Distributed Database Systems (3)

Restricted Electives (at least 9 credit hours must be STA coursework)

• Other courses may be included in a Plan of Study with departmental approval. Other electives can be used at the discretion of the student advisor and/or Graduate Coordinator.
• STA6107 - Statistical Computing II (3)
• STA6226 - Sampling Theory and Applications (3)
• STA6237 - Nonlinear Regression (3)
• STA6246 - Linear Models (3)
• STA6346 - Advanced Statistical Inference I (3)
• STA6347 - Advanced Statistical Inference II (3)
• STA6507 - Nonparametric Statistics (3)
• STA6662 - Statistical Methods for Industrial Practice (3)
• STA6705 - Data Mining Methodology III (3)
• STA6707 - Multivariate Statistical Methods (3)
• STA6709 - Spatial Statistics (3)
• STA6857 - Applied Time Series Analysis (3)
• STA7239 - Dimension Reduction in Regression (3)
• STA7719 - Survival Analysis (3)
• STA7935 - Current Topics in Big Data Analytics (3)
• CAP5610 - Machine Learning (3)
• CAP6307 - Text Mining I (3)
• CAP6315 - Social Media and Network Analysis (3)
• CAP6318 - Computational Analysis of Social Complexity (3)
• CAP6737 - Interactive Data Visualization (3)
• COP5537 - Network Optimization (3)
• COP6526 - Parallel and Cloud Computation (3)
• COP6616 - Multicore Programming (3)
• COT6417 - Algorithms on Strings and Sequences (3)
• COT6505 - Computational Methods/Analysis I (3)
• ECM6308 - Current Topics in Parallel Processing (3)
• EEL5825 - Machine Learning and Pattern Recognition (3)
• EEL6760 - Data Intensive Computing (3)
• FIL6146 - Screenplay Refinement (3)
• ESI6247 - Experimental Design and Taguchi Methods (3)
• ESI6358 - Decision Analysis (3)
• ESI6418 - Linear Programming and Extensions (3)
• ESI6609 - Industrial Engineering Analytics for Healthcare (3)
• ESI6891 - IEMS Research Methods (3)
• STA5825 - Stochastic Processes and Applied Probability Theory (3)
• STA7348 - Bayesian Modeling and Computation (3)
• COP6731 - Advanced Database Systems (3)

Dissertation

• Earn at least 15 credits from the following types of courses: STA 7980 - Dissertation Research The student must select a dissertation adviser by the end of the first year. In consultation with the dissertation adviser, the student should form a dissertation advisory committee. The dissertation adviser will be the chair of the student's dissertation advisory committee. In consultation with the dissertation advisor and with the approval of the chair of the department, each student must secure qualified members of their dissertation committee. This committee will consist of at least four faculty members chosen by the candidate, three of whom must be from the department and one from outside the department or UCF. Graduate faculty members must form the majority of any given committee. A dissertation committee must be formed prior to enrollment in dissertation hours. The dissertation serves as the culmination of the coursework that comprises this degree. It must make a significant original theoretical, intellectual, practical, creative or research contribution to the student's area within the discipline. The dissertation can be either research‐ or project‐based depending on the area of study, committee, and with the approval of the dissertation advisor. The dissertation will be completed through a minimum of 15 hours of dissertation research credit.

Examinations

• After passing candidacy, students will enroll into dissertation hours (STA7980) with their dissertation advisor. The dissertation can be either research‐ or project‐based depending on the area of study, committee, and with the approval of the dissertation advisor.

Qualifying Examination

• The qualifying examination is a written examination that will be administered by the doctoral exam committee at the start of the fall term (end of the summer) once a year. The courses required to prepare for the examination are STA 5703, STA 6704, CNT 5805, STA 6326, STA 6327 and COP 5711. Students must obtain permission from the Graduate Program Coordinator to take the examination. Students normally take this exam just before the start of their third year and are expected to have completed the exam by the start of their fourth year. To be eligible to take the Ph.D. qualifying examination, the student must have a minimum grade point average of 3.0 (out of 4.0) in all the coursework for the Ph.D. The exam may be taken twice. If a student does not pass the qualifying exam after the second try, he/she will be dismissed from the program. It is strongly recommended that the student select a dissertation adviser by the completion of 18 credit hours of course work, and it is strongly recommended that the student works with the dissertation adviser to form a dissertation committee within two semesters of passing the Qualifying Examination.

Candidacy Examination

• The candidacy exam is administered by the student's dissertation advisory committee and will be tailored to the student's individual program to propose either a research‐ or project‐based dissertation. The candidacy exam involves a dissertation proposal presented in an open forum, followed by an oral defense conducted by the student's advisory committee. This committee will give a Pass/No Pass grade. In addition to the dissertation proposal, the advisory committee may incorporate other requirements for the exam. The student can attempt candidacy any time after passing the qualifying examination, after the student has begun dissertation research (STA7919, if necessary), but prior to the end of the second year following the qualifying examination. The candidacy examination can be taken no more than two times. If a student does not pass the candidacy exam after the second try, he/she will be removed from the program

Admission to Candidacy

• The following are required to be admitted to candidacy and enroll in dissertation hours. Completion of all coursework, except for dissertation hours Successful completion of the qualifying examination Successful completion of the candidacy examination including a written proposal and oral defense The dissertation advisory committee is formed, consisting of approved graduate faculty and graduate faculty scholars Submittal of an approved program of study

Masters Along the Way

• PhD Students can obtain their Master's degree in Statistics & Data Science - Data Science Track along the way to their PhD degree. To satisfy the requirements for the MS degree, the student must complete the requirement for the MS degree. The student has the option of choosing between thesis option or non-thesis option.

Independent Learning

• As will all graduate programs, independent learning is an important component of the Big Data Analytics doctoral program. Students will demonstrate independent learning through research seminars and projects and the dissertation.

Grand Total Credits: 72

Application requirements, financial information.

Graduate students may receive financial assistance through fellowships, assistantships, tuition support, or loans. For more information, see the College of Graduate Studies Funding website, which describes the types of financial assistance available at UCF and provides general guidance in planning your graduate finances. The Financial Information section of the Graduate Catalog is another key resource.

Fellowship Information

Fellowships are awarded based on academic merit to highly qualified students. They are paid to students through the Office of Student Financial Assistance, based on instructions provided by the College of Graduate Studies. Fellowships are given to support a student's graduate study and do not have a work obligation. For more information, see UCF Graduate Fellowships, which includes descriptions of university fellowships and what you should do to be considered for a fellowship.

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• Visit the Alliance
• Visit HDR UK Futures

HDR UK-Turing Wellcome PhD Programme in Health Data Science

This truly outstanding and generously funded four-year programme at top UK universities provides you a pathway to join the UK’s leaders in health data research.

What this unique PhD programme offers you

Four-year programme: An initial foundation year allows students to gain real experience and insight into health data research.

Hosted by leading universities: Our host universities are among the very best in health data research.

Nurturing each student: Our programme aims to identify the particular abilities and interests of each student, and gear their PhD experience to effectively develop them.

Leadership Programme: Students benefit from a bespoke expert-led programme to develop the skills they need to understand, collaborate and influence others.

Generous funding: Students have their tuition fees (UK Home rate), college fees (where applicable), research expenses and travel costs paid and receive an enhanced, tax-free stipend with increases every year. (Y1 outside London: £23,955, Y1 in London: £25,954)

Building networks and experience: We actively support students in building networks and contacts in academia, the NHS and industry as well as taking internships and other opportunities to gain real-world experience. This includes a post-PhD bursary to support your next career step.

Team spirit: Strong relationships are built between our entire cohort of students through joint activities that build a genuine team spirit.

Personal support:  Each student has their own Director of Studies who is an additional point of contact during their time with us. All students are also further supported by the PhD team.

“The PhD programme has enabled me to gain first-hand experience in modern health data science approaches. It’s a truly unrivalled opportunity.”  Steven Wambua

Who is the PhD programme for?

We recruit enthusiastic, talented students who want to use data-driven research to develop and shape the UK’s response to the most complex health challenges of our times.

Applicants must have (or be on track to obtain):

• A first class or 2:1 undergraduate degree in statistics, mathematics, computer / data science, physics or an allied subject  or
• Another undergraduate degree subject and outcome but can demonstrate their suitability for this programme through additional qualifications or research experience.

Active or currently registered health care professionals   are not eligible and should consider the Wellcome PhD Fellowships for Health Professionals .

Applicants also need to meet the following criteria:

• Successful admission to the specified degree programme at one of our partner universities. Students will be expected to meet the admissions requirements of that department and university but do not need to hold the offer at the point of application.
• Two satisfactory academic or relevant references.
• Proof of a legal right to study in the UK or ability to satisfy the current requirements of UK Visa and Immigration.

Training is in-person, hybrid and virtual throughout the first year.

We are committed to a diverse and inclusive research culture . We welcome those who are returning from the workplace, international candidates and everyone underrepresented in STEM and academia. For further details see our FAQs .

We cannot accept applicants who are looking for a part-time PhD or those who are aiming to study whilst continuing to be employed elsewhere.

We aim to accommodate specific needs and personal circumstances. Please make us aware of individual circumstances when applying or contact us directly at  [email protected] . Please note our  applicant privacy notice .

If you have questions or require adjustments to the application process, please contact us below via email or telephone (+44 (0)770 847 8846).

There are no nationality restrictions and international students are able to apply. However, applicants are advised the award only covers fees at the UK/Home level. International students will be required to secure an additional scholarship from our partner universities (after receiving a offer from us at interview) to cover the difference between Home and Overseas fees. This will limit the university choices available:

(Please be aware that these are usually highly competitive and will need to be applied for separately in your application to the university post-offer. A successful application to the PhD programme does not guarantee a fee waiver or scholarship. We do not accept applications from candidates who are self-funding.)

These are only initial programmes of study for Year 1. Students may transfer to a new university programme from Year 2 after research projects have been confirmed.

Is this the PhD future for you?

Watch our Applicant Open Day hosted by our current students to find out more about the programme and whether it’s for you.

Applications are currently: Closed

There are up to five studentships are available for 2024 entry, based at our partner universities. Applications are currently being evaluated through the Turing Flexigrant system.

The application process

Details required:

• Contact details
• Details and transcripts of university qualification(s)
• Any relevant job history
• Answers to personal-statement type questions
• Contact details for two referees
• Applicants can indicate preferences for universities, and provide relevant information for their choice such as caring responsibilities or personal circumstances
• There is no need to apply to universities, submit a research proposal, provide IELTS scores or contact supervisors at this stage

Submitted applications will first be checked for eligibility and then will undergo a first stage review. This will involve triage by the 2024 recruitment panel and then reviewed by academics and PhD students from our partner universities in December 2023 . Successful applicants will be invited to an interview in January 2024 .

