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500+ Statistics Research Topics

Statistics is a branch of mathematics that deals with the collection, analysis, interpretation, presentation, and organization of data . It is a fundamental tool used in various fields such as business, social sciences, engineering, healthcare, and many more. As a research topic , statistics can be a fascinating subject to explore, as it allows researchers to investigate patterns, trends, and relationships within data. With the help of statistical methods, researchers can make informed decisions and draw valid conclusions based on empirical evidence. In this post, we will explore some interesting statistics research topics that can be pursued by researchers to further expand our understanding of this field.

Statistics Research Topics

Statistics Research Topics are as follows:

Analysis of the effectiveness of different marketing strategies on consumer behavior.
An investigation into the relationship between economic growth and environmental sustainability.
A study of the effects of social media on mental health and well-being.
A comparative analysis of the educational outcomes of public and private schools.
The impact of climate change on agriculture and food security.
A survey of the prevalence and causes of workplace stress in different industries.
A statistical analysis of crime rates in urban and rural areas.
An evaluation of the effectiveness of alternative medicine treatments.
A study of the relationship between income inequality and health outcomes.
A comparative analysis of the effectiveness of different weight loss programs.
An investigation into the factors that affect job satisfaction among employees.
A statistical analysis of the relationship between poverty and crime.
A study of the factors that influence the success of small businesses.
A survey of the prevalence and causes of childhood obesity.
An evaluation of the effectiveness of drug addiction treatment programs.
A statistical analysis of the relationship between gender and leadership in organizations.
A study of the relationship between parental involvement and academic achievement.
An investigation into the causes and consequences of income inequality.
A comparative analysis of the effectiveness of different types of therapy for mental health conditions.
A survey of the prevalence and causes of substance abuse among teenagers.
An evaluation of the effectiveness of online education compared to traditional classroom learning.
A statistical analysis of the impact of globalization on different industries.
A study of the relationship between social media use and political polarization.
An investigation into the factors that influence customer loyalty in the retail industry.
A comparative analysis of the effectiveness of different types of advertising.
A survey of the prevalence and causes of workplace discrimination.
An evaluation of the effectiveness of different types of employee training programs.
A statistical analysis of the relationship between air pollution and health outcomes.
A study of the factors that affect employee turnover rates.
An investigation into the causes and consequences of income mobility.
A comparative analysis of the effectiveness of different types of leadership styles.
A survey of the prevalence and causes of mental health disorders among college students.
An evaluation of the effectiveness of different types of cancer treatments.
A statistical analysis of the impact of social media influencers on consumer behavior.
A study of the factors that influence the adoption of renewable energy sources.
An investigation into the relationship between alcohol consumption and health outcomes.
A comparative analysis of the effectiveness of different types of conflict resolution strategies.
A survey of the prevalence and causes of childhood poverty.
An evaluation of the effectiveness of different types of diversity training programs.
A statistical analysis of the relationship between immigration and economic growth.
A study of the factors that influence customer satisfaction in the service industry.
An investigation into the causes and consequences of urbanization.
A comparative analysis of the effectiveness of different types of economic policies.
A survey of the prevalence and causes of elder abuse.
An evaluation of the effectiveness of different types of rehabilitation programs for prisoners.
A statistical analysis of the impact of automation on different industries.
A study of the factors that influence employee productivity in the workplace.
An investigation into the causes and consequences of gentrification.
A comparative analysis of the effectiveness of different types of humanitarian aid.
A survey of the prevalence and causes of homelessness.
Exploring the relationship between socioeconomic status and access to healthcare services
An analysis of the relationship between parental education level and children’s academic performance.
Exploring the effects of different statistical models on prediction accuracy in machine learning.
The Impact of Social Media on Consumer Behavior: A Statistical Analysis
Bayesian hierarchical modeling for network data analysis
Spatial statistics and modeling for environmental data
Nonparametric methods for time series analysis
Bayesian inference for high-dimensional data analysis
Multivariate analysis for genetic data
Machine learning methods for predicting financial markets
Causal inference in observational studies
Sampling design and estimation for complex surveys
Robust statistical methods for outlier detection
Statistical inference for large-scale simulations
Survival analysis and its applications in medical research
Mixture models for clustering and classification
Time-varying coefficient models for longitudinal data
Multilevel modeling for complex data structures
Graphical modeling and Bayesian networks
Experimental design for clinical trials
Inference for network data using stochastic block models
Nonlinear regression modeling for data with complex structures
Statistical learning for social network analysis
Time series forecasting using deep learning methods
Model selection and variable importance in high-dimensional data
Spatial point process modeling for environmental data
Bayesian spatial modeling for disease mapping
Functional data analysis for longitudinal studies
Bayesian network meta-analysis
Statistical methods for big data analysis
Mixed-effects models for longitudinal data
Clustering algorithms for text data
Bayesian modeling for spatiotemporal data
Multivariate analysis for ecological data
Statistical analysis of genomic data
Bayesian network inference for gene regulatory networks
Principal component analysis for high-dimensional data
Time series analysis of financial data
Multivariate survival analysis for complex outcomes
Nonparametric estimation of causal effects
Bayesian network analysis of complex systems
Statistical inference for multilevel network data
Generalized linear mixed models for non-normal data
Bayesian inference for dynamic systems
Latent variable modeling for categorical data
Statistical inference for social network data
Regression models for panel data
Bayesian spatiotemporal modeling for climate data
Predictive modeling for customer behavior analysis
Nonlinear time series analysis for ecological systems
Statistical modeling for image analysis
Bayesian hierarchical modeling for longitudinal data
Network-based clustering for high-dimensional data
Bayesian spatial modeling for ecological systems.
Analysis of the Effect of Climate Change on Crop Yields: A Case Study
Examining the Relationship Between Physical Activity and Mental Health in Young Adults
A Comparative Study of Crime Rates in Urban and Rural Areas Using Statistical Methods
Investigating the Effect of Online Learning on Student Performance in Mathematics
A Statistical Analysis of the Relationship Between Economic Growth and Environmental Sustainability
Evaluating the Effectiveness of Different Marketing Strategies for E-commerce Businesses
Identifying the Key Factors Affecting Customer Loyalty in the Hospitality Industry
An Analysis of the Factors Influencing Student Dropout Rates in Higher Education
Examining the Impact of Gender on Salary Disparities in the Workplace Using Statistical Methods
Investigating the Relationship Between Physical Fitness and Academic Performance in High School Students
Analyzing the Effect of Social Support on Mental Health in Elderly Populations
A Comparative Study of Different Methods for Forecasting Stock Prices
Investigating the Effect of Online Reviews on Consumer Purchasing Decisions
Identifying the Key Factors Affecting Employee Turnover Rates in the Technology Industry
Analyzing the Effect of Advertising on Brand Awareness and Purchase Intentions
A Study of the Relationship Between Health Insurance Coverage and Healthcare Utilization
Examining the Effect of Parental Involvement on Student Achievement in Elementary School
Investigating the Impact of Social Media on Political Campaigns Using Statistical Methods
A Comparative Analysis of Different Methods for Detecting Fraud in Financial Transactions
Analyzing the Relationship Between Entrepreneurial Characteristics and Business Success
Investigating the Effect of Job Satisfaction on Employee Performance in the Service Industry
Identifying the Key Factors Affecting the Adoption of Renewable Energy Technologies
A Study of the Relationship Between Personality Traits and Academic Achievement
Examining the Impact of Social Media on Body Image and Self-Esteem in Adolescents
Investigating the Effect of Mobile Advertising on Consumer Behavior
Analyzing the Relationship Between Healthcare Expenditures and Health Outcomes Using Statistical Methods
A Comparative Study of Different Methods for Analyzing Customer Satisfaction Data
Investigating the Impact of Economic Factors on Voter Behavior Using Statistical Methods
Identifying the Key Factors Affecting Student Retention Rates in Community Colleges
Analyzing the Relationship Between Workplace Diversity and Organizational Performance
Investigating the Effect of Gamification on Learning and Motivation in Education
A Study of the Relationship Between Social Support and Depression in Cancer Patients
Examining the Impact of Technology on the Travel Industry Using Statistical Methods
Investigating the Effect of Customer Service Quality on Customer Loyalty in the Retail Industry
Analyzing the Relationship Between Internet Usage and Social Isolation in Older Adults
A Comparative Study of Different Methods for Predicting Customer Churn in Telecommunications
Investigating the Impact of Social Media on Consumer Attitudes Towards Brands Using Statistical Methods
Identifying the Key Factors Affecting Student Success in Online Learning Environments
Analyzing the Relationship Between Employee Engagement and Organizational Commitment
Investigating the Effect of Customer Reviews on Sales in E-commerce Businesses
A Study of the Relationship Between Political Ideology and Attitudes Towards Climate Change
Examining the Impact of Technological Innovations on the Manufacturing Industry Using Statistical Methods
Investigating the Effect of Social Support on Postpartum Depression in New Mothers
Analyzing the Relationship Between Cultural Intelligence and Cross-Cultural Adaptation
Investigating the relationship between socioeconomic status and health outcomes using statistical methods.
Analyzing trends in crime rates and identifying factors that contribute to them using statistical methods.
Examining the effectiveness of different advertising strategies using statistical analysis of consumer behavior.
Identifying factors that influence voting behavior and election outcomes using statistical methods.
Investigating the relationship between employee satisfaction and productivity in the workplace using statistical methods.
Developing new statistical models to better understand the spread of infectious diseases.
Analyzing the impact of climate change on global food production using statistical methods.
Identifying patterns and trends in social media data using statistical methods.
Investigating the relationship between social networks and mental health using statistical methods.
Developing new statistical models to predict financial market trends and identify investment opportunities.
Analyzing the effectiveness of different educational programs and interventions using statistical methods.
Investigating the impact of environmental factors on public health using statistical methods.
Developing new statistical models to analyze complex biological systems and identify new drug targets.
Analyzing trends in consumer spending and identifying factors that influence buying behavior using statistical methods.
Investigating the relationship between diet and health outcomes using statistical methods.
Developing new statistical models to analyze gene expression data and identify biomarkers for disease.
Analyzing patterns in crime data to predict future crime rates and improve law enforcement strategies.
Investigating the effectiveness of different medical treatments using statistical methods.
Developing new statistical models to analyze the impact of air pollution on public health.
Analyzing trends in global migration and identifying factors that influence migration patterns using statistical methods.
Investigating the impact of automation on the job market using statistical methods.
Developing new statistical models to analyze climate data and predict future climate trends.
Analyzing trends in online shopping behavior and identifying factors that influence consumer decisions using statistical methods.
Investigating the impact of social media on political discourse using statistical methods.
Developing new statistical models to analyze gene-environment interactions and identify new disease risk factors.
Analyzing trends in the stock market and identifying factors that influence investment decisions using statistical methods.
Investigating the impact of early childhood education on long-term academic and social outcomes using statistical methods.
Developing new statistical models to analyze the relationship between human behavior and the environment.
Analyzing trends in the use of renewable energy and identifying factors that influence adoption rates using statistical methods.
Investigating the impact of immigration on labor market outcomes using statistical methods.
Developing new statistical models to analyze the relationship between social determinants and health outcomes.
Analyzing patterns in customer churn to predict future customer behavior and improve business strategies.
Investigating the effectiveness of different marketing strategies using statistical methods.
Developing new statistical models to analyze the relationship between air pollution and climate change.
Analyzing trends in global tourism and identifying factors that influence travel behavior using statistical methods.
Investigating the impact of social media on mental health using statistical methods.
Developing new statistical models to analyze the impact of transportation on the environment.
Analyzing trends in global trade and identifying factors that influence trade patterns using statistical methods.
Investigating the impact of social networks on political participation using statistical methods.
Developing new statistical models to analyze the relationship between climate change and biodiversity loss.
Analyzing trends in the use of alternative medicine and identifying factors that influence adoption rates using statistical methods.
Investigating the impact of technological change on the labor market using statistical methods.
Developing new statistical models to analyze the impact of climate change on agriculture.
Investigating the impact of social media on mental health: A longitudinal study.
A comparison of the effectiveness of different types of teaching methods on student learning outcomes.
Examining the relationship between sleep duration and productivity among college students.
A study of the factors that influence employee job satisfaction in the tech industry.
Analyzing the relationship between income level and health outcomes among low-income populations.
Investigating the effectiveness of online learning platforms for high school students.
A study of the factors that contribute to success in online entrepreneurship.
Analyzing the impact of climate change on agricultural productivity in developing countries.
A comparison of different statistical models for predicting stock market trends.
Examining the impact of sports on mental health: A cross-sectional study.
A study of the factors that influence employee retention in the hospitality industry.
Analyzing the impact of cultural differences on international business negotiations.
Investigating the effectiveness of different weight loss interventions for obese individuals.
A study of the relationship between personality traits and academic achievement.
Examining the impact of technology on job displacement: A longitudinal study.
A comparison of the effectiveness of different types of advertising strategies on consumer behavior.
Analyzing the impact of environmental regulations on corporate profitability.
Investigating the effectiveness of different types of therapy for treating depression.
A study of the factors that contribute to success in e-commerce.
Examining the relationship between social support and mental health in the elderly population.
A comparison of different statistical methods for analyzing complex survey data.
Analyzing the impact of employee diversity on organizational performance.
Investigating the effectiveness of different types of exercise for improving cardiovascular health.
A study of the relationship between emotional intelligence and job performance.
Examining the impact of work-life balance on employee well-being.
A comparison of the effectiveness of different types of financial education programs for low-income populations.
Analyzing the impact of air pollution on respiratory health in urban areas.
Investigating the relationship between personality traits and leadership effectiveness.
A study of the factors that influence consumer behavior in the luxury goods market.
Examining the impact of social networks on political participation: A cross-sectional study.
A comparison of different statistical methods for analyzing survival data.
Analyzing the impact of government policies on income inequality.
Investigating the effectiveness of different types of counseling for substance abuse.
A study of the relationship between cultural values and consumer behavior.
Examining the impact of technology on privacy: A longitudinal study.
A comparison of the effectiveness of different types of online marketing strategies.
Analyzing the impact of the gig economy on job satisfaction: A cross-sectional study.
Investigating the effectiveness of different types of education interventions for improving financial literacy.
A study of the factors that contribute to success in social entrepreneurship.
Examining the impact of gender diversity on board performance in publicly-traded companies.
A comparison of different statistical methods for analyzing panel data.
Analyzing the impact of employee involvement in decision-making on organizational performance.
Investigating the effectiveness of different types of treatment for anxiety disorders.
A study of the relationship between cultural values and entrepreneurial success.
Examining the impact of technology on the labor market: A longitudinal study.
A comparison of the effectiveness of different types of direct mail campaigns.
Analyzing the impact of telecommuting on employee productivity: A cross-sectional study.
Investigating the effectiveness of different types of retirement planning interventions for low-income individuals.
Analyzing the effectiveness of different educational interventions in improving student performance
Investigating the impact of climate change on food production and food security
Identifying factors that influence employee satisfaction and productivity in the workplace
Examining the prevalence and causes of mental health disorders in different populations
Evaluating the effectiveness of different marketing strategies in promoting consumer behavior
Analyzing the prevalence and consequences of substance abuse in different communities
Investigating the relationship between social media use and mental health outcomes
Examining the role of genetics in the development of different diseases
Identifying factors that contribute to the gender wage gap in different industries
Analyzing the effectiveness of different policing strategies in reducing crime rates
Investigating the impact of immigration on economic growth and development
Examining the prevalence and causes of domestic violence in different populations
Evaluating the effectiveness of different interventions for treating addiction
Analyzing the prevalence and impact of childhood obesity on health outcomes
Investigating the relationship between diet and chronic diseases such as diabetes and heart disease
Examining the effects of different types of exercise on physical and mental health outcomes
Identifying factors that influence voter behavior and political participation
Analyzing the prevalence and impact of sleep disorders on health outcomes
Investigating the effectiveness of different educational interventions in improving health outcomes
Examining the impact of environmental pollution on public health outcomes
Evaluating the effectiveness of different interventions for reducing opioid addiction and overdose rates
Analyzing the prevalence and causes of homelessness in different communities
Investigating the relationship between race and health outcomes
Examining the impact of social support networks on health outcomes
Identifying factors that contribute to income inequality in different regions
Analyzing the prevalence and impact of workplace stress on employee health outcomes
Investigating the relationship between education and income levels in different communities
Examining the effects of different types of technology on mental health outcomes
Evaluating the effectiveness of different interventions for reducing healthcare costs
Analyzing the prevalence and impact of chronic pain on health outcomes
Investigating the relationship between urbanization and public health outcomes
Examining the effects of different types of drugs on health outcomes
Identifying factors that contribute to educational attainment in different populations
Analyzing the prevalence and causes of food insecurity in different communities
Investigating the relationship between race and crime rates
Examining the impact of social media on political participation and engagement
Evaluating the effectiveness of different interventions for reducing poverty levels
Analyzing the prevalence and impact of stress on mental health outcomes
Investigating the relationship between religion and health outcomes
Examining the effects of different types of parenting styles on child development outcomes
Identifying factors that contribute to political polarization in different regions
Analyzing the prevalence and causes of teenage pregnancy in different communities
Investigating the impact of globalization on economic growth and development
Examining the prevalence and impact of social isolation on mental health outcomes
Evaluating the effectiveness of different interventions for reducing gun violence
Analyzing the prevalence and impact of bullying on mental health outcomes
Investigating the relationship between immigration and crime rates
Examining the effects of different types of diets on health outcomes
Identifying factors that contribute to social inequality in different regions
Bayesian inference for high-dimensional models
Analysis of longitudinal data with missing values
Nonparametric regression with functional predictors
Estimation and inference for copula models
Statistical methods for neuroimaging data analysis
Robust methods for high-dimensional data analysis
Analysis of spatially correlated data
Bayesian nonparametric modeling
Statistical methods for network data
Optimal experimental design for nonlinear models
Multivariate time series analysis
Inference for partially identified models
Statistical learning for personalized medicine
Statistical inference for rare events
High-dimensional mediation analysis
Analysis of multi-omics data
Nonparametric regression with mixed types of predictors
Estimation and inference for graphical models
Statistical inference for infectious disease dynamics
Robust methods for high-dimensional covariance matrix estimation
Analysis of spatio-temporal data
Bayesian modeling for ecological data
Multivariate spatial point pattern analysis
Statistical methods for functional magnetic resonance imaging (fMRI) data
Nonparametric estimation of conditional distributions
Statistical methods for spatial econometrics
Inference for stochastic processes
Bayesian spatiotemporal modeling
High-dimensional causal inference
Analysis of data from complex survey designs
Bayesian nonparametric survival analysis
Statistical methods for fMRI connectivity analysis
Spatial quantile regression
Statistical modeling for climate data
Estimation and inference for item response models
Bayesian model selection and averaging
High-dimensional principal component analysis
Analysis of data from clinical trials with noncompliance
Nonparametric regression with censored data
Statistical methods for functional data analysis
Inference for network models
Bayesian nonparametric clustering
High-dimensional classification
Analysis of ecological network data
Statistical modeling for time-to-event data with multiple events
Estimation and inference for nonparametric density estimation
Bayesian nonparametric regression with time-varying coefficients
Statistical methods for functional magnetic resonance spectroscopy (fMRS) data