After receiving an offer, applications will be invited to relevant partner universities.

Selection criteria

Applicants should demonstrate that they meet the following criteria:

* These criteria will be assessed at interview via a pre-interview exercise.

HDR UK reserves the right to reject applicants who do not meet the criteria at any stage. Regretfully, we can only provide feedback for candidates who reach interview.

Programme Structure

The four-year programme is divided in two. There is an initial Foundation Year followed by a three-year research project. The first year combines the best in university-based training with HDR UK-led national activities. And we support students to produce game-changing research plans and their projects are backed by substantial research funding.

Foundation year

3-5 day immersion events allow students to gain insight into the work of HDR UK, and our academic, clinical and industry partners. Courses may be residential (expenses provided) with up to a week away from their home university or online. Students undertake an intensive deep dive into an important area of health data science. Immersion topics include risk prediction, oncology, clinical trials, epidemiology and bioinformatics. Past immersion weeks have been hosted by the Universities of Birmingham, Manchester, Oxford and University College London and the European Bioinformatics Institute.

The immersion events encourage students to work together and stimulate new interactions:

• Axes of Prognosis
• The Different Facets of Data

Research areas

PhD research projects can be linked to The Institute’s:

• Research priorities
• Research hubs
• Partnerships

Team working

Students operate as a national cohort and work collaboratively with others, overcoming traditional institutional silos. Students are registered with a  partner university  but can draw on academic expertise from across the HDR UK network and are supported to formulate research activities that bring together experts from across the UK.

• You can contact us at [email protected]   or phone (+44 (0)770 847 8846). 
• For details of how we process applicants’ data see PhD Applicant Privacy Notice .

Students have access to graduate-level courses and research project rotation in their university to introduce them to different areas of health data science and enable them to develop a bespoke research project under the guidance of our expert university leads.

Regular workshops and short courses introduce students to the work of HDR UK experts across our hubs, themes and priority areas and to external organisations. Past contributors have included NHSX, IQVIA and AstraZeneca.

Immersion and workshop events allow students to better understand the wider health and social care landscape and accelerate their potential to become sector leaders. They also enable students to develop more ambitious PhD research projects by stimulating collaboration with external academics, industry-based organisations, or by using national data infrastructure.

Training is provided by academic, industry and NHS experts to promote personal and professional development in leadership capability, cross-sector collaborative skills and inter-disciplinary working. In particular, HDR UK is committed to working with public and patients to build increased trust in health data research as well as designing solutions focused on improving patient outcomes and experience. Students will develop communication and collaborative skills to help put them at the forefront of this mission.

At the end of the Foundation Year students design a bespoke three-year research project and a multi-disciplinary supervision team based on their training experiences.

Research proposals will be rigorously reviewed by expert academics and public-patient representatives to ensure they are of the highest standards in terms of ambition, scientific methodology and impact on patient outcomes.

The research will be carried out at their home university and could be linked to HDR UK  research priorities ,  research hubs  or  partnerships .

This includes short immersions plus  longer practical real-world projects with businesses and other organisations at the cutting edge of everything from medical devices, to life sciences, to vaccines. Students also learn about leadership theory and attend specially-convened seminars from senior figures in relevant areas of healthcare.

Networks and experience: Students will be actively supported in building networks and contacts in academia, the NHS and industry as well as taking internships and other opportunities to gain real-world experience.

Team working: Students operate as a national cohort, building strong relationships through joint activities and overcoming traditional institutional silos.

Workshops: Regular workshops and short courses introduce the work of HDR UK experts and to external organisations.

Immersion events: These allow students to better understand the wider health and social care landscape and accelerate students’ potential to become a sector leader. They also enable them to develop an ambitious PhD research project.

Researcher development: Training is provided by academic, industry and NHS experts to promote personal and professional development in cross-sector collaborative skills, communication and inter-disciplinary working.

“Our Leadership Programme will give PhD students the chance to develop the practical skills they need to bring people together to use health data science to deliver much-needed innovations and advances in health and care,”  Professor Peter Bannister

Our partners

Programme partners include NHS Digital, AstraZeneca, Moorfields Eye Hospital NHS Foundation Trust, and University Hospitals Birmingham.

More broadly it will work with winners of the NHSX AI Innovation Award , which funds and supports promising artificial intelligence technologies in health and care. There will also be opportunities with businesses on the DTI listed top 100 digital health innovators which are using big data for healthcare innovation.

Master’s Degree Scholarships

We offer 10 annual Master’s degree scholarships worth £10,000 for students with an interest in dementia or diabetes research.

Undergraduate Summer Internship in Health Data Research

Apply for a summer work placement in health data research at a UK research organisation, with an HDR UK-Wellcome Biomedical Vacation Scholarship

Join the HDR UK Alumni Network

HDR UK’s online Alumni Network brings together the amazing people who have been part of our training and education programmes.

Our host universities

- - - - Meet our PhD students

Our PhD students come from a wide range of backgrounds - discover who they are and what their experiences have been as part of the PhD programme

Meet the PhD Programme team

Our wider team consists of leading experts in disciplines including theoretical physics, computer science, mathematics and statistics, applied mathematics and biochemistry.

• Miguel Bernabeu – University of Edinburgh
• Ioanna Manolopoulou – University College London
• Niels Peek – University of Manchester
• Iain Styles – Queen’s University Belfast
• Paul Taylor – University College London
• Catalina Vallejos – University of Edinburgh
• Angela Wood – University of Cambridge
• David Wong – University of Manchester
• Tom Nichols – University of Oxford
• Magnus Rattray – University of Manchester

Collaboration on AI-powered Patient Safety Research Flags Drug Side Effects

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A recent capstone project by an interdisciplinary team of graduate students from  Heinz College of Information Systems and Public Policy (opens in new window) at Carnegie Mellon University and the University of Pittsburgh School of Medicine used artificial intelligence to examine health care data and look for patterns to detect potentially dangerous drug interactions.

Four CMU master’s degree students used AI and machine learning to review databases housed at the University of Pittsburgh to look for signals of adverse drug events, adverse drug reactions or medication errors in millions of records from more than 600,000 patients from across three years of routinely collected patient care data.

“We learned all these applied skills in class — data cleaning, data analysis — but then in real-world applications, you start to see all these obstacles that you have to know how to get around,” said Quentin Auster, a student in the Heinz College’s  Master of Public Policy and Management: Data Analytics (opens in new window) program. He led the capstone project team that also included Joanna Sam and (now-graduate) Alex Liu of the  Master of Information Systems Program in Business Intelligence & Data Analytics (opens in new window) ; and Yue (Zoey) Sun, a student in the  Master of Health Care Analytics & Information Technology (opens in new window) Program.

The research team received access to the data through funding from the Pittsburgh  Regional Autonomous Patient Safety (opens in new window) (RAPS) initiative of the Jewish Healthcare Foundation that established Carnegie Mellon’s  Initiative for Patient Safety Research (opens in new window) (IPSR) in 2022 with a two-year $500,000 grant.

Detecting Adverse Events

The students mined association rules in the medication data using a method often applied to e-commerce data. Instead of suggesting similar products for customers to buy together, in this case they generated the rules from the data to guide the discovery of different medication combinations that may lead to adverse outcomes, said  Rema Padman (opens in new window) , Trustees Professor of Management Science and Healthcare Informatics in the Heinz College and faculty advisor of the capstone project. The team also collaborated with  Ari Lightman (opens in new window) , digital media and marketing professor in the Heinz College, and  Alan Scheller-Wolf (opens in new window) , Richard M. Cyert Professor of Operations Management in the  Tepper School of Business (opens in new window) , both faculty leads at IPSR who facilitated access to the data at the University of Pittsburgh.

Once these combinations are identified, researchers can determine those with the highest frequency within the hundreds of thousands of records in the dataset. Clinicians can use the information to verify the risk of an adverse reaction and identify alternative medications. Detecting and reviewing the importance of the anomalies in the data would be difficult without AI and machine learning, Padman said.

“These methods can really help to sift through the vast amounts of data, since every patient might be taking multiple medications, and with thousands of patients, there are many different combinations to examine,” she said. “We can apply these methods to extract some useful information.”

Uncovering Patterns Within The Data

The team from Carnegie Mellon collaborated with Richard D. Boyce, Associate Professor of Biomedical Informatics at the University of Pittsburgh, and his research team to obtain secure access to the data, including Pitt’s  Medication Error Avoidance at Regional Scale (opens in new window) (MEARs) database and the U.S. Food and Drug Administration’s  Adverse Event Reporting System database (opens in new window) (FAERS), and specialized domain knowledge about MEARs and medication-related errors.

The capstone team conducted research through an open-source approach in a highly secure virtual workbench using a combination of data science tools that included a user-friendly research web application for large-scale analytics developed by the Observational Health Data Sciences and Informatics (or OHDSI, “Odyssey”) collaborative. As the students progressed, they gained an understanding of patient journeys through visualization and analysis.

The team demonstrated their approach by narrowing their focus to patients taking colchicine, typically used to treat gout but increasingly prescribed to  prevent coronary artery disease (opens in new window) . Then, they looked for the antibiotic clarithromycin and medications like it that influence how the body breaks down colchicine.

Partners in Health Care Data

Carnegie Mellon University participates in the  Pittsburgh Health Data Alliance (opens in new window) , which unites the world-class computer science, artificial intelligence and medical research from CMU and University of Pittsburgh with clinical expertise and data from UPMC to facilitate innovation in digital health. The effort includes the  Center for Machine Learning and Health (opens in new window) and projects focus on health care outcomes, consumer-oriented health care, and health care infrastructure and efficiencies.

Capstones, Explained

Experiential learning is more than a buzz term. Students at the Heinz College engage in hands-on projects with industry partners to solve real-world problems.

As the culmination of their learning, a team of students applies the skills they’ve learned. Over 15 weeks, students will work with their client and a faculty mentor to create an innovative and customized solution to a challenge the partner has identified. Check out examples of  recent capstone projects (opens in new window) .

“If you are a doctor practicing every day, you should know this already, so you shouldn’t have prescribed this combination together,” Sun said. “We wouldn’t expect to see a lot of instances in the EHR (electronic health record) system, which added to the difficulty for us to research and study trying to find this combination.”

In applying association rule-mining to the EHR data, the team noticed the frequent pairing of colchicine with metoprolol, used to treat high blood pressure but potentially exacerbate a patient’s gout.

Their findings show that nuanced clinical judgment is necessary in the interpretation of data-derived medication patterns, said Sun, who also holds a doctorate in pharmacy, knowledge that was invaluable in helping the team decipher information on pharmaceuticals.

In future work, Padman said the approach used in this project can be evaluated using other known combinations of medications that result in adverse events or reactions, then generalized to detect new combinations that can be verified by domain experts.

“There’s really not a flag that’s specific to say ‘an adverse drug event happened here,’ so it’s a bit like if you lose your keys,” Auster said. “You're going to look around the streetlamp where you might have lost them. The streetlamp in this case was colchicine, which has actual signals of a place where we would expect adverse events to happen.”

Determining Future Solutions

Through the IPSR, Padman is also advising other Ph.D. students on similar patient safety-related research, all part of the Carnegie Mellon’s  Center for Innovation in Health (opens in new window) , led by  Carl Kingsford (opens in new window) , Herbert A. Simon Professor of Computer Science in the Computational Biology Department of the  School of Computer Science (opens in new window) .

“AI does not start at the beginning of the deep network,” Kingsford said. “You can’t train AI without data. You can’t do anything without setting up a problem. All that stuff is super-crucial, especially in the health care domain — structured data, unstructured data, weird specialized terms — that all have to be put into a model that can be used to train and apply AI.”

About 250,000 to 400,000 people die annually from preventable medical errors, said Karen Feinstein, CEO of the  Jewish Healthcare Foundation (opens in new window) . For now, because of the fragmented nature of the health care industry, she said investment in patient safety is difficult to incentivize, but research like this project can help interest in it gain momentum.

“Health care systems — and the data that evaluate their performance — are complex and confusing,” she said. “Employers and patients lack knowledge of the serious safety deficits that put them at risk and lack avenues to express their concerns about safety. In addition, the convoluted payment systems for health care do not reward exceptional performance in quality or safety and regulation has, so far, proved ineffective. This has put health care far behind other industries in their product and services safety. It does, however, leave the door wide open for entrepreneurs, and CMU is equipping students with the skills and insight to help drive the revolution of tech solutions for patient safety.”

@CarnegieMellon and @PittTweet are more than just neighbors in Oakland. This article from @PittMedMag explores how the two schools are collaborating to train the next generation of #BiomedicalLeaders . https://t.co/szKqEPAAYm — Computational & Systems Biology at Pitt (@compbiopitt) February 23, 2024

— Related Content —

Revolutionizing Health Care: Harnessing Artificial Intelligence for Better Patient Care

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AI Major Wins Automated Medical Diagnosis Challenge

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Smart City Data Sensing during COVID-19: Public Reaction to Accelerating Digital Transformation

Alexander a. kharlamov.

1 Institute of Higher Nervous Activity and Neurophysiology, Russian Academy of Sciences, 117865 Moscow, Russia; ur.tsylana@vomalrahk

Aleksei N. Raskhodchikov

2 Moscow Centre of Urban Studies, 115280 Moscow, Russia; ur.liam@awolsalis

Maria Pilgun

3 Institute of Linguistics, Russian Academy of Sciences, 125009 Moscow, Russia

Associated Data

Publicly available datasets were analyzed in this study.

The article presents the results of the analysis of the adaptation of metropolis IT technologies to solve operational problems in extreme conditions during the COVID-19 pandemic. The material for the study was Russian-language data from social networks, microblogging, blogs, instant messengers, forums, reviews, video hosting services, thematic portals, online media, print media and TV related to the first wave of the COVID-19 pandemic in Russia. The data were collected between 1 March 2020 and 1 June 2020. The database size includes 85,493,717 characters. To analyze the content of social media, a multimodal approach was used involving neural network technologies, text analysis, sentiment-analysis and analysis of lexical associations. The transformation of old digital services and applications, as well as the emergence of new ones were analyzed in terms of the perception of digital communications by actors.

1. Introduction

The digital services, measurement and data management technologies of smart cities were stimulated to develop rapidly during the COVID-19 pandemic,. The public response to technological innovations and the introduction of new regulations was mixed. Some innovations were implemented quite successfully and were popular with the urban residents. Others have prompted various forms of resistance, from social media criticism to civil disobedience. This article is devoted to the analysis of the reaction of Moscow residents to accelerating digital transformation, which the Moscow authorities were forced to carry out during the onset of the COVID-19 pandemic.

There is already a sufficient amount of scientific research revealing various aspects of the use of IT technologies to combat the spread of COVID-19, accelerate digital transformation, as well as a study that analyzed the role of old and new media during the coronavirus pandemic, reactions on social networks and the transformation of communication processes, etc.

For example, researchers are actively developing technological solutions for detecting coronavirus, monitoring symptoms [ 1 , 2 , 3 , 4 ], rehabilitation [ 5 ], limiting the spread of the pandemic [ 6 ].

It should be noted that the “digital turn” radically changed politics, transformed the traditional modernist binary systems in state-society, public-private, consumption-production, work-leisure, culture-nature and man-posthuman terms. The growing influence of digital technologies on society has formed two opposite positions. Digital optimists focus on the positive aspects of digital transformations that provide new opportunities for the formation of diverse forms of communities, alternative ways of learning and perception, creative innovations, a culture of participation, networking and cloud democracy (“e-democracy”). Digital pessimists point to the negative aspects associated with the expansion of dominance through new forms of control, a surveillance society, network authoritarianism, digital dehumanization, alienation 2.0 and network exploitation. Socially disadvantaged people living on the “outskirts” of the digital society are oppressed both in terms of access to the benefits of use (there are three levels of the digital divide) and in terms of understanding and handling new digital technologies (three levels of separation) [ 7 ].

Research has shown that the majority of residents highly appreciated the benefits of using digital technologies in the field of transport and services provision; however, decision-making in the field of healthcare and justice involving Artificial Intelligence (AI) technologies causes a negative attitude. Residents’ greatest concerns are related to ethical issues, lack of transparency and the potential AI impact on employment. It is appropriate to recall that, according to the research by Carrasco, Mills, Whybrew and Jura, the level of confidence in the government was the decisive factor in the readiness of residents to master artificial intelligence technologies [ 8 ].

The growth of technology has led to the emergence of the concepts of “digital twin” and “digital identity” [ 9 , 10 ]. Digital twin technologies have proven to be extremely in demand in the formation and development of the smart city concept, since they provided ample opportunities for ensuring the safety and sustainable development of smart cities.

Researchers have proven that digital twin technologies used to monitor, visualize, diagnose and predict in real time are vital to the sustainability and efficiency of urban systems and infrastructure elements that interact with each other. Moreover, one of the key factors ensuring the security of digital twins is the Internet of things. The negative side of the development of digital technologies, as is recognized, are the risks associated with the loss of privacy of a person’s identity, since digital footprints provide unprecedentedly detailed information about private life. The privacy of a person is directly dependent on the effectiveness of his personal data protection [ 11 ].

Thus, with the development of “smart” cities and technologies that use various types of data, ethical problems are exacerbated that are associated with privacy, the possibilities of the Internet of things, “smart” infrastructure, digital government, etc.

Face recognition technologies are the most in demand in the “smart” city system. Mass video surveillance is designed to provide security and reduce the crime rate, but it does not correlate well with privacy and significantly expands the possibilities of control and surveillance. The Internet of things, on the one hand, makes it possible based on data to increase the efficiency of management decisions fight against crime and to increase the comfort of residents by creating new services; on the other hand, the data collected by Internet of things technologies is by no means limited to information about the state of urban infrastructure, but also contain personal data, as well as personal information of residents. As a result, fundamentally new risks for the safety of the city are formed; the urban infrastructure depends heavily on the manufacturers of technologies, software and components; there is a risk of data leakage.

The rapid development of artificial intelligence technologies in the near future will significantly expand the boundaries of digital technologies and will pose a number of new problems in terms of contradictions between the public good and the boundaries of a person’s private life in a “smart” city. Thus, decision support systems using artificial intelligence technologies can improve the efficiency of the decision support system and predictive analytics, organize preventive targeted social assistance, ensure a quick reaction of city authorities to changes, but increase the risks of discrimination due to the bias of algorithms and lead to an increase in digital inequality [ 12 , 13 , 14 , 15 ].

In the world, already in the pre-COVID-19 era, there were precedents of confrontation between city residents and technology campaigns. Thus, residents of the city of Toronto (Canada) refused the opportunity to turn their city into a “smart” one with the participation of the Alphabet Corporation because of the negative attitude of the residents towards data-corporations that commit gross violations in the collection and use of people’s personal data [ 16 ].

In the Netherlands, the contradictory nature of the processes accompanying the development of a “smart” city has led to the formation of the basic principles of urban planning that define the boundaries of the technology invasion into the residents’ lives [ 17 ].

Analysis of the development of epidemics in past years has shown that an increase in social tension is caused by communicative errors, which are made in the course of an information campaign on the prevention and treatment of the population [ 18 , 19 , 20 ]. Moreover, the main role in the conflict escalation and the growth of negative sentiments, is played by the media. The health care crisis causes panic and forms the collective anger in society that falls upon the authorities [ 21 ].

The results of previous studies were also confirmed by the practice of the COVID-19 pandemic, which showed that prevention and protection against infection in communities play a decisive role in containing and controlling the spread of infection [ 22 ].

Anti-crisis communications during the COVID-19 pandemic have already been described in detail in the specialized literature [ 23 , 24 ]. In particular, the study [ 25 ] analyzed the role of old and new media during the COVID-19 pandemic, reactions in social media, information support of quarantine measures, the phenomenon of infodemic and the transformation of communication processes.

The features of organizational communications during the pandemic [ 26 ], the transformation of the event management industry [ 27 ], examples of successful and unsuccessful leadership and guidelines for responding to COVID-19 [ 28 ] have also received attention.

The public reaction, which is expressed in the digital footprints of users, is usually analyzed using sentiment analysis. Modern sentiment analysis of the text includes at least three types of tasks: (1) classification of tonal messages (positive/negative or finer gradation); (2) determination of sentiment regarding a given sentiment object (often followed by visual marking of the sentence dependency tree; (3) determination of sentiment of a sentiment object with respect to its implicit and explicit attributes (feature-based) [ 29 , 30 , 31 ].