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Graduate Student Handbook (Coming Soon: New Graduate Student Handbook)

Phd program overview.

The PhD program prepares students for research careers in probability and statistics in academia and industry. Students admitted to the PhD program earn the MA and MPhil along the way. The first year of the program is spent on foundational courses in theoretical statistics, applied statistics, and probability. In the following years, students take advanced topics courses. Research toward the dissertation typically begins in the second year. Students also have opportunities to take part in a wide variety of projects involving applied probability or applications of statistics.

Students are expected to register continuously until they distribute and successfully defend their dissertation. Our core required and elective curricula in Statistics, Probability, and Machine Learning aim to provide our doctoral students with advanced learning that is both broad and focused. We expect our students to make Satisfactory Academic Progress in their advanced learning and research training by meeting the following program milestones through courseworks, independent research, and dissertation research:

By the end of year 1: passing the qualifying exams;

By the end of year 2: fulfilling all course requirements for the MA degree and finding a dissertation advisor;

By the end of year 3: passing the oral exam (dissertation prospectus) and fulfilling all requirements for the MPhil degree

By the end of year 5: distributing and defending the dissertation.

We believe in the Professional Development value of active participation in intellectual exchange and pedagogical practices for future statistical faculty and researchers. Students are required to serve as teaching assistants and present research during their training. In addition, each student is expected to attend seminars regularly and participate in Statistical Practicum activities before graduation.

We provide in the following sections a comprehensive collection of the PhD program requirements and milestones. Also included are policies that outline how these requirements will be enforced with ample flexibility. Questions on these requirements should be directed to ADAA Cindy Meekins at [email protected] and the DGS, Professor John Cunningham at [email protected] .

Applications for Admission

Our students receive very solid training in all aspects of modern statistics. See Graduate Student Handbook for more information.
Our students receive Fellowship and full financial support for the entire duration of their PhD. See more details here .
Our students receive job offers from top academic and non-academic institutions .
Our students can work with world-class faculty members from Statistics Department or the Data Science Institute .
Our students have access to high-speed computer clusters for their ambitious, computationally demanding research.
Our students benefit from a wide range of seminars, workshops, and Boot Camps organized by our department and the data science institute .
Suggested Prerequisites: A student admitted to the PhD program normally has a background in linear algebra and real analysis, and has taken a few courses in statistics, probability, and programming. Students who are quantitatively trained or have substantial background/experience in other scientific disciplines are also encouraged to apply for admission.
GRE requirement: Waived for Fall 2024.
Language requirement: The English Proficiency Test requirement (TOEFL) is a Provost's requirement that cannot be waived.
The Columbia GSAS minimum requirements for TOEFL and IELTS are: 100 (IBT), 600 (PBT) TOEFL, or 7.5 IELTS. To see if this requirement can be waived for you, please check the frequently asked questions below.
Deadline: Jan 8, 2024 .
Application process: Please apply by completing the Application for Admission to the Columbia University Graduate School of Arts & Sciences .
Timeline: P.hD students begin the program in September only. Admissions decisions are made in mid-March of each year for the Fall semester.

Frequently Asked Questions

What is the application deadline? What is the deadline for financial aid? Our application deadline is January 5, 2024 .
Can I meet with you in person or talk to you on the phone? Unfortunately given the high number of applications we receive, we are unable to meet or speak with our applicants.
What are the required application materials? Specific admission requirements for our programs can be found here .
Due to financial hardship, I cannot pay the application fee, can I still apply to your program? Yes. Many of our prospective students are eligible for fee waivers. The Graduate School of Arts and Sciences offers a variety of application fee waivers . If you have further questions regarding the waiver please contact gsas-admissions@ columbia.edu .
How many students do you admit each year? It varies year to year. We finalize our numbers between December - early February.
What is the distribution of students currently enrolled in your program? (their background, GPA, standard tests, etc)? Unfortunately, we are unable to share this information.
How many accepted students receive financial aid? All students in the PhD program receive, for up to five years, a funding package consisting of tuition, fees, and a stipend. These fellowships are awarded in recognition of academic achievement and in expectation of scholarly success; they are contingent upon the student remaining in good academic standing. Summer support, while not guaranteed, is generally provided. Teaching and research experience are considered important aspects of the training of graduate students. Thus, graduate fellowships include some teaching and research apprenticeship. PhD students are given funds to purchase a laptop PC, and additional computing resources are supplied for research projects as necessary. The Department also subsidizes travel expenses for up to two scientific meetings and/or conferences per year for those students selected to present. Additional matching funds from the Graduate School Arts and Sciences are available to students who have passed the oral qualifying exam.
Can I contact the department with specific scores and get feedback on my competitiveness for the program? We receive more than 450 applications a year and there are many students in our applicant pool who are qualified for our program. However, we can only admit a few top students. Before seeing the entire applicant pool, we cannot comment on admission probabilities.
What is the minimum GPA for admissions? While we don’t have a GPA threshold, we will carefully review applicants’ transcripts and grades obtained in individual courses.
Is there a minimum GRE requirement? No. The general GRE exam is waived for the Fall 2024 admissions cycle.
Can I upload a copy of my GRE score to the application? Yes, but make sure you arrange for ETS to send the official score to the Graduate School of Arts and Sciences.
Is the GRE math subject exam required? No, we do not require the GRE math subject exam.
What is the minimum TOEFL or IELTS requirement? The Columbia Graduate School of Arts and Sciences minimum requirements for TOEFL and IELTS are: 100 (IBT), 600 (PBT) TOEFL, or 7.5 IELTS
I took the TOEFL and IELTS more than two years ago; is my score valid? Scores more than two years old are not accepted. Applicants are strongly urged to make arrangements to take these examinations early in the fall and before completing their application.
I am an international student and earned a master’s degree from a US university. Can I obtain a TOEFL or IELTS waiver? You may only request a waiver of the English proficiency requirement from the Graduate School of Arts and Sciences by submitting the English Proficiency Waiver Request form and if you meet any of the criteria described here . If you have further questions regarding the waiver please contact gsas-admissions@ columbia.edu .
My transcript is not in English. What should I do? You have to submit a notarized translated copy along with the original transcript.

Can I apply to more than one PhD program? You may not submit more than one PhD application to the Graduate School of Arts and Sciences. However, you may elect to have your application reviewed by a second program or department within the Graduate School of Arts and Sciences if you are not offered admission by your first-choice program. Please see the application instructions for a more detailed explanation of this policy and the various restrictions that apply to a second choice. You may apply concurrently to a program housed at the Graduate School of Arts and Sciences and to programs housed at other divisions of the University. However, since the Graduate School of Arts and Sciences does not share application materials with other divisions, you must complete the application requirements for each school.

How do I apply to a dual- or joint-degree program? The Graduate School of Arts and Sciences refers to these programs as dual-degree programs. Applicants must complete the application requirements for both schools. Application materials are not shared between schools. Students can only apply to an established dual-degree program and may not create their own.

With the sole exception of approved dual-degree programs , students may not pursue a degree in more than one Columbia program concurrently, and may not be registered in more than one degree program at any institution in the same semester. Enrollment in another degree program at Columbia or elsewhere while enrolled in a Graduate School of Arts and Sciences master's or doctoral program is strictly prohibited by the Graduate School. Violation of this policy will lead to the rescission of an offer of admission, or termination for a current student.

When will I receive a decision on my application? Notification of decisions for all PhD applicants generally takes place by the end of March.

Notification of MA decisions varies by department and application deadlines. Some MA decisions are sent out in early spring; others may be released as late as mid-August.

Can I apply to both MA Statistics and PhD statistics simultaneously? For any given entry term, applicants may elect to apply to up to two programs—either one PhD program and one MA program, or two MA programs—by submitting a single (combined) application to the Graduate School of Arts and Sciences. Applicants who attempt to submit more than one Graduate School of Arts and Sciences application for the same entry term will be required to withdraw one of the applications.

The Graduate School of Arts and Sciences permits applicants to be reviewed by a second program if they do not receive an offer of admission from their first-choice program, with the following restrictions:

This option is only available for fall-term applicants.
Applicants will be able to view and opt for a second choice (if applicable) after selecting their first choice. Applicants should not submit a second application. (Note: Selecting a second choice will not affect the consideration of your application by your first choice.)
Applicants must upload a separate Statement of Purpose and submit any additional supporting materials required by the second program. Transcripts, letters, and test scores should only be submitted once.
An application will be forwarded to the second-choice program only after the first-choice program has completed its review and rendered its decision. An application file will not be reviewed concurrently by both programs.
Programs may stop considering second-choice applications at any time during the season; Graduate School of Arts and Sciences cannot guarantee that your application will receive a second review.
What is the mailing address for your PhD admission office? Students are encouraged to apply online . Please note: Materials should not be mailed to the Graduate School of Arts and Sciences unless specifically requested by the Office of Admissions. Unofficial transcripts and other supplemental application materials should be uploaded through the online application system. Graduate School of Arts and Sciences Office of Admissions Columbia University 107 Low Library, MC 4303 535 West 116th Street New York, NY 10027
How many years does it take to pursue a PhD degree in your program? Our students usually graduate in 4‐6 years.
Can the PhD be pursued part-time? No, all of our students are full-time students. We do not offer a part-time option.
One of the requirements is to have knowledge of linear algebra (through the level of MATH V2020 at Columbia) and advanced calculus (through the level of MATH V1201). I studied these topics; how do I know if I meet the knowledge content requirement? We interview our top candidates and based on the information on your transcripts and your grades, if we are not sure about what you covered in your courses we will ask you during the interview.
Can I contact faculty members to learn more about their research and hopefully gain their support? Yes, you are more than welcome to contact faculty members and discuss your research interests with them. However, please note that all the applications are processed by a central admission committee, and individual faculty members cannot and will not guarantee admission to our program.
How do I find out which professors are taking on new students to mentor this year? Applications are evaluated through a central admissions committee. Openings in individual faculty groups are not considered during the admissions process. Therefore, we suggest contacting the faculty members you would like to work with and asking if they are planning to take on new students.

For more information please contact us at [email protected] .

For more information please contact us at [email protected]

Upcoming Events

PhD Program information

The Statistics PhD program is rigorous, yet welcoming to students with interdisciplinary interests and different levels of preparation. Students in the PhD program take core courses on the theory and application of probability and statistics during their first year. The second year typically includes additional course work and a transition to research leading to a dissertation. PhD thesis topics are diverse and varied, reflecting the scope of faculty research interests. Many students are involved in interdisciplinary research. Students may also have the option to pursue a designated emphasis (DE) which is an interdisciplinary specialization: Designated Emphasis in Computational and Genomic Biology , Designated Emphasis in Computational Precision Health , Designated Emphasis in Computational and Data Science and Engineering . The program requires four semesters of residence.

Normal progress entails:

Year 1 . Perform satisfactorily in preliminary coursework. In the summer, students are required to embark on a short-term research project, internship, graduate student instructorship, reading course, or on another research activity. Years 2-3 . Continue coursework. Find a thesis advisor and an area for the oral qualifying exam. Formally choose a chair for qualifying exam committee, who will also serve as faculty mentor separate from the thesis advisor. Pass the oral qualifying exam and advance to candidacy by the end of Year 3. Present research at BSTARS each year. Years 4-5 . Finish the thesis and give a lecture based on it in a department seminar.

Program Requirements

Qualifying Exam

Course work and evaluation

Preliminary stage: the first year.