Sentiment analysis on the impact of coronavirus in social life using the BERT model is presented in a detailed study. [ 32 ]. However, it should be noted that the analysis of public sentiment based on digital data during the period of the pandemic was carried out mainly on the basis of tweets [ 33 , 34 , 35 , 36 ]. For Russian-speaking users, the analysis of Twitter content is not indicative, since this resource is not popular with users due to the morphological and syntactic features of the Russian language. In the Russian-speaking media space, Twitter is used primarily by PR experts and spin doctors in political communications. A technology for monitoring the mental health of citizens during the COVID-19 pandemic using sentiment analysis based on the material of the Korean language was proposed in the study [ 37 ]. Meanwhile, highlighting four emotional labels (anger, sadness, neutral, and happiness) and three possible interactive responses for each emotion (reciting wise sayings, playing music, and sympathizing: reciting wise sayings, playing music, and sympathizing) that gives good results for the Korean language users is not relevant for the analysis of the content of Russian-speaking users.

The novelty of this study lies in the fact that, despite the existing research, the specificity of the data analysis and management of the smart city during COVID-19 in terms of public reaction to the acceleration of digital transformation and the perception of digital innovations by residents has not yet received coverage in the specialized literature.

The aim of the study is to analyze actors’ perceptions of the accelerations of digital transformation, adaptation of IT technologies of the metropolis to solve operational problems in the extreme conditions of the first wave of the COVID-19 pandemic, as well as study society’s reaction to technical transformations, the specifics of analysis and management of urban infrastructure data.

The significance of the proposed work in the current situation is due to the fact that the spread of COVID-19 continues and acceleration of the digital transformation will also have to continue. Meanwhile, the success of digital transformation largely depends on the reaction of society. The algorithm proposed by the authors of this article can be used to analyze the reaction of society to different types of transformations, for predictive analytics of conflicts, to increase the level of trust and the effectiveness of dialogue between society and the government.

2. Materials and Methods

The material for the study was data from social networks, microblogging, blogs, instant messengers, forums, reviews, video hosting services, thematic portals, online media, print media and TV related to the first wave of the COVID-19 pandemic in Russia.

Data collection was carried out using monitoring by message texts, recognized texts in pictures, video transcripts, check-ins, and stories. When forming the empirical database, various types of digital sources were used: social networks, blogs, forums, reviews, marketplaces, map services, stores of mobile applications; Telegram public channels and chats; online media; websites of government agencies, market-forming companies and organizations.

• Data collection period: 1 March 2020 to 1 June 2020.
• Database volume: 11,120,287 words and 85,493,717 characters.
• Number of messages: 161,541.
• Number of active actors: 47,574.
• Number of sources: 1325.

2.2. Method

To analyze the content of social media, a multimodal approach was used involving neural network technologies, text analysis, sentiment-analysis, analysis of lexical associations [ 38 ] and content analysis [ 39 , 40 ]

The study involved a model using neural-like elements with temporal summation of signals or corticomorphic associative memory, which made it possible to single out explicit knowledge, topics that aroused the greatest interest of actors, to study the topic structure of content and to summarize data. In addition, the neural network representation of the text made it possible to form and interpret the semantic network in the form of a set of interrelated concepts. With the help of the semantic network, implicatures and semantic accents, which are most important for the actors, were analyzed and then rated. The analysis of associative networks of relevant stimuli made it possible to draw conclusions about the perception.

2.3. Procedures

• Content selection and cleaning (filtering).

Data processing was carried out using the method of random Markov fields and its modification—the method of Conditional Random Fields (CRF). The CRF method, like the Maximum Entropy Markov Models (MEMM) method, refers to discriminative probabilistic methods, in contrast to generative methods such as Hidden Markov Models (HMM) [ 41 ] or the naive Bayes method [ 42 ]. By analogy with MEMM [ 43 ], the choice of factor-signs for setting the probability of transition between states in the presence of an observed value of xt depends on the specifics of specific data, but unlike the same MEMM, CRF can take into account any peculiarities and interdependencies in the initial data. The feature vector L = {λk} is calculated based on the training sample and determines the weight of each potential function. For training and application of the model, algorithms similar to those of HMM are used: Viterbi and its variant—the “forward–backward” algorithm [ 43 , 44 ]. It is believed that the CRF method is the most popular and accurate way of extracting objects from text and can be a significant competitor to other statistical methods used in linguistic text processing [ 45 ]. For example, it was implemented in the Stanford Named Entity Recognizer project [ 46 ].

• 1.1. Isolation and extraction of artificial entities (bots were carried out using a model using neural-like elements with temporal summation of signals.
• 1.2. Content clustering was performed using dynamic network metrics, trail metrics, procedures for grouping nodes, identifying local patterns, comparing and contrasting networks, groups, and individuals from a dynamic meta-network perspective
• 2. Performing sentiment analysis.

In this study, sentiment analysis was performed using the Eureka Engine sentiment determination module. The technique is based on a statistical algorithm for conditional random CRF fields using sentiment dictionaries. Sequences of lexems are used as input data, after which the algorithm calculates the probabilities of possible sequences of tags and chooses the maximum probable one.

• 3. Performing Content Analysis.

Content analysis was performed in accordance with [ 39 , 40 ] using the AutoMap text mining tool.

• 4. Identifying key topics.

The procedures listed in paragraphs 4–6 were carried out avail of a model using neural-like elements with temporal summation of signals or corticomorphic associative memory, which made it possible to single out explicit information [ 38 ].

• 4.1. Selection and analysis of the topic structure.
• 4.2. Summarization.
• 5.1. Extraction of the semantic core (nominations with link weights of 98–100).
• 5.2. Textual analysis of the semantic core.
• 6.1. Performing an associative search.
• 6.2. Word associations

To collect data, the Brand Analytics ( https://br-analytics.ru/ ) (accessed on 30 January 2020) and Sketch Engine ( https://www.sketchengine.eu/ ) systems were used.

The verbal content was analyzed using the neural network technology TextAnalyst 2.3. ( http://www.analyst.ru/index.php?lang=eng&dir=content/products/&id=ta ) (accessed on 30 January 2020) developed by one of the article authors, A.A. Kharlamov.

ORA-LITE was used for network analysis, which is a dynamic meta-network assessment and analysis tool specifically developed ( http://www.casos.cs.cmu.edu/projects/ora/ ) (accessed on 30 January 2020)

Content analysis was performed using the AutoMap text mining tool ( http://www.casos.cs.cmu.edu/projects/automap/ ) (accessed on 30 January 2020).

For visual analytics, the Tableau platform was used ( https://www.tableau.com/ ) (accessed on 30 January 2020).

3.1. General Description of the Content

The period of the pandemic onset in Russia was selected for the study. The database was divided by type of actors into two groups by geolocation. Since the spread of the coronavirus infection in Russia began from the capital, the content was divided into two groups to ensure correct analysis: Moscow actors and actors from the Moscow region, other regions of Russia, as well as Russian-speaking actors from other countries, which were conditionally designated as regional.

Both groups of actors differ in their preferences for the digital platforms they have chosen to generate content on the coronavirus infection spread. The group of regional actors is more diverse, they used mostly messengers; the Moscow community preferred microblogging ( Figure 1 and Figure 2 ).

Digital platforms of regional actors.

Digital platforms of Moscow actors.

Analysis of the data shows a difference in the development and perception of the coronavirus topic among Moscow and regional actors. Moscow actors began discussing COVID-19 earlier and with greater intensity. The activity index of Moscow actors expressed in the number of posts per author (11.69) is 3.5-times higher than the regional index (3.33).

3.2. Content Sentiment Analysis

The content of Moscow actors is also characterized by a higher degree of negative reactions. It should be noted that the neutral cluster predominates in the database for both groups, while in the Moscow content the negative cluster is more extensive, and the positive one is nearly non-existent ( Figure 3 and Figure 4 ).

Sentiment of the regional actors’ content.

Sentiment of the Moscow actors’ content.

The sentiment features of digital footprints also indicate a greater intensity of negative emotions in the Moscow group: 29.03% of negative comments; and in the regional group only 4.39% of comments are negative ( Figure 5 and Figure 6 ).

Sentiment of digital footprints of the regional actors’ content.

Sentiment of digital footprints of the Moscow actors’ content.

3.3. Key Topics of the Content

Identification and analysis of the topic structure, summarization and content analysis made it possible to determine that the key topics of the regional content related to the pandemic in this period were as follows:

• Emergence of coronavirus infection in Moscow;
• Coronavirus spread in the Moscow region;
• Pandemic spread throughout Russia;
• Discussion of the specifics of the new disease ( Appendix A ).

In the Moscow content, the topics were more diverse:

• Discussion of the measures taken to fight the infection spread;
• Health problems that have worsened since the beginning of the quarantine;
• Criticism of the actions of the authorities regarding the fight against the coronavirus infection;
• Political issues;
• Discussion of constitutional amendments ( Appendix B ).

3.4. Core of the Semantic Network

The semantic network makes it possible to identify semantic accents that are the most significant for actors, to analyze the implicit knowledge hidden behind explicitly expressed speech structures. The core of the semantic network was identified from the nominations with link weights of 98–199.

The regional actors pay special attention to the threat of the coronavirus spread, medical problems and anti-coronavirus measures. The regional content distinguishes the discussion of the negative economic consequences of the pandemic ( unemployment ), as well as Orthodox topics ( Patriarch Kirill, Russian Orthodox Church (ROC), Synod, Sanitary Instruction of the Synod, Church ). In addition to Moscow , the cities of the Moscow Region and St. Petersburg , in the semantic network of the regional authors, the nominations of the following Russian cities have large link weights: Barnaul, Bataysk, Blagoveshchensk, Bugulma, Vladivostok, Volgograd, Volzhsky, Vologda, Vlasovo, Grigorievsk, Irkutsk, Kaluga, Kamensk-Uralsk, Kemerovo, Kirov, Kovrov, Kronshtadt, Lipetsk, Naberezhnye Chelny, Novokuznetsk, Omsk, St. Petersburg, Surgut, Taganrog, Togliatti, Tomsk, Tyumen , etc. ( Figure 7 ) (colors and sizes of shapes represent three clusters according to the weight of the vertices).

Core of the semantic network of the regional actors’ content.

In the core of the semantic network of the Moscow actors, semantic accents were identified related to the spread of the pandemic and its prevention. Muscovites are also worried about the state of medicine, the specifics of treatment, the availability of drugs, the state of hospitals, the efficiency of the Ministry of Health, the number of deaths, as well as the economic crisis and the actions of the authorities. In addition to the problems of the coronavirus infection spread, political ( zeroing, amendments, Constitution, voting, rights ) and economic issues are of great importance for the Moscow actors. It is significant that in the Moscow content there are no mentions of Russian cities at all, except for Moscow , but there are the following nominations: regions, Italy, China, Europe, USA ( Figure 8 ).

Core of the semantic network of the Moscow actors’ content.