Effective Fall 2019, students are expected to take four semester-long courses for a letter grade during their first year which should be selected from the core first-year PhD courses offered in the department: Probability (204/205A, 205B,), Theoretical Statistics (210A, 210B), and Applied Statistics (215A, 215B). These requirements can be altered by a member of the PhD Program Committee (in consultation with the faculty mentor and by submitting a graduate student petition ) in the following cases:

Students primarily focused on probability will be allowed to substitute one semester of the four required semester-long courses with an appropriate course from outside the department.
Students may request to postpone one semester of the core PhD courses and complete it in the second year, in which case they must take a relevant graduate course in their first year in its place. In all cases, students must complete the first year requirements in their second year as well as maintain the overall expectations of second year coursework, described below. Some examples in which such a request might be approved are described in the course guidance below.
Students arriving with advanced standing, having completed equivalent coursework at another institution prior to joining the program, may be allowed to take other relevant graduate courses at UC Berkeley to satisfy some or all of the first year requirements

Requirements on course work beyond the first year

Students entering the program before 2022 are required to take five additional graduate courses beyond the four required in the first year, resulting in a total of nine graduate courses required for completion of their PhD. In their second year, students are required to take three graduate courses, at least two of them from the department offerings, and in their third year, they are required to take at least two graduate courses. Students are allowed to change the timing of these five courses with approval of their faculty mentor. Of the nine required graduate courses, students are required to take for credit a total of 24 semester hours of courses offered by the Statistics department numbered 204-272 inclusive. The Head Graduate Advisor (in consultation with the faculty mentor and after submission of a graduate student petition) may consent to substitute courses at a comparable level in other disciplines for some of these departmental graduate courses. In addition, the HGA may waive part of this unit requirement.

Starting with the cohort entering in the 2022-23 academic year , students are required to take at least three additional graduate courses beyond the four required in the first year, resulting in a total of seven graduate courses required for completion of their PhD. Of the seven required graduate courses, five of these courses must be from courses offered by the Statistics department and numbered 204-272, inclusive. With these reduced requirements, there is an expectation of very few waivers from the HGA. We emphasize that these are minimum requirements, and we expect that students will take additional classes of interest, for example on a S/U basis, to further their breadth of knowledge.

For courses to count toward the coursework requirements students must receive at least a B+ in the course (courses taken S/U do not count, except for STAT 272 which is only offered S/U). Courses that are research credits, directed study, reading groups, or departmental seminars do not satisfy coursework requirements (for courses offered by the Statistics department the course should be numbered 204-272 to satisfy the requirements). Upper-division undergraduate courses in other departments can be counted toward course requirements with the permission of the Head Graduate Advisor. This will normally only be approved if the courses provide necessary breadth in an application area relevant to the student’s thesis research.

First year course work: For the purposes of satisfactory progression in the first year, grades in the core PhD courses are evaluated as: A+: Excellent performance in PhD program A: Good performance in PhD program A-: Satisfactory performance B+: Performance marginal, needs improvement B: Unsatisfactory performance

First year and beyond: At the end of each year, students must meet with his or her faculty mentor to review their progress and assess whether the student is meeting expected milestones. The result of this meeting should be the completion of the student’s annual review form, signed by the mentor ( available here ). If the student has a thesis advisor, the thesis advisor must also sign the annual review form.

Guidance on choosing course work

Choice of courses in the first year: Students enrolling in the fall of 2019 or later are required to take four semesters of the core PhD courses, at least three of which must be taken in their first year. Students have two options for how to schedule their four core courses:

Option 1 -- Complete Four Core Courses in 1st year: In this option, students would take four core courses in the first year, usually finishing the complete sequence of two of the three sequences. Students following this option who are primarily interested in statistics would normally take the 210A,B sequence (Theoretical Statistics) and then one of the 205A,B sequence (Probability) or the 215A,B sequence (Applied Statistics), based on their interests, though students are allowed to mix and match, where feasible. Students who opt for taking the full 210AB sequence in the first year should be aware that 210B requires some graduate-level probability concepts that are normally introduced in 205A (or 204).
Option 2 -- Postponement of one semester of a core course to the second year: In this option, students would take three of the core courses in the first year plus another graduate course, and take the remaining core course in their second year. An example would be a student who wanted to take courses in each of the three sequences. Such a student could take the full year of one sequence and the first semester of another sequence in the first year, and the first semester of the last sequence in the second year (e.g. 210A, 215AB in the first year, and then 204 or 205A in the second year). This would also be a good option for students who would prefer to take 210A and 215A in their first semester but are concerned about their preparation for 210B in the spring semester. Similarly, a student with strong interests in another discipline, might postpone one of the spring core PhD courses to the second year in order to take a course in that discipline in the first year. Students who are less mathematically prepared might also be allowed to take the upper division (under-graduate) courses Math 104 and/or 105 in their first year in preparation for 205A and/or 210B in their second year. Students who wish to take this option should consult with their faculty mentor, and then must submit a graduate student petition to the PhD Committee to request permission for postponement. Such postponement requests will be generally approved for only one course. At all times, students must take four approved graduate courses for a letter grade in their first year.

After the first year: Students with interests primarily in statistics are expected to take at least one semester of each of the core PhD sequences during their studies. Therefore at least one semester (if not both semesters) of the remaining core sequence would normally be completed during the second year. The remaining curriculum for the second and third years would be filled out with further graduate courses in Statistics and with courses from other departments. Students are expected to acquire some experience and proficiency in computing. Students are also expected to attend at least one departmental seminar per week. The precise program of study will be decided in consultation with the student’s faculty mentor.

Remark. Stat 204 is a graduate level probability course that is an alternative to 205AB series that covers probability concepts most commonly found in the applications of probability. It is not taught all years, but does fulfill the requirements of the first year core PhD courses. Students taking Stat 204, who wish to continue in Stat 205B, can do so (after obtaining the approval of the 205B instructor), by taking an intensive one month reading course over winter break.

Designated Emphasis: Students with a Designated Emphasis in Computational and Genomic Biology or Designated Emphasis in Computational and Data Science and Engineering should, like other statistics students, acquire a firm foundation in statistics and probability, with a program of study similar to those above. These programs have additional requirements as well. Interested students should consult with the graduate advisor of these programs.

Starting in the Fall of 2019, PhD students are required in their first year to take four semesters of the core PhD courses. Students intending to specialize in Probability, however, have the option to substitute an advanced mathematics class for one of these four courses. Such students will thus be required to take Stat 205A/B in the first year, at least one of Stat 210A/B or Stat 215A/B in the first year, in addition to an advanced mathematics course. This substitute course will be selected in consultation with their faculty mentor, with some possible courses suggested below. Students arriving with advanced coursework equivalent to that of 205AB can obtain permission to substitute in other advanced probability and mathematics coursework during their first year, and should consult with the PhD committee for such a waiver.

During their second and third years, students with a probability focus are expected to take advanced probability courses (e.g., Stat 206 and Stat 260) to fulfill the coursework requirements that follow the first year. Students are also expected to attend at least one departmental seminar per week, usually the probability seminar. If they are not sufficiently familiar with measure theory and functional analysis, then they should take one or both of Math 202A and Math 202B. Other recommended courses from the department of Mathematics or EECS include:

Math 204, 222 (ODE, PDE) Math 205 (Complex Analysis) Math 258 (Classical harmonic analysis) EE 229 (Information Theory and Coding) CS 271 (Randomness and computation)

The Qualifying Examination

The oral qualifying examination is meant to determine whether the student is ready to enter the research phase of graduate studies. It consists of a 50-minute lecture by the student on a topic selected jointly by the student and the thesis advisor. The examination committee consists of at least four faculty members to be approved by the department. At least two members of the committee must consist of faculty from the Statistics and must be members of the Academic Senate. The chair must be a member of the student’s degree-granting program.

Qualifying Exam Chair. For qualifying exam committees formed in the Fall of 2019 or later, the qualifying exam chair will also serve as the student’s departmental mentor, unless a student already has two thesis advisors. The student must select a qualifying exam chair and obtain their agreement to serve as their qualifying exam chair and faculty mentor. The student's prospective thesis advisor cannot chair the examination committee. Selection of the chair can be done well in advance of the qualifying exam and the rest of the qualifying committee, and because the qualifying exam chair also serves as the student’s departmental mentor (unless the student has co-advisors), the chair is expected to be selected by the beginning of the third year or at the beginning of the semester of the qualifying exam, whichever comes earlier. For more details regarding the selection of the Qualifying Exam Chair, see the "Mentoring" tab.

Paperwork and Application. Students at the point of taking a qualifying exam are assumed to have already found a thesis advisor and to should have already submitted the internal departmental form to the Graduate Student Services Advisor ( found here ). Selection of a qualifying exam chair requires that the faculty member formally agree by signing the internal department form ( found here ) and the student must submit this form to the Graduate Student Services Advisor. In order to apply to take the exam, the student must submit the Application for the Qualifying Exam via CalCentral at least three weeks prior to the exam. If the student passes the exam, they can then officially advance to candidacy for the Ph.D. If the student fails the exam, the committee may vote to allow a second attempt. Regulations of the Graduate Division permit at most two attempts to pass the oral qualifying exam. After passing the exam, the student must submit the Application for Candidacy via CalCentral .

The Doctoral Thesis

The Ph.D. degree is granted upon completion of an original thesis acceptable to a committee of at least three faculty members. The majority or at least half of the committee must consist of faculty from Statistics and must be members of the Academic Senate. The thesis should be presented at an appropriate seminar in the department prior to filing with the Dean of the Graduate Division. See Alumni if you would like to view thesis titles of former PhD Students.

Graduate Division offers various resources, including a workshop, on how to write a thesis, from beginning to end. Requirements for the format of the thesis are rather strict. For workshop dates and guidelines for submitting a dissertation, visit the Graduate Division website.

Students who have advanced from candidacy (i.e. have taken their qualifying exam and submitted the advancement to candidacy application) must have a joint meeting with their QE chair and their PhD advisor to discuss their thesis progression; if students are co-advised, this should be a joint meeting with their co-advisors. This annual review is required by Graduate Division. For more information regarding this requirement, please see https://grad.berkeley.edu/ policy/degrees-policy/#f35- annual-review-of-doctoral- candidates .

Teaching Requirement

For students enrolled in the graduate program before Fall 2016, students are required to serve as a Graduate Student Instructor (GSI) for a minimum of 20 hours (equivalent to a 50% GSI appointment) during a regular academic semester by the end of their third year in the program.

Effective with the Fall 2016 entering class, students are required to serve as a GSI for a minimum of two 50% GSI appointment during the regular academic semesters prior to graduation (20 hours a week is equivalent to a 50% GSI appointment for a semester) for Statistics courses numbered 150 and above. Exceptions to this policy are routinely made by the department.

Each spring, the department hosts an annual conference called BSTARS . Both students and industry alliance partners present research in the form of posters and lightning talks. All students in their second year and beyond are required to present a poster at BSTARS each year. This requirement is intended to acclimate students to presenting their research and allow the department generally to see the fruits of their research. It is also an opportunity for less advanced students to see examples of research of more senior students. However, any students who do not yet have research to present can be exempted at the request of their thesis advisor (or their faculty mentors if an advisor has not yet been determined).

Mentoring for PhD Students

Initial Mentoring: PhD students will be assigned a faculty mentor in the summer before their first year. This faculty mentor at this stage is not expected to be the student’s PhD advisor nor even have research interests that closely align with the student. The job of this faculty mentor is primarily to advise the student on how to find a thesis advisor and in selecting appropriate courses, as well as other degree-related topics such as applying for fellowships. Students should meet with their faculty mentors twice a semester. This faculty member will be the designated faculty mentor for the student during roughly their first two years, at which point students will find a qualifying exam chair who will take over the role of mentoring the student.

Research-focused mentoring : Once students have found a thesis advisor, that person will naturally be the faculty member most directly overseeing the student’s progression. However, students will also choose an additional faculty member to serve as a the chair of their qualifying exam and who will also serve as a faculty mentor for the student and as a member of his/her thesis committee. (For students who have two thesis advisors, however, there is not an additional faculty mentor, and the quals chair does NOT serve as the faculty mentor).

The student will be responsible for identifying and asking a faculty member to be the chair of his/her quals committee. Students should determine their qualifying exam chair either at the beginning of the semester of the qualifying exam or in the fall semester of the third year, whichever is earlier. Students are expected to have narrowed in on a thesis advisor and research topic by the fall semester of their third year (and may have already taken qualifying exams), but in the case where this has not happened, such students should find a quals chair as soon as feasible afterward to serve as faculty mentor.

Students are required to meet with their QE chair once a semester during the academic year. In the fall, this meeting will generally be just a meeting with the student and the QE chair, but in the spring it must be a joint meeting with the student, the QE chair, and the PhD advisor. If students are co-advised, this should be a joint meeting with their co-advisors.

If there is a need for a substitute faculty mentor (e.g. existing faculty mentor is on sabbatical or there has been a significant shift in research direction), the student should bring this to the attention of the PhD Committee for assistance.

PhD Student Forms:

Important milestones: .

Each of these milestones is not complete until you have filled out the requisite form and submitted it to the GSAO. If you are not meeting these milestones by the below deadline, you need to meet with the Head Graduate Advisor to ask for an extension. Otherwise, you will be in danger of not being in good academic standing and being ineligible for continued funding (including GSI or GSR appointments, and many fellowships).

†Students who are considering a co-advisor, should have at least one advisor formally identified by the end of the second year; the co-advisor should be identified by the end of the fall semester of the 3rd year in lieu of finding a Research Mentor/QE Chair.

Expected Progress Reviews:

* These meetings do not need to be held in the semester that you take your Qualifying Exam, since the relevant people should be members of your exam committee and will discuss your research progress during your qualifying exam

** If you are being co-advised by someone who is not your primary advisor because your primary advisor cannot be your sole advisor, you should be meeting with that person like a research mentor, if not more frequently, to keep them apprised of your progress. However, if both of your co-advisors are leading your research (perhaps independently) and meeting with you frequently throughout the semester, you do not need to give a fall research progress report.

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Department of Statistics

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PhD Program

The department encourages research in both theoretical and applied statistics. Faculty members of the department have been leaders in research on a multitude of topics that include statistical inference, statistical computing and Monte-Carlo methods, analysis of missing data, causal inference, stochastic processes, multilevel models, experimental design, network models and the interface of statistics and the social, physical, and biological sciences. A unique feature of the department lies in the fact that apart from methodological research, all the faculty members are also heavily involved in applied research, developing novel methodology that can be applied to a wide array of fields like astrophysics, biology, chemistry, economics, engineering, public policy, sociology, education and many others.

Two carefully designed special courses offered to Ph.D. students form a unique feature of our program. Among these, Stat 303 equips students with the basic skills necessary to teach statistics , as well as to be better overall statistics communicators. Stat 399 equips them with generic skills necessary for problem solving abilities.

Our Ph.D. students often receive substantial guidance from several faculty members, not just from their primary advisors, and in several settings. For example, every Ph.D. candidate who passes the qualifying exam gives a 30 minute presentation each semester (in Stat 300 ), in which the faculty ask questions and make comments. The Department recently introduced an award for Best Post-Qualifying Talk (up to two per semester), to further encourage and reward inspired research and presentations.

PhD Program Requirements

PhD Program Admissions Process

PhD Admissions FAQ
AM in Statistics
Stat 300: Research in Statistics
Stat 303: The Art and Practice of Teaching Statistics

Need Assistance?

Cornell University does not offer a separate Masters of Science (MS) degree program in the field of Statistics. Applicants interested in obtaining a masters-level degree in statistics should consider applying to Cornell's MPS Program in Applied Statistics.

Choosing a Field of Study

There are many graduate fields of study at Cornell University. The best choice of graduate field in which to pursue a degree depends on your major interests. Statistics is a subject that lies at the interface of theory, applications, and computing. Statisticians must therefore possess a broad spectrum of skills, including expertise in statistical theory, study design, data analysis, probability, computing, and mathematics. Statisticians must also be expert communicators, with the ability to formulate complex research questions in appropriate statistical terms, explain statistical concepts and methods to their collaborators, and assist them in properly communicating their results. If the study of statistics is your major interest then you should seriously consider applying to the Field of Statistics.

There are also several related fields that may fit even better with your interests and career goals. For example, if you are mainly interested in mathematics and computation as they relate to modeling genetics and other biological processes (e.g, protein structure and function, computational neuroscience, biomechanics, population genetics, high throughput genetic scanning), you might consider the Field of Computational Biology . You may wish to consider applying to the Field of Electrical and Computer Engineering if you are interested in the applications of probability and statistics to signal processing, data compression, information theory, and image processing. Those with a background in the social sciences might wish to consider the Field of Industrial and Labor Relations with a major or minor in the subject of Economic and Social Statistics. Strong interest and training in mathematics or probability might lead you to choose the Field of Mathematics . Lastly, if you have a strong mathematics background and an interest in general problem-solving techniques (e.g., optimization and simulation) or applied stochastic processes (e.g., mathematical finance, queuing theory, traffic theory, and inventory theory) you should consider the Field of Operations Research .

Residency Requirements

Students admitted to PhD program must be "in residence" for at least four semesters, although it is generally expected that a PhD will require between 8 and 10 semesters to complete. The chair of your Special Committee awards one residence unit after the satisfactory completion of each semester of full-time study. Fractional units may be awarded for unsatisfactory progress.

Your Advisor and Special Committee

The Director of Graduate Studies is in charge of general issues pertaining to graduate students in the field of Statistics. Upon arrival, a temporary Special Committee is also declared for you, consisting of the Director of Graduate Studies (chair) and two other faculty members in the field of Statistics. This temporary committee shall remain in place until you form your own Special Committee for the purposes of writing your doctoral dissertation. The chair of your Special Committee serves as your primary academic advisor; however, you should always feel free to contact and/or chat with any of the graduate faculty in the field of Statistics.

The formation of a Special Committee for your dissertation research should serve your objective of writing the best possible dissertation. The Graduate School requires that this committee contain at least three members that simultaneously represent a certain combination of subjects and concentrations. The chair of the committee is your principal dissertation advisor and always represents a specified concentration within the subject & field of Statistics. The Graduate School additionally requires PhD students to have at least two minor subjects represented on your special committee. For students in the field of Statistics, these remaining two members must either represent (i) a second concentration within the subject of Statistics, and one external minor subject; or, (ii) two external minor subjects. Each minor advisor must agree to serve on your special committee; as a result, the identification of these minor members should occur at least 6 months prior to your A examination.

Some examples of external minors include Computational Biology, Demography, Computer Science, Economics, Epidemiology, Mathematics, Applied Mathematics and Operations Research. The declaration of an external minor entails selecting (i) a field other than Statistics in which to minor; (ii) a subject & concentration within the specified field; and, (iii) a minor advisor representing this field/subject/concentration that will work with you in setting the minor requirements. Typically, external minors involve gaining knowledge in 3-5 graduate courses in the specified field/subject, though expectations can vary by field and even by the choice of advisor. While any choice of external minor subject is technically acceptable, the requirement that the minor representative serve on your Special Committee strongly suggests that the ideal choice(s) should share some natural connection with your choice of dissertation topic.

The fields, subjects and concentrations represented on your committee must be officially recognized by the Graduate School ; the Degrees, Subjects & Concentrations tab listed under each field of study provides this information. Information on the concentrations available for committee members chosen to represent the subject of Statistics can be found on the Graduate School webpage .

Statistics PhD Travel Support

The Department of Statistics and Data Science has established a fund for professional travel for graduate students. The intent of the Department is to encourage travel that enhances the Statistics community at Cornell by providing funding for graduate students in statistics that will be presenting at conferences. Please review the Graduate Student Travel Award Policy website for more information.

Completion of the PhD Degree

In addition to the specified residency requirements, students must meet all program requirements as outlined in Program Course Requirements and Timetables and Evaluations and Examinations, as well as complete a doctoral dissertation approved by your Special Committee. The target time to PhD completion is between 4 and 5 years; the actual time to completion varies by student.

Students should consult both the Guide to Graduate Study and Code of Legislation of the Graduate Faculty (available at www.gradschool.cornell.edu ) for further information on all academic and procedural matters pertinent to pursuing a graduate degree at Cornell University.

DEPARTMENT OF STATISTICS AND DATA SCIENCE

Phd program, phd program overview.

The doctoral program in Statistics and Data Science is designed to provide students with comprehensive training in theory and methodology in statistics and data science, and their applications to problems in a wide range of fields. The program is flexible and may be arranged to reflect students' interests and career goals. Cross-disciplinary work is encouraged. The PhD program prepares students for careers as university teachers and researchers and as research statisticians or data scientists in industry, government and the non-profit sector.

Requirements

Students are required to fulfill the Department requirements in addition to those specified by The Graduate School (TGS).

From the Graduate School’s webpage outlining the general requirements for a PhD :

In order to receive a doctoral degree, students must:

Complete all required coursework. .
Gain admittance to candidacy.
Submit a prospectus to be approved by a faculty committee.
Present a dissertation with original research. Review the Dissertation Publication page for more information.
Complete the necessary teaching requirement
Submit necessary forms to file for graduation
Complete degree requirements within the approved timeline

PhD degrees must be approved by the student's academic program. Consult with your program directly regarding specific degree requirements.

The Department requires that students in the Statistics and Data Science PhD program:

Meet the department minimum residency requirement of 2 years
STAT 344-0 Statistical Computing
STAT 350-0 Regression Analysis
STAT 353-0 Advanced Regression (new 2021-22)
STAT 415-0 I ntroduction to Machine Learning
STAT 420-1,2,3 Introduction to Statistical Theory and Methodology 1, 2, 3
STAT 430-1, STAT 430-2, STAT 440 (new courses in 2022-23 on probability and stochastic processes for statistics students)
STAT 457-0 Applied Bayesian Inference

Students generally complete the required coursework during their first two years in the PhD program. *note that required courses changed in the 2021-22 academic year, previous required courses can be found at the end of this page.

Pass the Qualifying Exam. This comprehensive examination covers basic topics in statistics and is typically taken in fall quarter of the second year.

Pass the Prospectus presentation/examination and be admitted for PhD candidacy by the end of year 3 . The statistics department requires that students must complete their Prospectus (proposal of dissertation topic) before the end of year 3, which is earlier than The Graduate School deadline of the end of year 4. The prospectus must be approved by a faculty committee comprised of a committee chair and a minimum of 2 other faculty members. Students usually first find an adviser through independent studies who will then typically serve as the committee chair. When necessary, exceptions may be made upon the approval of the committee chair and the director of graduate studies, to extend the due date of the prospectus exam until the end of year 4.

Successfully complete and defend a doctoral dissertation. After the prospectus is approved, students begin work on the doctoral dissertation, which must demonstrate an original contribution to a chosen area of specialization. A final examination (thesis defense) is given based on the dissertation. Students typically complete the PhD program in 5 years.
Attend all seminars in the department and participate in other research activities . In addition to these academic requirements, students are expected to participate in other research activities and attend all department seminars every year they are in the program.

Optional MS degree en route to PhD

Students admitted to the Statistics and Data Science PhD program can obtain an optional MS (Master of Science) degree en route to their PhD. The MS degree requires 12 courses: STAT 350-0 Regression Analysis, STAT 353 Advanced Regression, STAT 420-1,2,3 Introduction to Statistical Theory and Methodology 1, 2, 3, STAT 415-0 I ntroduction to Machine Learning , and at least 6 more courses approved by the department of which two must be 400 level STAT elective courses, no more than 3 can be non-STAT courses. For the optional MS degree, students must also pass the qualifying exam offered at the beginning of the second year at the MS level.

*Prior to 2021-2022, the course requirements for the PhD were:

STAT 351-0 Design and Analysis of Experiments
STAT 425 Sampling Theory and Applications
MATH 450-1,2 Probability 1, 2 or MATH 450-1 Probability 1 and IEMS 460-1,2 Stochastic Processes 1, 2
Six additional 300/400 graduate-level Statistics courses, at least two must be 400 -level

Doctoral Program

Program summary.

Students are required to

master the material in the prerequisite courses ;
pass the first-year core program;
attempt all three parts of the qualifying examinations and show acceptable performance in at least two of them (end of 1st year);
satisfy the depth and breadth requirements (2nd/3rd/4th year);
successfully complete the thesis proposal meeting (winter quarter of the 3rd year);
present a draft of their dissertation and pass the university oral examination (4th/5th year).

The PhD requires a minimum of 135 units. Students are required to take a minimum of nine units of advanced topics courses (for depth) offered by the department (not including literature, research, consulting or Year 1 coursework), and a minimum of nine units outside of the Statistics Department (for breadth). Courses for the depth and breadth requirements must equal a combined minimum of 24 units. In addition, students must enroll in STATS 390 Statistical Consulting, taking it at least twice.

All students who have passed the qualifying exams but have not yet passed the Thesis Proposal Meeting must take STATS 319 at least once each year. For example, a student taking the qualifying exams in the summer after Year 1 and having the dissertation proposal meeting in Year 3, would take 319 in Years 2 and 3. Students in their second year are strongly encouraged to take STATS 399 with at least one faculty member. All details of program requirements can be found in our PhD handbook (available to Stanford affiliates only, using Stanford authentication. Requests for access from non-affiliates will not be approved).

Statistics Department PhD Handbook

All students are expected to abide by the Honor Code and the Fundamental Standard .

Doctoral and Research Advisors

During the first two years of the program, students' academic progress is monitored by the department's Graduate Director. Each student should meet at least once a quarter with the Graduate Director to discuss their academic plans and their progress towards choosing a thesis advisor (before the final study list deadline of spring of the second year). From the third year onward students are advised by their selected advisor.

Qualifying Examinations

Qualifying examinations are part of most PhD programs in the United States. At Stanford these exams are intended to test the student's level of knowledge when the first-year program, common to all students, has been completed. There are separate examinations in the three core subjects of statistical theory and methods, applied statistics, and probability theory, which are typically taken during the summer at the end of the student's first year. Students are expected to attempt all three examinations and show acceptable performance in at least two of them. Letter grades are not given. Qualifying exams may be taken only once. After passing the qualifying exams, students must file for Ph.D. Candidacy, a university milestone, by the end of spring quarter of their second year.

While nearly all students pass the qualifying examinations, those who do not can arrange to have their financial support continued for up to three quarters while alternative plans are made. Usually students are able to complete the requirements for the M.S. degree in Statistics in two years or less, whether or not they have passed the PhD qualifying exams.

Thesis Proposal Meeting and Dissertation Reading Committee

The thesis proposal meeting is intended to demonstrate a student's depth in some areas of statistics, and to examine the general plan for their research. In the meeting the student gives a 60-minute presentation involving ideas developed to date and plans for completing a PhD dissertation, and for another 60 minutes answers questions posed by the committee. which consists of their advisor and two other members. The meeting must be successfully completed by the end of winter quarter of the third year. If a student does not pass, the exam must be repeated. Repeated failure can lead to a loss of financial support.

The Dissertation Reading Committee consists of the student’s advisor plus two faculty readers, all of whom are responsible for reading the full dissertation. Of these three, at least two must be members of the Statistics Department (faculty with a full or joint appointment in Statistics but excluding for this purpose those with only a courtesy or adjunct appointment). Normally, all committee members are members of the Stanford University Academic Council or are emeritus Academic Council members; the principal dissertation advisor must be an Academic Council member.

The Doctoral Dissertation Reading Committee form should be completed and signed at the Dissertation Proposal Meeting. The form must be submitted before approval of TGR status or before scheduling a University Oral Examination.

For further information on the Dissertation Reading Committee, please see the Graduate Academic Policies and Procedures (GAP) Handbook section 4.8.

University Oral Examinations

The oral examination consists of a public, approximately 60-minute, presentation on the thesis topic, followed by a 60 minute question and answer period attended only by members of the examining committee. The questions relate to the student's presentation and also explore the student's familiarity with broader statistical topics related to the thesis research. The oral examination is normally completed during the last few months of the student's PhD period. The examining committee typically consists of four faculty members from the Statistics Department and a fifth faculty member from outside the department serving as the committee chair. Four out of five passing votes are required and no grades are given. Nearly all students can expect to pass this examination, although it is common for specific recommendations to be made regarding completion of the thesis.

The Dissertation Reading Committee must also read and approve the thesis.

For further information on university oral examinations and committees, please see the Graduate Academic Policies and Procedures (GAP) Handbook section 4.7 .

Dissertation

The dissertation is the capstone of the PhD degree. It is expected to be an original piece of work of publishable quality. The research advisor and two additional faculty members constitute the student's dissertation reading committee.

PhD in Statistics

The PhD in Statistics prepares students for a career pursuing research in either academia or industry. The program provides rigorous classroom training in the theory, methodology, and application of statistics, and provides the opportunity to work with faculty on advanced research topics over a wide range of theory and application areas.

Degree Requirements

The requirements for obtaining an PhD in Statistics can be found on the associated page of the BU Bulletin .

Courses : All Ph.D. students in the statistics track must take the following two-semester sequences: MA779 and MA780 (Probability Theory I and II), MA781 (Estimation Theory) and MA782 (Hypothesis Testing), and MA750 and MA751 (Advanced Statistical Methods I and II). In addition to the required courses listed on the BU Bulletin page, the remaining coursework can be chosen from the graduate courses we offer here . Students can also request to use courses from other departments to satisfy some of these requirements. Please contact your advisor for more information about which courses can be used in this way. All courses must be passed with a grade of B- or higher.
PhD Exam in Probability: This exam covers the material covered in MA779 and MA780 (Probability Theory I and II).
PhD Exam in Mathematical Statistics: This exam covers material covered in MA781 (Estimation Theory) and MA782 (Hypothesis Testing).
PhD Exam in Applied Statistics: This exam covers the same material as the M.A. Applied comprehensive exam (see the MA degree requirements ) and is offered at the same time, except that in order to pass it at the PhD level a student must correctly solve all four problems.
Dissertation and Final Oral Examination: This follows the GRS General Requirements for the Doctor of Philosophy Degree .

Admissions information can be found on the BU Arts and Sciences PhD Admissions website .

Financial Aid

Our department funds our PhD students through a combination of University fellowships, teaching fellowships, and faculty research grants. More information will be provided to admitted students.

More Information

Please reach out to us directly at [email protected] if you have further questions.

Doing a PhD in Statistics

We live in a data-rich world. The study of statistics allows us to better understand data, measure uncertainty, and calculate risk. The applications of such knowledge are widespread – from economics to medicine. A PhD in Statistics will give you a deep understanding of the mathematical framework which underpins data analysis as we know it. Read on to find out the key information about a PhD in statistics, and whether it is worth it for you.

What Does a PhD in Statistics Focus On?