3.5. Associative Network

The analysis of lexical associations was performed according to the results of the associative search and the construction of an associative network made it possible to identify implicatures and subtext information characterizing the attitude of the actors to certain processes and phenomena.

The data analysis revealed that Russian-speaking actors pinned their greatest hopes on government bodies during the crisis caused by the coronavirus infection spread. Meanwhile, it should be noted that a group of Moscow actors stands out among the digital communities that showed sharp negative reactions. Thus, the stimulus authority in the content of the Moscow actors has the following reactions: kremlinbots, majors, accused, amendments, criminals, gangs, weapons . The stimulus state does not cause negative reactions in both groups, as well as stimulus authority among the regional actors ( Figure 9 , Figure 10 , Figure 11 and Figure 12 ) (the size and color of the circle depends on the weights of the vertices).

Associative network for stimulus state (10/24,393), regional actors).

Associative network for stimulus state (10/2,510), Moscow actors).

Associative network for stimulus authority (10/10,946, regional actors).

Associative network for stimulus authority (10/24,393, Moscow actors).

The attitude towards the strengthening of digital transformation is almost identical in all digital communities, therefore, below are the reactions derived from the common database:

Associative network for stimulus online-services.

The highest index of 10/99,460 among digital transformations was received by online services and applications, which the actors discussed most often and intensively. It was these technological solutions that helped the city residents cope with the conditions of movement restriction and the danger of infection. The most in demand were delivery services, medical and banking services, home digital theatres, applications for schoolchildren and platforms for convening conferences. Among the IT companies that provide such services, the actors distinguish the Russian holding Yandex.

The content included the following reactions:

“(…) Russians started buying groceries more often in “neighborhood stores”: the indicator there grew by 10%; on the contrary, in large supermarkets, the volume of expenditures fell by 3%. VTB also notes an increase in expenses for home delivery of groceries and home online services (…).”

Associative network for stimulus online-medicine.

The natural high interest of the actors was aroused by the possibilities of online-medicine (the stimulus index is 10/84,101) during the pandemic. Consulting services involving digital resources at the stages of prevention, diagnosis, rehabilitation and treatment of mild forms of the disease facilitated the organization of individual medical care and a reduced burden on medical personnel to a certain extent. It should be noted that this situation is typical mainly for the city of Moscow. In the regions, the pandemic revealed the shortcomings of digital development.

“During the Ebola epidemic in West Africa in 2014–2016, more people died from disruptions to daily health care than from the disease. Telemedicine should become more accessible and people with chronic conditions should receive medicines for three months whenever possible, in case of supply disruptions (...) preventive services should be continued.”

Associative network for stimulus distance learning.

The need to transfer the learning process to the distance learning form caused a strong reaction, especially in secondary schools, from both teachers and parents. The technical unreadiness of schools and staff and the lack of teaching materials led to a very difficult situation at the beginning of the transition to the online format. Later, the situation stabilized somewhat.

“Confusion. I can’t say it was just fear, because, as they say, it is necessary. But how to do it? How should it look like, how should all this be organized? These questions were considered, of course.”

Associative network for stimulus distance work.

The transition to distance working in a metropolis has led to the need to stay in a small enclosed space for all family members. The inability to go outside during the quarantine was especially painful for families with small children. As a result, there has been an unprecedented migration from metropolises to suburbs and small towns.

“Due to the COVID-19 pandemic, many office workers have switched over to remote work and have found that they have more stress and less time for themselves. It turned out that when they took a couple of hours every day to get to and from work, there was more freedom. The number of excessive work hours are now three hours a day on average.”

Associative network for stimulus digital pass.

The introduction of digital or electronic passes to get around the city provoked a violent reaction from the actors. It should be noted that some of the city residents showed understanding toward this innovation as a means to stop the spread of infection. Others began to actively discuss the violation of the residents’ rights and freedoms. Negative reactions were fueled by technological failures that led to the automatic issuance of fines for people who passed the registration correctly or did not leave their apartments at all.

“In Moscow, electronic passes based on 16-digit alphanumeric combinations were introduced, the issuance of which began on 13 April, and on 14 April it became known about the cancellation of almost a million of them: the reason was incorrect or inaccurate data indicated during their registration.”

Associative network for stimulus electronic pass.

“Since nothing makes a person so desperate that he/she no longer believes in anything holy and bright, as the inability to leave the house and go somewhere, by car, subway, bus, and sometimes just walk down the street or take a walk in the park. For example, in Russia, the mayor of Moscow is criticized a lot for what he did: with the onset of the viral madness, he introduced various restrictions for people who could not just go out into the street. And the electronic passes, without which it was impossible to get on any city transport, were called “the beginning of the end of the world.”

Associative network for stimulus surveillance camera.

Surveillance cameras have long been controversial in society. In August 2020, based on complaints from city residents concerning interference with their privacy, access to their biometric data and the technical security of such data in connection with the use of video cameras of the city video surveillance network with a face recognition system, RosKomSvoboda sent an official request to the Information Technology Department of the city of Moscow and the Ministry of Internal Affairs of Russia in Moscow. Later it was discovered that data from video surveillance cameras were sold on the Darknet; law enforcement officers tracked down two malefactors who turned out to be employees of the Ministry of Internal Affairs, and a criminal case was opened against them ( https://roskomsvoboda.org/post/court-fined-police-for-leak-of-biometric-data/ ) (accessed on 30 January 2020). Supporters argued that ubiquitous video surveillance makes it possible to maintain safety in an urban environment, while opponents insisted there was a violation of privacy as city residents found themselves defenseless against data leaks and sales, the collection of redundant data for profit, non-compliance or formal compliance with the law, and ineffective protection or unwillingness to protect personal data.

It is significant that during the quarantine period, data from surveillance cameras were used by both government agencies to detect violators of quarantine measures, and city residents to prove illegal actions, for example, of law enforcement agencies.

“The Investigative Committee opened two criminal cases. Alexander Konovalov, in turn, took the offensive and published two posts at once on his social networks. On the first, he showed how the security-service agents knock down the door of a restaurant and put everyone on the floor. The footage shows that some of those present in the bar took a beating with batons from the security-service agents in masks (it is unclear whether they are visitors or employees of the restaurant). The surveillance camera footage is accompanied with an emotional post.”

Associative network for stimulus Government Services.

During the period of quarantine and self-isolation, portals for the provision of electronic government services, court websites, etc. made it not possible to stop proceedings for important civil issues. Meanwhile, technical failures that occurred due to the increased load caused a negative assessment from city residents.

“The first exultation regarding the fact that the authorities finally turned around to face their citizens and began to respect their dignity and time, was replaced by disappointment and even indignation!”

4. Discussion

The crisis caused by the pandemic has led to the need to accelerate digital transformation, to find optimal solutions to overcome the negative consequences in various spheres of society. Thus, technologies that can help cope with the global threat, including drones to detect infected people, robots that replace and protect medical personnel working with infected people; and blockchain technologies to ensure the confidentiality of transactions are investigated in [ 47 ]. Artificial intelligence technologies have become in demand for predictive analytics, effective decision-making in healthcare [ 48 ] and the formation of epidemic forecasting models and control systems [ 49 ]. Intelligent systems are being actively implemented to prevent the coronavirus infection spread [ 50 ], to stop the pandemic and reduce risks [ 51 ]. To overcome the consequences of COVID-19, big data analysis tools and artificial intelligence methods are used [ 52 ], as well as technological solutions for data mining [ 53 , 54 ]. The concepts of nanotechnology in the fight against the COVID-19 pandemic are presented in [ 55 ].

It is clear that the pandemic has caused enormous damage to all aspects of society. Thus, in the real sector of the Russian economy, according to a study conducted by Google, the Center for Training Leaders and Teams of Digital Transformation (RANEPA) and the Center for Advanced Management Solutions, 33% of the total number of Russian companies in the first half of 2020 suffered losses of more than 1.5 billion rubles, 46% of representatives of business structures announced a decrease in demand for their products or services. In addition, 46% of the population noted a significant reduction in income, and 33% in savings.

Meanwhile, the pandemic had a stimulating effect on the development of digital technologies: 57% of business representatives noted the acceleration of digitalization within their companies, 38% noted changes in their management culture and corporate culture, and 29% noted a reduction and reorganization of ineffective components of the business process (departments, sections, regulations, etc.).

The pace of digitalization within the corporate and public sector has increased, as well as the digitalization of processes that are less effective in the “analog” form. The introduction of digital transformation in government structures began to be perceived more positively, since responsible officials saw the real benefits of these transformations.

Society has also been forced to use digital techniques more actively. More than 33% of respondents aged 31 to 45 answered that they have started to use digital services more often. Wealthy and educated residents of metropolises are most positive about digital transformation, which implies expanding economic opportunities. Residents of small settlements express fears related to tax increases, job losses and reduced opportunities to find work in the “gray” area [ 56 ].

5. Conclusions

Analysis of data from Russian-speaking users showed that the intensification of digital transformation during the COVID-19 pandemic caused a controversial response from society.

As a result of the study, for the first time, materials were collected and analyzed that made it possible to determine the features of the analysis of data from a smart city, via the digital traces of residents during the onset of the COVID-19 pandemic. The city of Moscow was used as an example to analyze society’s response to the acceleration of digital transformation and its citizens’ perception of digital innovations.

The study confirmed the assumptions that the growth of social tension directly depends on the communication strategies and the tactics that government agencies choose to inform residents with during the pandemic. To prevent panic caused by the healthcare crisis and the formation of collective anger, it is necessary to establish a dialogue between the authorities and society, and to organize targeted and personal assistance that can be implemented using digital resources. Meanwhile, it should be borne in mind that technological failures of such services are extremely painful for society during the crisis period. It was also confirmed that the willingness of citizens to use new smart city technologies of directly depends on the level of trust in social institutions, authorities and technology corporations. In addition, the thesis was confirmed that prevention and protection from infection in communities play an important role in containing and controlling the spread of infection.

The most popular among actors were online services providing delivery, medical, banking services, home digital theatres, applications for schoolchildren and platforms for convening conferences. Among the IT companies that provide such services, the actors distinguish the Russian holding Yandex.

Distance learning, digital passes and surveillance cameras received the most negative reactions from actors. The need to transfer the learning process to the distance learning form caused a strong reaction, especially in secondary schools, both among teachers and students’ parents. Digital passes and surveillance cameras demonstrate how the benefits of digital technology can lead to risks of unethical use of personal data and privacy breaches. The main fears of the actors were related to fines for erroneous data from video cameras and transport related to movement around the city, as well as technological errors with passes.

Portals for the provision of electronic government services, court sites, etc., made it possible not to stop proceedings for important civil issues, but technical failures that occurred due to the increased load caused a negative assessment from city residents, especially in connection with wrongful fines.