A Statistics PhD programme can focus on:

Statistical theory and statistical methods
Bayesian statistics
Covariance modelling
High dimensional data
Probability theory
Causal inference
Extreme value theory
Non-parametric regression
Symbolic computation
Applied statistics

The list above is only a small sample of the many different areas within probability and statistics. Many PhD research projects place a particular emphasis on statistics within environmental, biomedical, and social science. Aside from this there is also overlap with other field such as computer science, applied mathematics, and linear algebra.

Browse PhDs in Statistics

Application of artificial intelligence to multiphysics problems in materials design, study of the human-vehicle interactions by a high-end dynamic driving simulator, physical layer algorithm design in 6g non-terrestrial communications, machine learning for autonomous robot exploration, detecting subtle but clinically significant cognitive change in an ageing population, entry requirements for a phd in statistics.

Most Statistics PhD programmes require applicants to have, or expect to obtain, a bachelor’s degree (or international equivalent) in Mathematics or Statistics. However, many Statistics PhD research projects also accept applications from graduate students with a bachelor’s degree in other subjects if they involve a significant mathematical component (such as Data Science , Physics, or Computer Science). Many universities expect first class honours due to the high competition for places, though for some institutions second class honours (2:1) is adequate.

It is also common for universities to accept second class honours (2:1), if the graduate has a master’s degree or relevant work experience.

Universities typically expect international students to provide evidence of their English Language ability. This is usually benchmarked by a IELTS score of 6.5 (with a minimum score of 6 in each component), a TOEFL (iBT) score 92, a CAE and CPE score of 176 or another equivalent. The exact score requirements may differ across different universities.

Duration and Programme Types

The typical doctoral programme in Statistics takes 3-4 years full-time, or 6 years part-time.

A PhD research project in Statistics can focus on a particular application of statistics. For example, you may undertake a PhD in statistical genomics or biostatistics, which would involve interdisciplinary work and additional training modules to understand how statistics can improve biological and genetic study.

In addition to the statistics course modules, you will likely undertake ‘ transferable skills ‘ training in communication, management, and commerce – all of which are skills a good postgraduate research student needs.

As with most PhDs, you will have to complete a dissertation at the end of your postgraduate research project, and undertake an oral examination known as the viva , where you are required to defend your dissertation to a supervisory committee/dissertation committee usually made up of two examiners.

Costs and Funding

Annual tuition fees for PhDs in Statistics are typically around £4,000 to £5,000 for UK/EU students. Tuition fees for international students are usually much higher, typically around £20,000 – £25,000 per academic year. Tuition fees for part time programmes are typically scaled down according to the programme length.

Some Statistics PhD programmes also have additional costs to cover laboratory resources, administration and computational costs.

Together with EPSRC and other national funding sources, many Universities offer postgraduate studentships which cover the tuition fees for Statistical PhD programmes. EPSRC DTA research studentships are available in all areas for UK and EU students. Students who are normally resident in the EU but not in the UK are eligible for EPSRC PhD studentships, but the awards in such cases currently cover only the course fees, not maintenance stipends .

Available Career Paths in Statistics

One of the key advantages of Statistics is that it is a fundamental concept which underpins most industries. Consequently, there are an abundance of career paths available for Statistics PhD doctorates such as agriculture, forensics, machine learning, informatics, geosciences, law and biomathematics.

Examples of common destinations for a Statistics PhD student include:

Actuarial Science – Actuaries are responsible for analysing data to help non-specialists make informed decisions about risks. A good understanding of probability and investment is crucial in this field. Salaries for Statistics PhD students in this field vary, but with around 10 years’ experience typically are around £60,000.
Environmental statistician – In this role, Statistics doctorates use their knowledge to contribute to environmental study. This can include monitoring climate patterns, carrying out flood risk assessments, or transforming large amounts of temperature data into information for the public.
Data Analyst – Some people use their PhD in stats to become data analysts, responsible for data management, developing automated processes, tracking KPIs, and more. Data analysts can be found in various industries form logistics & transport to marketing. Again, with experience Statistics doctorates in this path can expect a lucrative salary.
Medical statistician – PhD graduates in the medical field aid health research in a number of ways, for example analysing data from clinical studies to identify patterns. The NHS, private health companies and the pharmaceutical industry are common employers for those with a PhD degree in statistics or applied statistics.
University lecturer – Often PhD students opt to stay in academia. This can be as a university lecturer where you will teach students about statistical theory.

Browse PhDs Now

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120 Statistical Research Topics: Explore Up-to-date Trends

Statistical Research Topics Latest Trends & Techniques

Researchers and statistics teachers are often tasked with writing an article or paper on a given stats project idea. One of the most crucial things in writing an outstanding and well-composed statistics research project, paper, or essay is to come up with a very interesting topic that will captivate your reader’s minds and provoke their thoughts.

What Are the Best Statistical Research Topics Worth Writing On?

Leading statistical research topics for college students that will interest you, project topics in statistics worth considering, the best idea for statistics project you can focus on, good experiments for statistics topics you should be writing on, what are the best ap statistics project ideas that will be of keen interest to you, good statistics project ideas suitable for our modern world, some of the most crucial survey topics for statistics project, statistical projects topics every researcher wants to write on, statistical research topics you can focus your research on.

Students often find it difficult to come up with well-composed statistical research project topics that take the format of argumentative essay topics to pass across their message. In this essay, we will look at some of the most interesting statistics research topics to focus your research on.

Here are some of the best statistical research topics worth writing on:

Predictive Healthcare Modeling with Machine Learning
Analyzing Online Education During COVID-19 Epidemic
Modeling How Climate Change Affects Natural Disasters
Essential Elements Influencing Personnel Productivity
Social Media Influence on Customer Choices and Behavior
Can Geographical Statistics Aid In Analyzing Crime Trends and Patterns?
Financial Markets and Stock Price Predictions
Statistical Analysis of Voting-related Behaviors
An Analysis of Public Transportation Usage Trends in Urban Areas
How Can Public Health Education Reduce Air Pollution?
Statistical Analysis of Suicide In Adolescents and Adults
A Review of Divorce and How It Affects Children

As a college student, here are the best statistical projects for high school students to focus your research on, especially if you need social media research topics .

Major Factors Influencing College Students’ Academic Performance
Social Media and How It Defines thee Mental Health of Students
Evaluation of the Elements Influencing Student Engagement and Retention
An Examination of Extracurricular Activities On Academic Success
Does Parental Involvement Determine Academic Achievement of Kids?
Examining How Technology Affects Improving Educational Performance
Factors That Motivate Students’ Involvement In Online Learning
The Impact of Socioeconomic Status On Academic Performance
Does Criticism Enhance Student Performance?
Student-Centered Learning and Improved Performance
A Cursory Look At Students’ Career Goals and Major Life Decisions
Does Mental Health Impact Academic Achievement?

Are you a student tasked with writing a project but can’t come up with befitting stats research topics? Here are the best ideas for statistical projects worth considering:

Financial Data And Stock Price Forecasting
Investigation of Variables Influencing Students’ Grades
What Causes Traffic Flow and Congestion In Urban Areas?
How to Guarantee Customer Retention In the Retail Sector
Using Epidemiological Data to Model the Spread of Infectious Diseases
Does Direct Advertisement Affect Consumer Preferences and Behavior?
How to Predict and Adapt to Climate Change
Using Spatial Statistics to Analyze Trends and Patterns In Crime
Examination of the Elements Influencing Workplace Morale and Productivity
Understanding User Behavior and Preferences Through Statistical Analysis of Social Media Data
How Many Percent Get Married After Their Degree Programs?
A Comparative Analysis of Different Academic Fee Payments

If you have been confused based on the availability of different statistics project topics to choose from, here are some of the best thesis statement about social media to choose from:

Analysis of the Variables Affecting A Startup’s Success
The Valid Connection Between Mental Health and Social Media Use
Different Teaching Strategies and Academic Performance
Factors Influencing Employee Satisfaction In Different Work Environments
The Impact of Public Policy On Different Population Groups
Reviewing Different Health Outcomes and Incomes
Different Marketing Tactics for Good Service Promotion
What Influences Results In Different Sports Competitions?
Differentiating Elements Affecting Students’ Performance In A Given Subject
Internal Communication and Building An Effective Workplace
Does the Use of Business Technologies Boost Workers’ Output?
The Role of Modern Communication In An Effective Company Management

Are you a student tasked with writing an essay on social issues research topics but having challenges coming up with a topic? Here are some amazing statistical experiments ideas you can center your research on.

How Global Pandemic Affects Local Businesses
Investigating the Link Between Income and Health Outcomes In a Demography
Key Motivators for Student’s Performance In a Particular Academic Program
Evaluating the Success of a Promotional Plan Over Others
Continuous Social Media Use and Impact On Mental Health
Does Culture Impact the Religious Beliefs of Certain Groups?
Key Indicators of War and How to Manage These Indicators
An Overview of War As a Money Laundering Scheme
How Implementations Guarantee Effectiveness of Laws In Rural Areas
Performance of Students In War-torn Areas
Key Indicators For Measuring the Success of Your Venture
How Providing FAQs Can Help a Business Scale

The best AP statistic project ideas every student especially those interested in research topics for STEM students will want to write in include:

The Most Affected Age Demography By the Covid-19 Pandemic
The Health Outcomes Peculiar to a Specific Demography
Unusual Ways to Enhance Student Performance In a Classroom
How Marketing Efforts Can Determine Promotional Outputs
Can Mental Health Solutions Be Provided On Social Media?
Assessing How Certain Species Are Affected By Climate Change.
What Influences Voter Turnouts In Different Elections?
How Many People Have Used Physical Exercises to Improve Mental Health
How Financial Circumstances Can Determine Criminal Activities
Ways DUI Laws Can Reduce Road Accidents
Examining the Connection Between Corruption and Underdevelopment In Africa
What Key Elements Do Top Global Firms Engage for Success?

If you need some of the best economics research paper topics , here are the best statistics experiment ideas you can write research on:

Retail Client Behaviors and Weather Trends
The Impact of Marketing Initiatives On Sales and Customer Retention
How Socioeconomic Factors Determine Crime Rates In Different Locations
Public and Private School Students: Who Performs Better?
How Fitness Affects the Mental Health of People In Different Ages
Focus On the Unbanked Employees Globally
Does Getting Involve In a Kid’s Life Make Them Better?
Dietary Decisions and a Healthy Life
Managing Diabetes and High Blood Pressure of a Specific Group
How to Engage Different Learning Methods for Effectiveness
Understudying the Sleeping Habits of Specific Age Groups
How the Numbers Can Help You Create a Brand Recognition

As a student who needs fresh ideas relating to the topic for a statistics project to write on, here are crucial survey topics for statistics that will interest you.

Understanding Consumer Spending and Behavior In Different Regions
Why Some People in Certain Areas Live Longer than Others
Comparative Analysis of Different Customer Behaviors
Do Social Media Businesses Benefit More than Physical Businesses?
Does a Healthy Work Environment Guarantee Productivity?
The Impact of Ethnicity and Religion On Voting Patterns
Does Financial Literacy Guarantee Better Money Management?
Cultural Identities and Behavioral Patterns
How Religious Orientation Determines Social Media Use
The Growing Need for Economists Globally
Getting Started with Businesses On Social Media
Which Is Better: A 9-5 or An Entrepreneurial Job?

Do you want to write on unique statistical experiment ideas? Here are some topics you do not want to miss out on:

Consumer Satisfaction-Related Variables on E-Commerce Websites
Obesity Rates and Socioeconomic Status In Developed Countries
How Marketing Strategies Can Make or Mar Sales Performance
The Correlation Between Increased Income and Happiness In Various Nations
Regression Models and Forecasting Home Prices
Climate Change Affecting Agricultural Production In Specific Areas
A Study of Employee Satisfaction In the Healthcare Industry
Social Media, Marketing Tactics, and Consumer Behavior In the Fashion Industry
Predicting the Risk of Default Among Credit Card Holders In Different Regions
Why Crime Rates Are Increasing In Urban Areas than Rural Areas
Statistical Evaluation of Methamphetamine’s Impact On Drug Users
Genes and a Child’s Total Immunity

Here are some of the most carefully selected stat research topics you can focus on.

Social Media’s Effects On Consumer Behavior
The Correlation Between Urban Crime Rates and Poverty Levels
Physical Exercise and Mental Health Consequences
Predictive Modeling In the Financial Markets
How Minimum Wage Regulations Impact Employment Rates
Healthcare Outcomes and Access Across Various Socioeconomic Groups
How High School Students’ Environment Affect Academic Performance
Automated Technology and Employment Loss
Environmental Elements and Their Effects On Public Health
Various Advertising Tactics and How They Influence Customer Behavior
Political Polarization And Economic Inequality
Climate Change and Agricultural Productivity

The above statistics final project examples will stimulate your curiosity and test your abilities, and they can even be linked to some biochemistry topics and anatomy research paper topics . Writing about these statistics project ideas helps provide a deeper grasp of the natural and social phenomena that affect our lives and the environment by studying these subjects.

Statistics PhD theses

2015 onwards.

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Mathematics and Statistics (MATH)

Statistics (Ph.D.)

https://nextcatalog.unh.edu/graduate/programs-study/mathematics-statistics/statistics-phd/

The Ph.D. in statistics is a flexible program of coursework and research that meshes the faculty's expertise with the students' interests. Current faculty expertise are in Design of Experiments, Nonparametric Function Estimation, Model Selection, Time Series Analysis, Spatial Statistics, Bayesian Statistics, Data Mining and Large Data. Doctoral dissertations range from theoretical to applied. Interdisciplinary research is encouraged. Ph.D. students frequently work as research assistants in interdisciplinary studies, and also engage in statistical consulting.

Admission Requirement

Applicants must have completed significant undergraduate coursework in mathematics and Statistics, including basic Statistics (for example, design of experiments), the standard Calculus sequence, and Linear Algebra.

Please visit the Graduate School website for detailed instructions about applying to the doctoral program.

Degree Requirements

Students are advanced to candidacy after meeting the following requirements:

MATH 979 Research Topics in Statistics is a topics course and may be repeated barring duplication of topic.

Successful completion of written qualifying examinations in theory of statistics and in applied statistics.

Successful completion of a comprehensive exam in advanced theory of statistics.

Successful completion of a dissertation proposal defense in the major field of statistics.

Dissertation

Doctor of Philosophy in Statistics: A dissertation that includes original research in statistics.

Demonstrate deep knowledge of the theoretical foundations of statistics at the advanced level.
Conduct research that contributes to the development of statistical theory and methods.
Demonstrate competency in a broad array of advanced statistical methodologies, including skill in statistical computing for analysis and simulation.
Demonstrate familiarity with at least one scientific area of investigation that crucially depends on statistical methodology.

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Top 99+ Trending Statistics Research Topics for Students

Being a statistics student, finding the best statistics research topics is quite challenging. But not anymore; find the best statistics research topics now!!!

Statistics is one of the tough subjects because it consists of lots of formulas, equations and many more. Therefore the students need to spend their time to understand these concepts. And when it comes to finding the best statistics research project for their topics, statistics students are always looking for someone to help them.

In this blog, we will share with you the most interesting and trending statistics research topics in 2023. It will not just help you to stand out in your class but also help you to explore more about the world.

If you face any problem regarding statistics, then don’t worry. You can get the best statistics assignment help from one of our experts.

As you know, it is always suggested that you should work on interesting topics. That is why we have mentioned the most interesting research topics for college students and high school students. Here in this blog post, we will share with you the list of 99+ awesome statistics research topics.

Why Do We Need to Have Good Statistics Research Topics?

Table of Contents

Having a good research topic will not just help you score good grades, but it will also allow you to finish your project quickly. Because whenever we work on something interesting, our productivity automatically boosts. Thus, you need not invest lots of time and effort, and you can achieve the best with minimal effort and time.

What Are Some Interesting Research Topics?

If we talk about the interesting research topics in statistics, it can vary from student to student. But here are the key topics that are quite interesting for almost every student:-

Literacy rate in a city.
Abortion and pregnancy rate in the USA.
Eating disorders in the citizens.
Parent role in self-esteem and confidence of the student.
Uses of AI in our daily life to business corporates.

Top 99+ Trending Statistics Research Topics For 2023

Here in this section, we will tell you more than 99 trending statistics research topics:

Sports Statistics Research Topics

Statistical analysis for legs and head injuries in Football.
Statistical analysis for shoulder and knee injuries in MotoGP.
Deep statistical evaluation for the doping test in sports from the past decade.
Statistical observation on the performance of athletes in the last Olympics.
Role and effect of sports in the life of the student.

Psychology Research Topics for Statistics

Deep statistical analysis of the effect of obesity on the student’s mental health in high school and college students.
Statistical evolution to find out the suicide reason among students and adults.
Statistics analysis to find out the effect of divorce on children in a country.
Psychology affects women because of the gender gap in specific country areas.
Statistics analysis to find out the cause of online bullying in students’ lives.
In Psychology, PTSD and descriptive tendencies are discussed.
The function of researchers in statistical testing and probability.
Acceptable significance and probability thresholds in clinical Psychology.
The utilization of hypothesis and the role of P 0.05 for improved comprehension.
What types of statistical data are typically rejected in psychology?
The application of basic statistical principles and reasoning in psychological analysis.
The role of correlation is when several psychological concepts are at risk.
Actual case study learning and modeling are used to generate statistical reports.
In psychology, naturalistic observation is used as a research sample.
How should descriptive statistics be used to represent behavioral data sets?

Applied Statistics Research Topics

Does education have a deep impact on the financial success of an individual?
The investment in digital technology is having a meaningful return for corporations?
The gap of financial wealth between rich and poor in the USA.
A statistical approach to identify the effects of high-frequency trading in financial markets.
Statistics analysis to determine the impact of the multi-agent model in financial markets.

Personalized Medicine Statistics Research Topics

Statistical analysis on the effect of methamphetamine on substance abusers.
Deep research on the impact of the Corona vaccine on the Omnicrone variant.
Find out the best cancer treatment approach between orthodox therapies and alternative therapies.
Statistics analysis to identify the role of genes in the child’s overall immunity.
What factors help the patients to survive from Coronavirus .

Experimental Design Statistics Research Topics

Generic vs private education is one of the best for the students and has better financial return.
Psychology vs physiology: which leads the person not to quit their addictions?
Effect of breastmilk vs packed milk on the infant child overall development
Which causes more accidents: male alcoholics vs female alcoholics.
What causes the student not to reveal the cyberbullying in front of their parents in most cases.

Easy Statistics Research Topics

Application of statistics in the world of data science
Statistics for finance: how statistics is helping the company to grow their finance
Advantages and disadvantages of Radar chart
Minor marriages in south-east Asia and African countries.
Discussion of ANOVA and correlation.
What statistical methods are most effective for active sports?
When measuring the correctness of college tests, a ranking statistical approach is used.
Statistics play an important role in Data Mining operations.
The practical application of heat estimation in engineering fields.
In the field of speech recognition, statistical analysis is used.
Estimating probiotics: how much time is necessary for an accurate statistical sample?
How will the United States population grow in the next twenty years?
The legislation and statistical reports deal with contentious issues.
The application of empirical entropy approaches with online grammar checking.
Transparency in statistical methodology and the reporting system of the United States Census Bureau.

Statistical Research Topics for High School

Uses of statistics in chemometrics
Statistics in business analytics and business intelligence
Importance of statistics in physics.
Deep discussion about multivariate statistics
Uses of Statistics in machine learning

Survey Topics for Statistics

Gather the data of the most qualified professionals in a specific area.
Survey the time wasted by the students in watching Tvs or Netflix.
Have a survey the fully vaccinated people in the USA
Gather information on the effect of a government survey on the life of citizens
Survey to identify the English speakers in the world.

Statistics Research Paper Topics for Graduates

Have a deep decision of Bayes theorems
Discuss the Bayesian hierarchical models
Analysis of the process of Japanese restaurants.
Deep analysis of Lévy’s continuity theorem
Analysis of the principle of maximum entropy

AP Statistics Topics

Discuss about the importance of econometrics
Analyze the pros and cons of Probit Model
Types of probability models and their uses
Deep discussion of ortho stochastic matrix
Find out the ways to get an adjacency matrix quickly

Good Statistics Research Topics

National income and the regulation of cryptocurrency.
The benefits and drawbacks of regression analysis.
How can estimate methods be used to correct statistical differences?
Mathematical prediction models vs observation tactics.
In sociology research, there is bias in quantitative data analysis.
Inferential analytical approaches vs. descriptive statistics.
How reliable are AI-based methods in statistical analysis?
The internet news reporting and the fluctuations: statistics reports.
The importance of estimate in modeled statistics and artificial sampling.

Business Statistics Topics

Role of statistics in business in 2023
Importance of business statistics and analytics
What is the role of central tendency and dispersion in statistics
Best process of sampling business data.
Importance of statistics in big data.
The characteristics of business data sampling: benefits and cons of software solutions.
How may two different business tasks be tackled concurrently using linear regression analysis?
In economic data relations, index numbers, random probability, and correctness are all important.
The advantages of a dataset approach to statistics in programming statistics.
Commercial statistics: how should the data be prepared for maximum accuracy?

Statistical Research Topics for College Students

Evaluate the role of John Tukey’s contribution to statistics.
The role of statistics to improve ADHD treatment.
The uses and timeline of probability in statistics.
Deep analysis of Gertrude Cox’s experimental design in statistics.
Discuss about Florence Nightingale in statistics.
What sorts of music do college students prefer?
The Main Effect of Different Subjects on Student Performance.
The Importance of Analytics in Statistics Research.
The Influence of a Better Student in Class.
Do extracurricular activities help in the transformation of personalities?
Backbenchers’ Impact on Class Performance.
Medication’s Importance in Class Performance.
Are e-books better than traditional books?
Choosing aspects of a subject in college

How To Write Good Statistics Research Topics?

So, the main question that arises here is how you can write good statistics research topics. The trick is understanding the methodology that is used to collect and interpret statistical data. However, if you are trying to pick any topic for your statistics project, you must think about it before going any further.

As a result, it will teach you about the data types that will be researched because the sample will be chosen correctly. On the other hand, your basic outline for choosing the correct topics is as follows:

Introduction of a problem
Methodology explanation and choice.
Statistical research itself is in the main part (Body Part).
Samples deviations and variables.
Lastly, statistical interpretation is your last part (conclusion).

Note: Always include the sources from which you obtained the statistics data.

Top 3 Tips to Choose Good Statistics Research Topics

It can be quite easy for some students to pick a good statistics research topic without the help of an essay writer. But we know that it is not a common scenario for every student. That is why we will mention some of the best tips that will help you choose good statistics research topics for your next project. Either you are in a hurry or have enough time to explore. These tips will help you in every scenario.

1. Narrow down your research topic

We all start with many topics as we are not sure about our specific interests or niche. The initial step to picking up a good research topic for college or school students is to narrow down the research topic.

For this, you need to categorize the matter first. And then pick a specific category as per your interest. After that, brainstorm about the topic’s content and how you can make the points catchy, focused, directional, clear, and specific.

2. Choose a topic that gives you curiosity

After categorizing the statistics research topics, it is time to pick one from the category. Don’t pick the most common topic because it will not help your grades and knowledge. Instead of it, please choose the best one, in which you have little information, or you are more likely to explore it.

In a statistics research paper, you always can explore something beyond your studies. By doing this, you will be more energetic to work on this project. And you will also feel glad to get them lots of information you were willing to have but didn’t get because of any reasons.

It will also make your professor happy to see your work. Ultimately it will affect your grades with a positive attitude.

3. Choose a manageable topic

Now you have decided on the topic, but you need to make sure that your research topic should be manageable. You will have limited time and resources to complete your project if you pick one of the deep statistics research topics with massive information.

Then you will struggle at the last moment and most probably not going to finish your project on time. Therefore, spend enough time exploring the topic and have a good idea about the time duration and resources you will use for the project.

Statistics research topics are massive in numbers. Because statistics operations can be performed on anything from our psychology to our fitness. Therefore there are lots more statistics research topics to explore. But if you are not finding it challenging, then you can take the help of our statistics experts . They will help you to pick the most interesting and trending statistics research topics for your projects.

With this help, you can also save your precious time to invest it in something else. You can also come up with a plethora of topics of your choice and we will help you to pick the best one among them. Apart from that, if you are working on a project and you are not sure whether that is the topic that excites you to work on it or not. Then we can also help you to clear all your doubts on the statistics research topic.

Frequently Asked Questions

Q1. what are some good topics for the statistics project.

Have a look at some good topics for statistics projects:- 1. Research the average height and physics of basketball players. 2. Birth and death rate in a specific city or country. 3. Study on the obesity rate of children and adults in the USA. 4. The growth rate of China in the past few years 5. Major causes of injury in Football

Q2. What are the topics in statistics?

Statistics has lots of topics. It is hard to cover all of them in a short answer. But here are the major ones: conditional probability, variance, random variable, probability distributions, common discrete, and many more.

Q3. What are the top 10 research topics?

Here are the top 10 research topics that you can try in 2023:

1. Plant Science 2. Mental health 3. Nutritional Immunology 4. Mood disorders 5. Aging brains 6. Infectious disease 7. Music therapy 8. Political misinformation 9. Canine Connection 10. Sustainable agriculture

How to Find the Best Online Statistics Homework Help

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MPhil/PhD Statistics

Graduate research
Department of Statistics
Application code G4ZS
Starting 2024
Home full-time: Open
Overseas full-time: Open
Location: Houghton Street, London

Programme details

For more information about tuition fees and entry requirements, see the fees and funding and assessing your application sections.

Entry requirements

Minimum entry requirements for mphil/phd statistics.

The minimum entry requirement for this programme is a distinction in a taught master’s (or equivalent) with substantial statistical content, or equivalent experience.

Competition for places at the School is high. This means that even if you meet our minimum entry requirement, this does not guarantee you an offer of admission.

If you have studied or are studying outside of the UK then have a look at our Information for International Students to find out the entry requirements that apply to you.

Assessing your application

We welcome applications for research programmes that complement the academic interests of members of staff at the School, and we recommend that you investigate staff research interests before applying.

We carefully consider each application on an individual basis, taking into account all the information presented on your application form, including your:

- academic achievement (including existing and pending qualifications) - statement of academic purpose - references - CV - research proposal - sample of written work.

See further information on supporting documents

You may also have to provide evidence of your English proficiency. You do not need to provide this at the time of your application to LSE, but we recommend that you do. See our English language requirements .

Most applicants will have little or no prior experience of research and therefore we do not expect a fully-developed research proposal. We are assessing the potential of the applicant for research and the chosen topic. The following is a guideline of what to emphasise in the proposal.

pose a research question rather than a very broad research topic
be specific, to aid selectors to assess the suitability of the topic for PhD study
a statement of how the proposed research builds on earlier research on the topic, with reference to two or three key papers
demonstrate your understanding of the area and the need for further research
selectors will look for a sense of the merits of your approach
most topics will involve an application of the proposed methods to a substantive research question. Give a brief outline of this question and explain how it will benefit from this particular approach
be specific about the training and skills you have to undertake the proposed research (do not simply list courses attended: this information is already available in the CV and transcripts).

When to apply

The application deadline for this programme is 23 May 2024 . However, to be considered for any LSE funding opportunity, you must have submitted your application and all supporting documents by the funding deadline. See the fees and funding section for more details.

Fees and funding

Every research student is charged a fee in line with the fee structure for their programme. The fee covers registration and examination fees payable to the School, lectures, classes and individual supervision, lectures given at other colleges under intercollegiate arrangements and, under current arrangements, membership of the Students' Union. It does not cover living costs or travel or fieldwork.

Tuition fees 2024/25 for MPhil/PhD Statistics

Home students: £4,829 for the first year (provisional) Overseas students: £22,632 for the first year

The fee is likely to rise over subsequent years of the programme. The School charges home research students in line with the level of fee that the Research Councils recommend. The fees for overseas students are likely to rise in line with the assumed percentage increase in pay costs (ie, 4 per cent per annum).

The Table of Fees shows the latest tuition amounts for all programmes offered by the School.

Fee status

The amount of tuition fees you will need to pay, and any financial support you are eligible for, will depend on whether you are classified as a home or overseas student, otherwise known as your fee status. LSE assesses your fee status based on guidelines provided by the Department of Education.

Further information about fee status classification.

Scholarships, studentships and other funding

The School recognises that the cost of living in London may be higher than in your home town or country, and we provide generous scholarships each year to home and overseas students.

This programme is eligible for LSE PhD Studentships , and Economic and Social Research Council (ESRC) funding . Selection for the PhD Studentships and ESRC funding is based on receipt of an application for a place – including all ancillary documents, before the funding deadline.

Funding deadline for LSE PhD Studentships and ESRC funding: 15 January 2024

In addition to our needs-based awards, LSE also makes available scholarships for students from specific regions of the world and awards for students studying specific subject areas. Find out more about financial support.

Statistics PhD Scholarship

The Department of Statistics offers one studentship to a 2023/24 offer holder covering fees and living expenses for four years. This scholarship is available for a home or overseas student undertaking research in any statistics discipline, with annual renewal subject to satisfactory academic performance. The scholarship is awarded strictly on academic merit and research potential. To be considered for this scholarship you must submit your application, including all supporting documentation, by 13 January 2023.

External funding

There may be other funding opportunities available through other organisations or governments and we recommend you investigate these options as well.

Further information

Fees and funding opportunities

Information for international students

LSE is an international community, with over 140 nationalities represented amongst its student body. We celebrate this diversity through everything we do.

If you are applying to LSE from outside of the UK then take a look at our Information for International students .

1) Take a note of the UK qualifications we require for your programme of interest (found in the ‘Entry requirements’ section of this page).

2) Go to the International Students section of our website.

3) Select your country.

4) Select ‘Graduate entry requirements’ and scroll until you arrive at the information about your local/national qualification. Compare the stated UK entry requirements listed on this page with the local/national entry requirement listed on your country specific page.

Programme structure and courses

In addition to progressing with your research, you are expected to take the listed training and transferable skills courses. You may take courses in addition to those listed but you must discuss this with your supervisor.

At the end of your second year (full-time), you will need to satisfy certain requirements and if you meet these, will be retroactively upgraded to PhD status.

(* denotes half unit)

Training courses

Compulsory (examined)

Probability and Mathematical Statistics I*

Statistical Modelling and Data Analysis*

And one of:

Foundations of Machine Learning*

Probability and Mathematical Statistics II* Students may take a different course option with the agreement of both the supervisor and PhD Programme Director.