The speed of coronavirus infection in Russia began from the capital, which explains the selection of actors of two groups in the database by type: Moscow and regional (actors from the Moscow region, other regions of Russia, as well as Russian-speaking actors from other countries). The analysis of the content showed the politicization, opposition and territorial egocentrism of the former, which is not observed among regional actors in the period under study. The reactions of Moscow users are characterized by negative perception and skepticism; regional actors are more loyal, showing more support and approval of the actions of the state structures during the first wave of the pandemic, in particular, regarding the strengthening of digital transformations.

Future directions of research may be related to identifying the reaction of society to various measures that the authorities are proposing to stop the pandemic, identifying the most effective resources for digital transformation.

Thematic structure of the content of regional actors:

Thematic structure of the content of Moscow actors

Author Contributions

Conceptualization, A.A.K., A.N.R. and M.P.; methodology, A.A.K., A.N.R. and M.P.; software, A.A.K.; validation, A.N.R.; formal analysis, A.N.R.; investigation, M.P.; writing—original draft preparation, M.P.; writing—review and editing, A.N.R.; visualization, M.P.; supervision, A.A.K. and A.N.R. All authors have read and agreed to the published version of the manuscript.

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Data availability statement, conflicts of interest.

The authors declare no conflict of interest.

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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Cervical Cancer Research Breakthrough: MSHA Student's Capstone Published in Prestigious Journal

Please join us in congratulating Northwestern SPS online Master's in Health Analytics (MSHA) student Dr. Francis Magaji, who had his capstone project paper accepted for publication in the BMC Public Health journal . The paper aimed to determine predictors of cervical precancer in women living with HIV and receiving care and treatment at HIV/AIDS clinics in Jos, Nigeria.

"[Dr. Magaji’s achievement] showcases the depth of knowledge and analytical skills our students acquire during their time in the program and underscores the real-world impact of our curriculum," says Imran Khan, faculty director of the MSHA program.

As a specialty-trained obstetrician, Dr. Magaji has an interest in cervical cancer prevention and control among women with HIV in Jos, where he is based. Nigeria, second only to South Africa in the number of persons living with HIV in Sub-Saharan Africa, is estimated to have over 53 million women at risk of cervical cancer . "Women with HIV have a six-fold higher risk of developing cervical cancer due to both a higher prevalence and longer clearance time, and/or persistence of high-risk HPV compared to women without HIV," says Dr. Magaji.

Despite the high prevalence of HIV, the study found a significant lack of preventive care and resources in the metropolitan area. "The absence of organized screening services within HIV clinics exposes women with HIV to preventable cervical cancer incidence and mortality, thereby eroding the hard-fought gains from HIV treatment," says Dr. Magaji.

The online Northwestern MS in Health Analytics program was developed in partnership with the Feinberg School of Medicine and Northwestern Memorial HealthCare. Dr. Magaji discusses more about his experience in the program, as well as how it has led to further opportunities in research and advancing his healthcare career below.

What did you gain from your experience in the online MS in Health Analytics program?

"Completing the Northwestern MS in Health Analytics program enabled me to learn about healthcare analytics leadership that utilizes data generated from research projects for strategic use as data assets, facilitates faster response to changes in the research project, and supports information and analytics. I learned that healthcare data could be used for policy change to increase access to healthcare, improve the quality of healthcare, and reduce disease burden in a population.

The skills acquired were demonstrated in this capstone project, which measured prevalence and predictors of abnormal cervical cytology outcomes among previously unscreened women and the general population of women with HIV in Jos Metropolis, Nigeria. The estimates measured would be used for designing standard care for women with HIV, determining costs, and for predictive screening model. The findings from the publication will be used to advocate for policy change to increase cervical cancer screening coverage in both the general population and the high-risk population."

What's next for you after the online MS in Health Analytics program?

"My learning experience at Northwestern SPS has resulted in my participation in the NIH/NCI implementation research grant titled ‘U01 West Africa Self-Sampling HPV Based Cervical Cancer Control Program (WA-SSHCCP)’ as a co-investigator. I look forward to collaborating with resource-rich countries to increase research funding for projects on both HIV treatment and cervical cancer prevention. This clarion call for funding aims to reduce cervical cancer incidence and mortality by preventing the progression of abnormal cervical cytology to invasive cervical cancer through effective screening and early treatment of precancerous cervical lesions among women with HIV in resource-limited settings.

I am equally hopeful for an opportunity for career advancement through a PhD program in areas related to clinical services, public health, and health analytics."

Northwestern University School of Professional Studies offers many degree and certificate programs, with evening and online options available. To learn more about how Northwestern University's  Graduate Programs in Health Analytics prepares students to advance their careers in health analytics, fill out the form below and we will contact you soon.   

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Our Leadership

The alturas leadership team is comprised of long-time industry professionals who work together to drive a culture of innovation with a team-first mentality. we strive to create a future of continual progress for our employees, clients, industry and our world..

Robin Woods

Robin Woods is president and founder of Alturas Analytics, Inc. A 1987 graduate of Washington State University, Robin brings her entrepreneurial spirit and business acumen to the contract lab industry. Previously, Robin contributed to the foundation and success of two environmental testing laboratories providing her with essential contract laboratory experience to lead Alturas through over 20 years of sustained organic growth. Robin’s business philosophy is to encourage teamwork, diligence and scientific integrity while nurturing an environment of cultural excellence. Robin’s achievements have been recognized by several state officials – In 2007, Robin was appointed to the Idaho Innovation Council by Governor Dirk Kempthorne. In 2010, Robin’s personal and professional accomplishments were acknowledged as she was honored as one of Idaho’s Women of the Year. In 2013, Robin was appointed by Governor C.L. (Butch) Otter to the Idaho Economic Advisory Council. She continues to serve as Region II Representative on the seven-member council, which advises Governor Brad Little and the Department of Commerce on economic development policy and block grant awards. Robin is also a member of the Higher Education Research Council, providing her knowledge to serve the Idaho State Board of Education. She is a board member of the University of Idaho Research Foundation and Gritman Medical Center in Moscow, ID. As president, Robin oversees business operation at Alturas and all duties required of Test Facility Management as well as sets strategic direction for growth and expansion. She is also responsible for assuring Alturas’ full regulatory compliance and its financial welfare.

Jennifer Zimmer, PhD

Laboratory director.

Dr. Jennifer Zimmer is the Laboratory Director at Alturas Analytics, Inc. and has been working in the field of bioanalysis for over 20 years. She received her B.A. degree in English and Zoology from the University of Idaho and her Ph.D. in Pharmacology from the University of Colorado Health Sciences Center, working in Dr. Robert Murphy’s laboratory on the leukotriene lipid mediator pathway. Her post-doctoral experience in Dr. Richard Smith’s laboratory focused on using metabolomics to elucidate disease pathways and to discover novel biomarker targets. Dr. Zimmer is responsible for the overall operation of the Alturas Analytics laboratory. She has experience with FTICR, TOF, ion trap and quadrupole instrumentation. She has utilized these instruments for quantitation as well as structure elucidation using HPLC-MS/MS and HPLC-MSn. She oversees the scientific staff and ensures that client deliverables are met while working laterally with the Alturas Analytics, Inc. QAU in order to maintain laboratory compliance with all procedures and regulations. Dr. Zimmer is an active participant in the Global CRO Council (GCC) and a member of the American Society for Mass Spectrometry.

Chad Christianson

Senior principal scientist.

Chad has over 20 years of related experience as an analytical scientist with over 17 years focused on bioanalysis at Alturas Analytics. This depth of experience in applied LC–MS/MS and GC–MS/MS, along with an education in chemical engineering, provides the backbone for productive and innovative science. Chad leads the biologics quantitation group at Alturas, applying novel techniques to a regulated, high-throughput production environment. As a Senior Principal Scientist, Chad’s primary focus is LC–MS/MS method development, validation, and sample analysis for small new chemical entities, oligonucleotides, biologics, and antibody drug conjugate (ADC) programs in accordance with GLP guidelines. In addition, Chad leads a team of scientists as Study Director and Principal Investigator, providing technical oversight to clients across all therapeutic areas.

Bo Cheng, PhD

Director of information technology.

Bo is the Director of Information Technology and GDPR Data Protection Officer at Alturas Analytics, Inc. As modern instruments rely heavily on computers and IT technologies to acquire, process, and store data, it is vitally important for a modern laboratory to have a robust and secure IT environment. As a GLP and 21 CFR Part 11 compliant laboratory, Alturas Analytics must maintain the highest standards in its IT systems to ensure the integrity and security of electronic data. This is exactly what Bo and his department do. Bo holds multiple IT vendor certifications as Systems Engineer and DBA. His 20 years of IT experience in analytical laboratory settings allows him to effectively perform system planning, validation, and management.

Ann Hoffman

Vp strategic development.

Ms. Hoffman has a history of over 30 years in the bioanalytical CRO and applied life sciences industries from both the scientific and business perspectives. The primary objective as the Vice President of Strategic Development at Alturas Analytics, Inc. is to lead business planning strategies of both the external and internal competitive landscape, identify opportunities for expansion, customers, markets, and new industry developments and standards. The goal is to achieve sustainable business growth while mitigating risk. Ms. Hoffman came to Alturas Analytics, Inc. from the specialized field of accelerator mass spectrometry services as Director of Marketing and Sales at Eckert & Ziegler Vitalea Science. She served as primary interface between Vitalea’s scientific team and Sponsors with the goal of developing loyalties that encourage repeat business. Prior to joining Vitalea Ann was the strategic account manager at Tandem Labs responsible for the company’s largest accounts worldwide. She was Tandem’s first Business Development Director and was instrumental in growing the company from start-up to a major Bioanalytical CRO with a reputation for quality and personalized service. Ann has been involved in product launches of early commercial ion trap systems at Torion Technologies, Thermo Fischer Scientific (formerly Finnigan Corporation) and time-of-flight mass spectrometers at Sciex (formerly Perseptive Biosystems) directed to early adopters of new technologies. She started mass spectrometry user groups in the greater Salt Lake City, Denver, and Seattle areas bringing scientists together across a broad spectrum of applications to engage in open exchange of ideas. She is a member of the American Society for Mass Spectrometry, American Association of Pharmaceutical Scientists, International Society for the Study of Xenobiotics, and Pharmaceutical and BioScience Society.

Maggie McMullen

Chief business development officer.