Optional (examined)

Courses offered by the London Graduate School in Mathematical Finance Courses offered by the London Taught Course Centre Optional (examined) Master's-level courses relevant to research and agreed by supervisor in Department, the School or University of London College

Transferable skills courses

Compulsory (not examined)

One presentation

Attendance of departmental seminar appropriate to the student's field of study.

Optional (not examined) London Graduate School in Mathematical Finance PhD Presentation Day

Poster Presentations The Department encourages you to attend and, where the opportunity arises, present a paper or poster at conferences during your PhD programme in relation to your particular research topic.

Optional (examined) Courses provided by the Department of Methodology

Second year

Optional (not examined) Courses offered by the London Graduate School in Mathematical Finance Courses offered by the London Taught Course Centre Optional (examined) Master’s-level courses relevant to research and agreed by supervisor in Department, the School or University of London College

Two presentations

Attendance of departmental seminars appropriate to the student's field of study.

Optional (not examined) London Graduate School in Mathematical Finance Seminar Day

Optional (not examined) Courses offered by the London Graduate School in Mathematical Finance Courses offered by the London Taught Course Centre Optional (examined) Master’s-level courses relevant to research and agreed by supervisor in the Department, the School or University of London College

Two presentations

Optional (not examined) London Graduate School in Mathematical Finance PhD Presentation Day Poster Presentations The Department encourages you to attend and, where the opportunity arises, present a paper or poster at conferences during your PhD programme in relation to your particular research topic.

Fourth year

Optional (not examined) Courses offered by the London Graduate School in Mathematical Finance Courses offered by the London Taught Course Centre

Optional (examined) Master's level courses relevant to research and agreed by supervisor in the Department, the School or University of London College

Optional (not examined)

London Graduate School in Mathematical Finance Seminar Day

Optional (examined) Courses provided by the Department of Methodology.

For the most up-to-date list of optional courses please visit the relevant School Calendar page.

You must note, however, that while care has been taken to ensure that this information is up to date and correct, a change of circumstances since publication may cause the School to change, suspend or withdraw a course or programme of study, or change the fees that apply to it. The School will always notify the affected parties as early as practicably possible and propose any viable and relevant alternative options. Note that the School will neither be liable for information that after publication becomes inaccurate or irrelevant, nor for changing, suspending or withdrawing a course or programme of study due to events outside of its control, which includes but is not limited to a lack of demand for a course or programme of study, industrial action, fire, flood or other environmental or physical damage to premises.

You must also note that places are limited on some courses and/or subject to specific entry requirements. The School cannot therefore guarantee you a place. Please note that changes to programmes and courses can sometimes occur after you have accepted your offer of a place. These changes are normally made in light of developments in the discipline or path-breaking research, or on the basis of student feedback. Changes can take the form of altered course content, teaching formats or assessment modes. Any such changes are intended to enhance the student learning experience. You should visit the School’s Calendar , or contact the relevant academic department, for information on the availability and/or content of courses and programmes of study. Certain substantive changes will be listed on the updated graduate course and programme information page.

Supervision, progression and assessment

Supervision.

You will be assigned a lead supervisor (and a second supervisor/adviser) who is a specialist in your chosen research field, though not necessarily in your topic. Lead supervisors guide you through your studies.

You may wish to first send an informal application email to the Department of Statistics to enquire about making a formal application within the area of your research interests and to check about the availability of potential supervisors.

Progression and assessment

Formal assessment is made towards the end of each Spring Term. This assessment is based on statements made by you and the supervisors in the progress report form. You are also required to complete a supplementary report of one to two pages (A4), providing in more detail an outline of your current research.

The review to upgrade to the PhD normally takes place within two years of full-time registration. Progress is assessed by the first and/or second supervisor in consultation with the PhD programme director and another expert in the field of the research you are undertaking. If satisfactory progress has been made, the programme director will recommend that registration be upgraded to PhD status. The Department's research committee also monitors the progress of PhD students.

Student support and resources

We’re here to help and support you throughout your time at LSE, whether you need help with your academic studies, support with your welfare and wellbeing or simply to develop on a personal and professional level.

Whatever your query, big or small, there are a range of people you can speak to who will be happy to help.

Department librarians – they will be able to help you navigate the library and maximise its resources during your studies.

Accommodation service – they can offer advice on living in halls and offer guidance on private accommodation related queries.

Class teachers and seminar leaders – they will be able to assist with queries relating to specific courses.

Disability and Wellbeing Service – they are experts in long-term health conditions, sensory impairments, mental health and specific learning difficulties. They offer confidential and free services such as student counselling, a peer support scheme and arranging exam adjustments. They run groups and workshops.

IT help – support is available 24 hours a day to assist with all your technology queries.

LSE Faith Centre – this is home to LSE's diverse religious activities and transformational interfaith leadership programmes, as well as a space for worship, prayer and quiet reflection. It includes Islamic prayer rooms and a main space for worship. It is also a space for wellbeing classes on campus and is open to all students and staff from all faiths and none.

Language Centre – the Centre specialises in offering language courses targeted to the needs of students and practitioners in the social sciences. We offer pre-course English for Academic Purposes programmes; English language support during your studies; modern language courses in nine languages; proofreading, translation and document authentication; and language learning community activities.

LSE Careers – with the help of LSE Careers, you can make the most of the opportunities that London has to offer. Whatever your career plans, LSE Careers will work with you, connecting you to opportunities and experiences from internships and volunteering to networking events and employer and alumni insights.

LSE Library – founded in 1896, the British Library of Political and Economic Science is the major international library of the social sciences. It stays open late, has lots of excellent resources and is a great place to study. As an LSE student, you’ll have access to a number of other academic libraries in Greater London and nationwide.

LSE LIFE – this is where you should go to develop skills you’ll use as a student and beyond. The centre runs talks and workshops on skills you’ll find useful in the classroom; offers one-to-one sessions with study advisers who can help you with reading, making notes, writing, research and exam revision; and provides drop-in sessions for academic and personal support. (See ‘Teaching and assessment’).

LSE Students’ Union (LSESU) – they offer academic, personal and financial advice and funding.

PhD Academy – this is available for PhD students, wherever they are, to take part in interdisciplinary events and other professional development activities and access all the services related to their registration.

Sardinia House Dental Practice – this offers discounted private dental services to LSE students.

St Philips Medical Centre – based in Pethwick-Lawrence House, the Centre provides NHS Primary Care services to registered patients.

Student Services Centre – our staff here can answer general queries and can point you in the direction of other LSE services.

Student advisers – we have a Deputy Head of Student Services (Advice and Policy) and an Adviser to Women Students who can help with academic and pastoral matters.

Student life

As a student at LSE you’ll be based at our central London campus. Find out what our campus and London have to offer you on academic, social and career perspective.

Student societies and activities

Your time at LSE is not just about studying, there are plenty of ways to get involved in extracurricular activities . From joining one of over 200 societies, or starting your own society, to volunteering for a local charity, or attending a public lecture by a world-leading figure, there is a lot to choose from.

The campus

LSE is based on one campus in the centre of London. Despite the busy feel of the surrounding area, many of the streets around campus are pedestrianised, meaning the campus feels like a real community.

Life in London

London is an exciting, vibrant and colourful city. It's also an academic city, with more than 400,000 university students. Whatever your interests or appetite you will find something to suit your palate and pocket in this truly international capital. Make the most of career opportunities and social activities, theatre, museums, music and more.

Want to find out more? Read why we think London is a fantastic student city , find out about key sights, places and experiences for new Londoners . Don't fear, London doesn't have to be super expensive: hear about London on a budget .

Student stories

MPhil/PhD Statistics Suzhou, China

Statistics is important in every industry in modern society; you need statistics to analyse data and ultimately to solve empirical problems. My programme enables me to apply my statistical knowledge to real world problems in finance and economics.

Quick Careers Facts for the Department of Statistics

Median salary of our PG students 15 months after graduating: £38,000

Top 5 sectors our students work in:

Financial and Professional Services
Information, Digital Technology and Data
FMCG, Manufacturing and Retail
Accounting and Auditing
Government, Public Sector and Policy

The data was collected as part of the Graduate Outcomes survey, which is administered by the Higher Education Statistics Agency (HESA). Graduates from 2020-21 were the fourth group to be asked to respond to Graduate Outcomes. Median salaries are calculated for respondents who are paid in UK pounds sterling and who were working in full-time employment.

Students who successfully complete the programme often embark on an academic career. Recent doctoral graduates have also gone into careers in investment banking. See career destinations for some of our former students .

Further information on graduate destinations for this programme

Support for your career

Many leading organisations give careers presentations at the School during the year, and LSE Careers has a wide range of resources available to assist students in their job search. Find out more about the support available to students through LSE Careers .

Find out more about LSE

Discover more about being an LSE student - meet us in a city near you, visit our campus or experience LSE from home.

Experience LSE from home

Webinars, videos, student blogs and student video diaries will help you gain an insight into what it's like to study at LSE for those that aren't able to make it to our campus. Experience LSE from home .

Come on a guided campus tour, attend an undergraduate open day, drop into our office or go on a self-guided tour. Find out about opportunities to visit LSE .

LSE visits you

Student Marketing, Recruitment and Study Abroad travels throughout the UK and around the world to meet with prospective students. We visit schools, attend education fairs and also hold Destination LSE events: pre-departure events for offer holders. Find details on LSE's upcoming visits .

How to apply

Virtual Graduate Open Day

Register your interest

Related programmes, mphil/phd mathematics.

Code(s) G1ZM

MSc Statistics (Research)

Code(s) G4U1

MSc Statistics (Financial Statistics) (Research)

Code(s) G4U7

MSc Data Science

Code(s) G3U1

MSc Statistics (Social Statistics) (Research)

Code(s) G3U3

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Program Description

Ph.d. program description.

The educational and research profile of the STOR Ph.D. program is focused on the core disciplines of statistics, optimization, probability, and stochastic modeling. These disciplines have driven, and continue to drive, progress in data science and machine learning, as well as business and medical analytics. The STOR Department is one of the few in the US that brings together experts in each of these disciplines under one academic roof.

STOR offers a rigorous but flexible interdisciplinary Ph.D. program within which students can benefit from the strength and diverse expertise of the Department’s core faculty, while also having the opportunity to interact with domain scientists and researchers working in other fields. STOR Ph.D. students complete foundational coursework in the four core disciplines before undertaking more specialized coursework and directed dissertation research. Dissertation research is completed under the supervision of one or more faculty advisers. Research topics may lie within a single core discipline, or may span several core disciplines. Many research topics involve interdisciplinary research, including active collaboration with faculty and students at UNC and elsewhere.

The breadth and depth of the STOR Ph.D. program prepare graduates for a wide variety of careers, ranging from academia to industry, and from the public to the private sector. Recent graduates have taken jobs in mathematics, statistics, IE, OR departments; medical and business schools; high-tech, biotech, and financial companies; and government agencies.

Requirements

Completion of the Ph.D. degree requires at least forty-five, 45, semester hours of graduate coursework. To meet this requirement, students typically take fifteen three-credit courses. These can be divided into core courses and electives:

By the end of the Fall of the 2nd year students complete the five Core-I courses, see below, in order to get a solid foundation in all areas of the department. This is accomplished by students choosing, at least, 3 out of the 5 Core-I courses in the Fall of the first year; the remaining, at most, two Core-I courses are completed in the Fall of the second year.
In the spring of the first year, students take at least 3 courses including at least 2 of the Core-II courses that are in the same subject as the Core-I course taken in the Fall of year one. The remaining course could either be another Core-II course, another course in the department or, with the Director’s of Graduate Studies approval, courses in other departments.
See the coursework timeline below for more details on course electives.

Core I Courses

Offered in Fall:

Probability
Optimization
Theoretical Statistics
Applied Statistics and Statistical Computing
Stochastic Modeling

Core II courses

Offered in Spring:

Probability II
Optimization II
Theoretical Statistics II
Applied Statistics and Statistical Computing II
Stochastic Modeling II

Coursework Timeline

Year 1: fall.

Students choose at least 3 of the 5 possible Core-I courses, lised above.

Year 1: Spring

In the spring of the first year, students take at least 3 courses including at least 2 of the Core-II courses that are in the same subject as the Core-I courses taken in the Fall of year 1. The third course could either be another Core II course, another course in the department or, the Director’s of Graduate Studies approval, courses in other departments.

Year 2: Fall

Students are required to take the remaining, typically two, Core-I courses that were not taken in the Fall of the first year. In addition students can take other advanced and topics courses offered in the department.

Year 2: Spring and beyond

By the completion of the program, students need to take in total 45 credit hours. By the end of the first year all students have completed at least 15 credit hours within the department. The remaining credit hours can be attained either via taking courses at the 600 or more advanced level in the department or by taking courses outside the department, up to nine credit hours.
Ph.D. students may count up to nine credit hours of coursework outside the program towards the forty-five credit hours required for the doctoral degree. Outside coursework can come from other units at the university including Biostatistics, Business School, Mathematics, Computer Science and Economics, and can also be at Duke University or at NCSU. Coursework outside the department needs to be approved by the Graduate Curriculum Committee in order to count towards the Ph.D. degree.

Doctoral Dissertation

Students develop and pursue their dissertation research under the guidance of a core member of the STOR Faculty. In some cases, a student may be co-advised by two core faculty members, or by a core faculty member and a co-advisor from another department.

Coursework Timeline : Students are expected to complete their coursework and thesis research within five years of entering the program. The University does not provide tuition remission beyond five years. In addition, the Department cannot provide assistantships for students after the end of their fifth year.

Note : For more information on the University’s requirements and procedures for the Ph.D. and Ph.D. dissertations, see the Graduate School Handbook .

Examinations

Written: Students choose 2 of the full sequences, a full sequence is a Core-I course and Core-II course in the same subject, they have completed in the first year and take a comprehensive written exam, CWE, on these two chosen sequences. A student can write a CWE in a sequence only if they have enrolled and passed both courses in that sequence in the first year. The CWE is usually taken just prior to a student’s second year of study.

Preliminary and Final Oral Examinations: Under normal circumstances, students take the Preliminary Oral Exam by the end of their third year of study. During the Preliminary Oral Examination students present a general, prospective outline of their thesis research, which may include preliminary results. The Final Oral Examination is the student’s formal thesis defense, and usually takes place during the fifth year of study.

Examples of Course Plans

None of the following are “set in stone” and are only meant to provide examples of what students can pursue. Paths are tailored for individual students based on their interests and ability when joining the program in discussion with the Directors of Graduate Studies and the Graduate Committee.

Focus on Probability

Example 1 – Previously part of the STAT track

Fall: P, SM, TS
Spring: PII, SMII, TSII

Take CWEs in, P-PII and TS-TSII.

Spring: PII, SMII, Functional Analysis (Math)

Take CWEs in P-PII and SM-SMII.

Focus on Theoretical Statistics

Fall: P, TS, AS
Spring: PII, TSII, ASII

Take CWEs in TS-TSII and AS-ASII.

Spring: PII, TSII, Machine Learning, if offered.

Take CWEs in P-PII and TS-TSII.

Focus on Applied Statistics and/or Machine learning

Spring: TSII, ASII, Machine Learning, if offered.

Example 2 – Previously the machine learning track in INSTORE

Fall: Opt, TS, AS
Spring: OptII, TSII, ASII

Focus on Stochastic Modeling

Fall: Opt, SM, AS
Spring: SMII, ASII, Real Analysis, Math

Take CWEs in SM-SMII and AS-ASII.