Maggie McMullen is the Chief Business Development Officer at Alturas Analytics, Inc. A 1996 graduate of the University of Wisconsin – Madison, Maggie has worked in the CRO industry for over 20 years. The first 10 years of her career were focused in the laboratory as a scientist and Study Director before transitioning to Business Development. Over the last 18 years, Maggie has worked in Business Development for a variety of fields including Drug Metabolism, Bioanalytical, CMC, Regulatory Affairs, and most recently in Pharmacology/Regulated Toxicology. As Chief Business Development Officer at Alturas Analytics, her primary objective is to strengthen Alturas’ business planning strategies, focus on building internal and external relationships and to foster company growth as a leader in the LC-MS/MS and GC-MS/MS bioanalytical field. Key to this position is identifying opportunities for expansion, customers, markets, and new industry developments and standards. Maggie joined Alturas Analytics from Lovelace Biomedical as Business Development Operations Director. She built the Commercial Business Development team to achieve a record 300% growth over a 5-year period. Her CRO history also includes Director of Business Development at Qualyst Transporter Solutions and XenoTech, a BioIVT Company. She is a member of the American Association of Pharmaceutical Scientists and served as the Chair of the Career Development Committee there from 2018 – 2020.

Lori Payne, PhD

Executive director of business development.

Dr. Lori Payne is the Executive Director of Business Development at Alturas Analytics, Inc. She earned a BS in Environmental Biochemistry from UC Davis and a Ph.D. in Chemistry from LSU Baton Rouge. Previously, she has volunteered with the Peace Corps providing assistance in Central America. Dr. Payne has a distinguished career in large pharma in addition to managing and directing growth and development in Contract Research Organizations with bioanalytical, discovery, and analytical groups in animal and human health. As Vice-President of Bioanalytical, Analytical, and Discovery at BASi (now Inotiv), she achieved numerous awards for her lean and continuous improvement focus. After joining the Alturas team in 2019, Dr. Payne successfully organized the research committee and contributed to the growth of the company. She acts as liaison between scientific and management staff at Alturas and prospective clients while looking for new business development and research opportunities. She enjoys maintaining business relationships and staying informed of current developments in the bioanalysis of large and small molecules. Dr. Payne looks forward to Alturas’ completion of a new building and expansion in Moscow, ID.

Jera Kantz, LATG, RQAP-GLP

Quality assurance manager.

With over 16 years in the CRO industry, Jera A. Kantz, BA, LATg, RQAP-GLP, serves as the Quality Assurance Manager at Alturas Analytics, Inc. Jera’s early history as a Research Associate II (RAII) with a focus on experimental therapeutics provided the background and a passion for the preclinical drug research process. After obtaining her AALAS LATG certification she then transitioned to Quality Assurance Auditor at MPI Research. In 2013, Jera joined the Charles River Laboratories QAU and quickly advanced to Senior Quality Systems Auditor. She hosted numerous client audits and became proficient at auditing computer systems validations and bioanalysis across immunogenicity, cell culture, flow cytometry and PCR techniques. An important aspect of Jera’s position is training auditors and general site staff in GLP, GCP, and Part 11 compliance, OECD principles and other applicable Guidance for Industry. Influencing others to embrace compliance that intuitively leads to generation of quality data and patient safety is her ultimate goal. Jera is an active member and contributor to the Society of Quality Assurance.

Lauren Bowersox, SHRM-CP

Human resource manager.

Lauren is a graduate of Calvin University with a Bachelor’s Degree in Business and Mathematics. Her passions of working closely with people and motivating them to develop their potential led to a career in human resources. With over 20 years in the profession, Lauren has held a variety of leadership positions including general management, compensation and benefits specialist, and recruitment coordinator within healthcare and related industries. Her focus on details and adherence to rules combined with her ability to help individuals feel connected are well suited for inspiring staff at all levels to advance their careers at Alturas.

Heidi Schmuck

Laboratory operations manager.

Heidi Schmuck is the Laboratory Operations Manager at Alturas Analytics, Inc. With a bachelor's degree in Biological Sciences and master's degree in Business Administration, she brings 17 years of regulated industry experience. During her nine-year tenure at Alturas Analytics, Heidi successfully built the supporting structure for Alturas’ facilities, equipment, and inventory management groups. Her continual emphasis on creation and organization of systems to increase efficiencies and safety throughout the operation allows Alturas to maintain high quality standards while continuing to grow in an evolving industry. Heidi oversees the logistics and functional workflows at Alturas facilities as well as the implementation of the recent 17,000 square foot expansion of the Alturas campus.

Kerri Renner

Director of finance.

Kerri Renner is the Director of Finance at Alturas Analytics. She joined the Alturas staff in 2009 and was responsible for all bookkeeping, financial reporting, and administrative duties. As Alturas’ business escalated, Kerri’s responsibilities evolved to accommodate the increased needs of the company. She was named Director of Finance in 2019 and supervises an increasing staff of financial and administrative personnel. Prior to joining the Alturas team, Kerri’s experience spanned various businesses around the Pacific Northwest and brings with her extensive knowledge of accounting, finance, and business principles. At Alturas, she has established strategies and policies that enhance the long-term growth and health of the company. Additionally, she serves as secretary of the board of directors and reports on the goals, budgets, and financial health of the company to the shareholders and directors. Kerri’s professionalism and personal integrity are integral to her role at Alturas. She diligently works to establish a productive, welcoming, and cooperative environment with both clients and team members. She sees the growth of Alturas and the anticipation of future needs and accomplishments as both exciting and challenging.

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• Meet a Husker: Kinna Arp

11 Mar 2024    

Meet Kinna Arp, a graduate student from Lincoln, Nebraska, who is pursuing a master's in nutrition and health sciences with a graduate certificate in business analytics.

Why did you choose the University of Nebraska-Lincoln?

I selected the University of Nebraska-Lincoln as my academic destination, drawn by the familiarity of my upbringing in Lincoln. After completing a couple of years at Southeast Community College, I discerned that transitioning to UNL would be a strategic and fulfilling move for my educational journey.

What activities and organizations are you involved in on campus?

I proudly serve as the Graduate Assistant for Group Fitness at Campus Recreation, where my responsibilities encompass teaching academic classes, leading dynamic group fitness sessions, overseeing the programming and training for the Women's Strength program, and working with personal training clients.

What has been your favorite class so far and why?

The highlight of my academic journey has been the Introduction to Personal Training class, as it not only allowed me to discover my passion but also led to a job that recognized my dedication. This recognition eventually resulted in my promotion to the position of Graduate Assistant.

What are your career goals?

I aspire to build a career in real estate, focusing on the ownership and management of small apartments. Additionally, I am passionate about assisting clients in enhancing their well-being by providing guidance on nutrition and physical fitness. My goal is to empower individuals to feel more confident and comfortable in their bodies, creating a holistic approach to their overall health and lifestyle.

If you are a CEHS student who would like to be featured in the Meet a Husker series, please complete  this form .

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Moscow DOH uses AI platform to detect lung cancer symptoms

Credit: Botkin.AI

Botkin.AI, a Russian software platform has been integrated with the Unified Radiological Information System of Moscow, allowing CT studies from 46 medical organisations connected to the service to be automatically downloaded for analysis.

Further it is planned that a large number of scans, implemented during the COVID-19 pandemic will be retrospectively reviewed with the help of Botkin.AI platform, using a new algorithm created to detect malignant tumours in the studies with the damaged lung tissues because of COVID-19.

The new functionality of the platform was funded by a grant from  Skolkovo Foundation , a non-profit government organisation which supports technological entrepreneurship in Russia.

The Botkin.AI platform will eventually be expanded to also analyse digital X-rays and mammography studies to aid breast cancer diagnosis.

WHY IT MATTERS

This project will contribute to a national government healthcare project to increase early stage lung cancer detection.

To date, the AI has analysed more than 7,000 studies and it is planned to process around 20,000 studies per month, making it the largest project in the world to use AI for radiology.

THE LARGER CONTEXT

Meanwhile in the UK, AI-powered cancer diagnostics, Ibex Medical Analytics and provider of digital pathology services in the NHS, LDPath, recently announced the rollout of clinical grade AI application for cancer detection in pathology.

In a US study published in the journal Nature Medicine , researchers from Mount Sinai Health System used AI algorithms in conjunction with chest CT scans and patient history to quickly diagnose patients who were positive for COVID-19 and improve the detection of patients who presented with normal CT scans.

ON THE RECORD

Sergey Voinov, head of digital medicine at Skolkovo Foundation, said: " I am confident that it will be possible to scale up AI technology in medicine even more in the near future, as we are now seeing positive effects on the healthcare system and patients.

“The particular relevance of AI technologies can be noted in terms of the. epidemiological situation that we are all experiencing now, and healthcare system performance as well as the welfare of patients in the future will depend on the integration speed of such technologies.” 

Sergey Morozov, director of the Diagnostic and Telemedicine Centre of the Moscow City Health Department, said: “A radiologist will see the CT scans in which a tumour process is most likely detected. It’s no exaggeration to say that such an intelligent triage can save patients’ lives.”

Sergey Sorokin, CEO of Botkin.AI, said: "The project implemented by the Diagnostic and Telemedicine Centre of the Moscow City Health Department is not only the world's largest project in the use of artificial intelligence technologies in healthcare for the whole region today, but also the most qualitatively elaborated one from both technological standpoint and from the AI use in practical healthcare methodology point of view. We are grateful to the whole project team from the Center for the constructive work and the opportunity to implement our products in this crucial project for the whole industry".

Learn more at the Skolkovo paviliion during the  HIMSS & Health 2.0 European Digital Event  taking place on 7-11 September 2020. 

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• Data Analytics
• Statistical Analysis using Excel
• Data Analysis Python and R
• Data Visualization Tableau and Power BI
• Linear and logistic regression modules
• Clustering using kmeans
• Supervised Learning

Tools Covered

Industry Projects

Rating prediction for apps on google play store.

Make a model to predict the app rating, with other information about the app provided to boost its visibility.

Demand Forecast for Walmart

Predict store sales and demand, factoring in economic conditions for the retail giant Walmart’s stores across the United States.

Designing a Sales dashboard in Excel

Explore Excel to analyze sales based on various product categories.

Online Car Rental Platform

Build an online car rental platform where customers should be able to view the available cars that can be rented based on categories

Comparison of Regions Based on Sales

Build a dashboard to visualize the region-wise sales performance and suggest the necessary improvements.

Identify the causes and develop a system to predict heart attacks in an effective manner using the datasets on the factors that might have an impact on cardiovascular health.

Disclaimer - The projects have been built leveraging real publicly available data-sets of the mentioned organizations.

Program Advisors and Trainers

Program advisors.

Patrick J. Wolfe

Patrick J. Wolfe, an award-winning researcher in the mathematical foundations of data science, is the Frederick L. Hovde Dean of the College of Science at Purdue University and was named the 2018 Distinguished Lecturer in Data Science by the IEEE.

Program Trainers

Christopher Hemmel

Business data analyst.

Sonal Ghanshani

Consultant and corporate trainer.

Shubham Pandey

Strategy consultant.

Data Scientist | Corporate Trainer

Join the data analytics industry.

Data science and analytics jobs are predicted to increase 28% by 2020, according to an IBM report. The global analytics market is expected to grow by $132.9 billion during the period of 2016 to 2026 (Source: Market research future report)

Expected New Jobs For Data Science And Analytics

Annual Job Growth By 2026

Average Annual Salary

Companies hiring Data Analysts

Batch Profile

This Data Analytics Course caters to working professionals across industries. Learner diversity adds richness to class discussions and interactions.

Alumni Review

I had a great learning experience, and the faculty was very encouraging. The projects were vital in helping me understand whatever I learned during the course. I have also gained a lot of great professional contacts through this course. The course was very well structured, too.

Rose Ashford

What other learners are saying

Admission details, eligibility criteria.

For admission to this Data Analytics Certification Course, candidates:

Admission Fee & Financing

The admission fee for this Data Analytics Course is € 2,790, which covers applicable certification charges & Alumni Association membership fee.

Financing Options

We are dedicated to making our programs accessible. We are committed to helping you find a way to budget for this program and offer a variety of financing options to make it more economical.

Pay in Installments

You can pay monthly installments for Post Graduate Programs using Splitit payment option with 0% interest and no hidden fees.

We provide the following options for one-time payment

• Credit Card

Program Cohorts

Next cohort.

25 Mar, 2024

11 May, 2024 - 17 Nov, 2024

16:30 - 20:30 MST

Weekend ( Sat - Sun )

Data Analytics Certification Course FAQs

What is data analytics.

Just about everything is data-driven these days, from market research and sales figures to expenses and logistics. To most people, this information can be overwhelming and daunting. It can be difficult and time-consuming to sort through it all and know what’s important, what isn’t, and what it all means. This is where Data Analysts come into the picture: they take this information and do thorough data analysis and  turn it into useful information for businesses, which allows them to make more informed decisions in the future.

What should I expect from this Data Analytics Course?

As a part of this Data Analytics Course, in collaboration with IBM, you will receive the following:

• Simplilearn-Purdue University Joint Certificate.
• Industry recognized certificates from IBM (for IBM Data Analytics modules) and Simplilearn
• Purdue Alumni Association membership eligibility.
• Lifetime access to all core eLearning content created by Simplilearn

How long does it take to learn Data Analysis?

The time taken to learn data analytics varies from person to person. It depends on your dedication to studying, prior knowledge of the field, and work experience in data analytics. While some of the concepts may take a few days, others may take a couple of months to grasp. When you take our Data Analytics Course, you should apply the concepts you learned to real-world use cases to gain practical exposure and reinforce your learning.

What is the salary potential of a Data Analytics Professional?

The average annual Data Analyst job salary is over $61,000 per year.

How do I know if the Data Analytics is right for me?

Learning new skills and expanding your knowledge is always a plus point. This Data Analytics Course is developed in collaboration with Purdue University, a perfect blend of world-renowned curriculum and industry-aligned training, which makes the Data Analytics Course just the right one for you!

What are the eligibility criteria for this Data Analytics Course in partnership with Purdue University?

For admission to this Data Analytics Course , candidates:

• Should have a bachelor's degree in any discipline with an average of 50% or higher marks
• With a non-programming background can also apply
• Having prior work experience is not mandatory

Is there any minimum education qualification required to apply for this Data Analytics Course?

Yes, you are supposed to have a bachelor’s degree with an average of 50% (or higher) if you wish to enroll in this Data Analytics Course.

What is the admission process for this Data Analytics Course in partnership with Purdue University?

The admission process for this Data Analytics Course consists of three simple steps:

• All interested candidates are required to apply through the online application form
• An admission panel will shortlist the candidates based on their application
• An offer of admission will be made to the selected candidates and is accepted by the candidates by paying the fee

Will I become an alumni of Purdue University after completion of the Data Analytics Course?

You will get eligibility for Purdue Alumni Association Membership after completing the Data Analytics Course.

How do I earn the Post Graduate Program certificate in Data Analytics?

Upon completion of the following minimum requirements, you will be eligible to receive the certificate that will testify to your skills as an expert in Data Analytics.

What are the top modules included in this Data Analytics Course?

You’ll find the best-in-class modules covered in this Data Analytics Course. The list includes:

• Analytics and Programming Foundation
• Data Analytics with Python
• R Programming for Data Science
• Data Science with R
• Data Analyst Capstone

Is there any financial aid provided for this Data Analytics Course?

To ensure money is not a barrier in the path of learning, we offer various financing options to help make this Data Analytics Course more financially manageable. Please refer to our “Admissions Fee and Financing” section for more details.

Will any preparation material be provided to get started in this Data Analytics Course?

Once you make the first installment of the fee, you will be given access to a preparatory program with eight to 10 hours of self-paced learning content in the form of videos. You will have to go through the assigned program before attending the first class.

What tools and languages do we learn in this Data Analytics Course?

With the growing popularity of data analytics, several tools have been thought in this Data Analytics Course. Some of the important Data Analytics tools that provide various advanced features include Tableau, Power BI, SAS, QlikView, RapidMiner, and MS Excel.

Is this Data Analytics Course taught online? Do I need to attend any physical classroom sessions?

This Data Analytics Course is completely online. You can access the Data Analytics Course material anytime and anywhere with a computer or smartphone connected to the internet.

How will my doubts/questions be addressed in this Data Analytics Course?

We have a team of dedicated admissions counselors who can guide you as you apply for this Data Analytics Course.

I don't have any prior knowledge in coding, can I make a career in Data Analytics?

Yes. This Data Analytics Course will teach you the fundamentals of programming languages, statistics, and industry-standard techniques from scratch to build up your foundational knowledge and enhance your analytics career journey. These concepts will make you a master in data analytics.

What is Global Teaching Assistance?

Our teaching assistants are a dedicated team of subject matter experts here to help you get Post Graduate Program Certificate in Data Analytics on your first attempt. They engage students proactively to assure the course path is followed and to help you enrich the learning experience, from class onboarding to project mentoring and job assistance.

Do I need to follow the mentioned learning path for this Data Analytics Course?

We highly recommend that you follow the Data Analytics Course curriculum in the same order as listed in the learning path as the initial concepts are used in lessons that follow it.

Who are the instructors for this Data Analytics Course and how are they selected?

All of our highly qualified Data Analytics instructors are Business Intelligence experts with years of relevant industry experience. Each of them has gone through a rigorous selection process that includes profile screening, technical evaluation, and a training demo before they are certified to train for us. We also ensure that only those trainers with a high alumni rating remain part of our faculty.

Will I be able to access the content after completion of the Data Analytics Course?

Yes, you can access the course content even after the completion of the Data Analytics Course.

I am not able to access the Data Analytics Course. Who can help me?

Contact us using the form on the right side of any page on the Simplilearn website, select the Live Chat link, or contact help and support .

My current role does not include exposure to data. Does it make sense for me to opt for this Data Analytics Course?

Data is ruling businesses around the world. The more data-driven you’re, the more beneficial it is for your organization. By taking insights from data, you can make meaningful decisions, plan strategies, and help your business achieve its goal faster. Enrolling in this extensive Data Analytics Course is definitely going to be an advantage, and nothing less.

I am not from a technical background. Can I still join this Data Analytics Course?

Yes, you can join Data Analytics Course even if you do not belong to a technical background. However, having a basic knowledge of programming languages and mathematics will be beneficial.

Can I enroll in a Data Analytics Course if I don't have any prior knowledge in Data Analysis?

Yes, you can enroll in the Data Analytics Course even if you don’t have any prior knowledge since this course will take you through the fundamentals to the top of the ladder, where you learn all the advanced critical Data Analytics skills.

What is covered under the 24/7 Support promise?

We offer 24/7 support through email, chat, and calls. We have a dedicated team that provides on-demand assistance through our community forum. What’s more, you will have lifetime access to the community forum, even after completion of your Data Analytics Course.

If I want to cancel my enrollment from this Data Analytics Course, can I get a refund?

Yes, you can cancel your enrollment if necessary. We will refund the program price after deducting an administration fee. To learn more, you can view our Refund Policy .

This Data Analytics Course is offered on a non-credit basis and is not transferable to a degree.

What are the different roles in Data Analytics?

Different job roles in data analytics are Business Intelligence Analyst, Data Analyst, Data Scientist, Data Engineer, Data Visualizer, Quantitative Analyst, Operations Analyst, Data Analytics Consultant, Marketing Analyst, IT Systems Analyst, Project Manager, and Transportation Logistics Specialist.

What are the benefits of taking this Data Analytics Course?

This Data Analytics Course is beneficial for you if you are a fresher because it covers enough information for entry-level positions. If you are interested in an analytical career, enrolling in a data analytics certification might help you prepare for a new job. Enrolling in this Data Analytics Course is strongly encouraged as it offers you a competitive advantage.

What are the top roles and responsibilities of a Data Analytics Expert?

A data analytics consultant might use their skills to collect and understand the data. Data analytics consultants employ data sets and models to obtain relevant insight and solve issues. The Data Analytics Course  offers intensive learning experiences that imitate the actual world and develop projects from scratch.

Is Data Analytics a promising career? Are Data Analytics Experts in demand?

Data Analytics is a fast-growing discipline that offers professional opportunities across a wide range of sectors. Considering the current rising demand for competent Big Data specialists, there is no better time to be part of the big data job market. You require a robust array of basic analytical skills to create a career in data analytics. There are many ways to learn these skills, but the best approach for many of them is through Data Analytics Course.

Can I get a sealed transcript for World Education Services (WES) at the end of the program?

These do not include any transcripts for WES, this is reserved only for degree. We do not offer sealed transcripts and hence, our certificates are not applicable for WES or similar services.

What are the benefits of this Generative AI Masterclass?

These masterclasses are delivered in the form of live virtual sessions by experienced industry experts. This delves deep into the world of AI-powered creativity, helping you understand multiple concepts & topics related to generative AI such as effective prompt engineering, ethical considerations in GenAI, and much more. 

You will gain exposure to the world of Gen AI, some of its practical applications, some of the latest advancements in the field and much more - thus setting you apart from your competitors and helping you stay ahead in your career.

Related Programs

Post Graduate Program in AI and Machine Learning

Post Graduate Program in Data Science

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