Spring: OptII, SMII, Simulation

Take CWEs in SM-SMII and OptI-OptII.

Example 3 – Previously part of the OR track

Fall: Opt, SM, TS

Focus on Business Analytics

Example – Previously part of the INSTORE program

Spring: OptII, SMII, ASII

Focus on Optimization

Take CWEs in: Opt-OptII and SM-SMII.

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Digital Commons @ USF > College of Arts and Sciences > Mathematics and Statistics > Theses and Dissertations

Mathematics and Statistics Theses and Dissertations

Theses/dissertations from 2023 2023.

Classification of Finite Topological Quandles and Shelves via Posets , Hitakshi Lahrani

Applied Analysis for Learning Architectures , Himanshu Singh

Rational Functions of Degree Five That Permute the Projective Line Over a Finite Field , Christopher Sze

Theses/Dissertations from 2022 2022

New Developments in Statistical Optimal Designs for Physical and Computer Experiments , Damola M. Akinlana

Advances and Applications of Optimal Polynomial Approximants , Raymond Centner

Data-Driven Analytical Predictive Modeling for Pancreatic Cancer, Financial & Social Systems , Aditya Chakraborty

On Simultaneous Similarity of d-tuples of Commuting Square Matrices , Corey Connelly

Symbolic Computation of Lump Solutions to a Combined (2+1)-dimensional Nonlinear Evolution Equation , Jingwei He

Boundary behavior of analytic functions and Approximation Theory , Spyros Pasias

Stability Analysis of Delay-Driven Coupled Cantilevers Using the Lambert W-Function , Daniel Siebel-Cortopassi

A Functional Optimization Approach to Stochastic Process Sampling , Ryan Matthew Thurman

Theses/Dissertations from 2021 2021

Riemann-Hilbert Problems for Nonlocal Reverse-Time Nonlinear Second-order and Fourth-order AKNS Systems of Multiple Components and Exact Soliton Solutions , Alle Adjiri

Zeros of Harmonic Polynomials and Related Applications , Azizah Alrajhi

Combination of Time Series Analysis and Sentiment Analysis for Stock Market Forecasting , Hsiao-Chuan Chou

Uncertainty Quantification in Deep and Statistical Learning with applications in Bio-Medical Image Analysis , K. Ruwani M. Fernando

Data-Driven Analytical Modeling of Multiple Myeloma Cancer, U.S. Crop Production and Monitoring Process , Lohuwa Mamudu

Long-time Asymptotics for mKdV Type Reduced Equations of the AKNS Hierarchy in Weighted L 2 Sobolev Spaces , Fudong Wang

Online and Adjusted Human Activities Recognition with Statistical Learning , Yanjia Zhang

Theses/Dissertations from 2020 2020

Bayesian Reliability Analysis of The Power Law Process and Statistical Modeling of Computer and Network Vulnerabilities with Cybersecurity Application , Freeh N. Alenezi

Discrete Models and Algorithms for Analyzing DNA Rearrangements , Jasper Braun

Bayesian Reliability Analysis for Optical Media Using Accelerated Degradation Test Data , Kun Bu

On the p(x)-Laplace equation in Carnot groups , Robert D. Freeman

Clustering methods for gene expression data of Oxytricha trifallax , Kyle Houfek

Gradient Boosting for Survival Analysis with Applications in Oncology , Nam Phuong Nguyen

Global and Stochastic Dynamics of Diffusive Hindmarsh-Rose Equations in Neurodynamics , Chi Phan

Restricted Isometric Projections for Differentiable Manifolds and Applications , Vasile Pop

On Some Problems on Polynomial Interpolation in Several Variables , Brian Jon Tuesink

Numerical Study of Gap Distributions in Determinantal Point Process on Low Dimensional Spheres: L -Ensemble of O ( n ) Model Type for n = 2 and n = 3 , Xiankui Yang

Non-Associative Algebraic Structures in Knot Theory , Emanuele Zappala

Theses/Dissertations from 2019 2019

Field Quantization for Radiative Decay of Plasmons in Finite and Infinite Geometries , Maryam Bagherian

Probabilistic Modeling of Democracy, Corruption, Hemophilia A and Prediabetes Data , A. K. M. Raquibul Bashar

Generalized Derivations of Ternary Lie Algebras and n-BiHom-Lie Algebras , Amine Ben Abdeljelil

Fractional Random Weighted Bootstrapping for Classiﬁcation on Imbalanced Data with Ensemble Decision Tree Methods , Sean Charles Carter

Hierarchical Self-Assembly and Substitution Rules , Daniel Alejandro Cruz

Statistical Learning of Biomedical Non-Stationary Signals and Quality of Life Modeling , Mahdi Goudarzi

Probabilistic and Statistical Prediction Models for Alzheimer’s Disease and Statistical Analysis of Global Warming , Maryam Ibrahim Habadi

Essays on Time Series and Machine Learning Techniques for Risk Management , Michael Kotarinos

The Systems of Post and Post Algebras: A Demonstration of an Obvious Fact , Daviel Leyva

Reconstruction of Radar Images by Using Spherical Mean and Regular Radon Transforms , Ozan Pirbudak

Analyses of Unorthodox Overlapping Gene Segments in Oxytricha Trifallax , Shannon Stich

An Optimal Medium-Strength Regularity Algorithm for 3-uniform Hypergraphs , John Theado

Power Graphs of Quasigroups , DayVon L. Walker

Theses/Dissertations from 2018 2018

Groups Generated by Automata Arising from Transformations of the Boundaries of Rooted Trees , Elsayed Ahmed

Non-equilibrium Phase Transitions in Interacting Diffusions , Wael Al-Sawai

A Hybrid Dynamic Modeling of Time-to-event Processes and Applications , Emmanuel A. Appiah

Lump Solutions and Riemann-Hilbert Approach to Soliton Equations , Sumayah A. Batwa

Developing a Model to Predict Prevalence of Compulsive Behavior in Individuals with OCD , Lindsay D. Fields

Generalizations of Quandles and their cohomologies , Matthew J. Green

Hamiltonian structures and Riemann-Hilbert problems of integrable systems , Xiang Gu

Optimal Latin Hypercube Designs for Computer Experiments Based on Multiple Objectives , Ruizhe Hou

Human Activity Recognition Based on Transfer Learning , Jinyong Pang

Signal Detection of Adverse Drug Reaction using the Adverse Event Reporting System: Literature Review and Novel Methods , Minh H. Pham

Statistical Analysis and Modeling of Cyber Security and Health Sciences , Nawa Raj Pokhrel

Machine Learning Methods for Network Intrusion Detection and Intrusion Prevention Systems , Zheni Svetoslavova Stefanova

Orthogonal Polynomials With Respect to the Measure Supported Over the Whole Complex Plane , Meng Yang

Theses/Dissertations from 2017 2017

Modeling in Finance and Insurance With Levy-It'o Driven Dynamic Processes under Semi Markov-type Switching Regimes and Time Domains , Patrick Armand Assonken Tonfack

Prevalence of Typical Images in High School Geometry Textbooks , Megan N. Cannon

On Extending Hansel's Theorem to Hypergraphs , Gregory Sutton Churchill

Contributions to Quandle Theory: A Study of f-Quandles, Extensions, and Cohomology , Indu Rasika U. Churchill

Linear Extremal Problems in the Hardy Space H p for 0 p , Robert Christopher Connelly

Statistical Analysis and Modeling of Ovarian and Breast Cancer , Muditha V. Devamitta Perera

Statistical Analysis and Modeling of Stomach Cancer Data , Chao Gao

Structural Analysis of Poloidal and Toroidal Plasmons and Fields of Multilayer Nanorings , Kumar Vijay Garapati

Dynamics of Multicultural Social Networks , Kristina B. Hilton

Cybersecurity: Stochastic Analysis and Modelling of Vulnerabilities to Determine the Network Security and Attackers Behavior , Pubudu Kalpani Kaluarachchi

Generalized D-Kaup-Newell integrable systems and their integrable couplings and Darboux transformations , Morgan Ashley McAnally

Patterns in Words Related to DNA Rearrangements , Lukas Nabergall

Time Series Online Empirical Bayesian Kernel Density Segmentation: Applications in Real Time Activity Recognition Using Smartphone Accelerometer , Shuang Na

Schreier Graphs of Thompson's Group T , Allen Pennington

Cybersecurity: Probabilistic Behavior of Vulnerability and Life Cycle , Sasith Maduranga Rajasooriya

Bayesian Artificial Neural Networks in Health and Cybersecurity , Hansapani Sarasepa Rodrigo

Real-time Classiﬁcation of Biomedical Signals, Parkinson’s Analytical Model , Abolfazl Saghafi

Lump, complexiton and algebro-geometric solutions to soliton equations , Yuan Zhou

Theses/Dissertations from 2016 2016

A Statistical Analysis of Hurricanes in the Atlantic Basin and Sinkholes in Florida , Joy Marie D'andrea

Statistical Analysis of a Risk Factor in Finance and Environmental Models for Belize , Sherlene Enriquez-Savery

Putnam's Inequality and Analytic Content in the Bergman Space , Matthew Fleeman

On the Number of Colors in Quandle Knot Colorings , Jeremy William Kerr

Statistical Modeling of Carbon Dioxide and Cluster Analysis of Time Dependent Information: Lag Target Time Series Clustering, Multi-Factor Time Series Clustering, and Multi-Level Time Series Clustering , Doo Young Kim

Some Results Concerning Permutation Polynomials over Finite Fields , Stephen Lappano

Hamiltonian Formulations and Symmetry Constraints of Soliton Hierarchies of (1+1)-Dimensional Nonlinear Evolution Equations , Solomon Manukure

Modeling and Survival Analysis of Breast Cancer: A Statistical, Artificial Neural Network, and Decision Tree Approach , Venkateswara Rao Mudunuru

Generalized Phase Retrieval: Isometries in Vector Spaces , Josiah Park

Leonard Systems and their Friends , Jonathan Spiewak

Resonant Solutions to (3+1)-dimensional Bilinear Differential Equations , Yue Sun

Statistical Analysis and Modeling Health Data: A Longitudinal Study , Bhikhari Prasad Tharu

Global Attractors and Random Attractors of Reaction-Diffusion Systems , Junyi Tu

Time Dependent Kernel Density Estimation: A New Parameter Estimation Algorithm, Applications in Time Series Classiﬁcation and Clustering , Xing Wang

On Spectral Properties of Single Layer Potentials , Seyed Zoalroshd

Theses/Dissertations from 2015 2015

Analysis of Rheumatoid Arthritis Data using Logistic Regression and Penalized Approach , Wei Chen

Active Tile Self-assembly and Simulations of Computational Systems , Daria Karpenko

Nearest Neighbor Foreign Exchange Rate Forecasting with Mahalanobis Distance , Vindya Kumari Pathirana

Statistical Learning with Artificial Neural Network Applied to Health and Environmental Data , Taysseer Sharaf

Radial Versus Othogonal and Minimal Projections onto Hyperplanes in l_4^3 , Richard Alan Warner

Ensemble Learning Method on Machine Maintenance Data , Xiaochuang Zhao

Theses/Dissertations from 2014 2014

Properties of Graphs Used to Model DNA Recombination , Ryan Arredondo

Recursive Methods in Number Theory, Combinatorial Graph Theory, and Probability , Jonathan Burns

On the Classification of Groups Generated by Automata with 4 States over a 2-Letter Alphabet , Louis Caponi

Statistical Analysis, Modeling, and Algorithms for Pharmaceutical and Cancer Systems , Bong-Jin Choi

Topological Data Analysis of Properties of Four-Regular Rigid Vertex Graphs , Grant Mcneil Conine

Trend Analysis and Modeling of Health and Environmental Data: Joinpoint and Functional Approach , Ram C. Kafle

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A Simple Beginner's Guide to PhD Research Topic Formulation

Greetings!!

Are you puzzled as to how you need to formulate a topic for your PhD research work? Feeling obscured under a lot of articles or books that you’ve read which only led to confusion? If it’s been more than a weeks’ time since you started, and one idea after another is running through your mind without any hint like how to mold one idea into a proper research project – you’re certainly feeling the burden!

We are here to help you in bringing up an appropriate approach for a workable research project. In addition, we have a provided a comprehensive 6-step-guideline for “Finding out a good and original PhD research topic”.

What is a ‘PhD research topic’?

There are some misinterpretations and uncertainty around the word ‘topic’, and many PhD scholars thinks that they require a general topic to get started with their research. However, it is not right. Normally, a PhD research topic is the heart of your research project which simply exposes the complete idea of your research work and further by using it, the dissertation/thesis needs to be framed out. In practice, deciding on a research topic and working out your research questions goes hand-in-hand.

Below six steps will guide you in formulating a good research topic for your PhD

Step 1. Research the state-of-art in your interested research domain

Initially find atleast 5-8 appropriate keywords for a literature search.

Further postulate your search regarding literature survey databases which you search, its publishing dates, topographical areas, or applied methods and techniques.

Summarize all the articles which you read and keep reference number for the same, so you can always revisit articles.

Step 2. Breakthrough your project ideas

Identify and write down all the project ideas which comes to your mind. Just gather your thoughts, without critically assessing them.

Find a quiet spot and write out all project ideas that come to your mind. Just collect all your thoughts, without critically evaluating them.

After few days, revisit your ideas and start reviewing and crossing out research ideas which are not worthwhile.

Continue adding new ideas and refine your list, as you go on with the reading process.

Step 3. Slender down your research ideas

After working on this, go through your formulated list and gradually narrow it down to 2-3 best research ideas.

Further to it, start in brainstorming each promising research idea, may be atleast one per day.

Pen down all possible and clear research questions, problem gaps, ideas for experiments/analysis, how to gather pragmatic evidence, hypothesis you’re having etc.

Step 4. Develop a “project work sheet”

Create and maintain a page “project work sheet” that surface out the specifically more about your research project in terms of approach, methods, techniques to be used of and expected outcomes of the research work taken.

The purpose of maintaining this project work sheet is to test how do you feel about the research topic-either positively or indifferent? You have time to work on the research topic for the approaching years. Preferably, you’re passionate about it.

Step 5. Discussing with your research guide/supervisor

Present all the formulated research topic to your supervisor would be always the best choice as they will help you in knowing whether the topics suggested is good to move ahead or need to work upon.

Now, based on their opinion (regarding time, funds, facilities & methods available) move further with any one of the research topics.

Integrate all their feedback on your “project-work sheet” to further polish them.

Step 6. Decide and develop statement of objectives

During this process, you might have decided one topic which is better and stands out with more potential. Go for that and start to further analyze the pro’s and con’s of it. But finally make a decision and move forward.

For the finalized research topic, create atleast 3-6 objectives which briefly gives the research project’s goal. Include research questions to answer or the hypothesis you’ll work with.

Conclusion:

If you’re one among those PhD candidates, it is most important to find your goal for doing PhD, now identifying the goal is your goal! We know that finding a research topic is a harder part, however the above mentioned six steps will definitely guide you in cracking and bringing about a good research topic.

Hope this article will be helpful for all the readers who are in the beginning stage of your PhD research work. In case of any queries related to this post, feel free to reach us through your comments or visit our website.

20 Possible Topics For A PhD Dissertation In Statistics

The dissertation is the culmination of a PhD student's career. Students must pour everything they have learned into their paper. Once completed it should represent of the student's intelligence and academic skills. That's a lot of pressure on one piece of work, with everything this on the line it's easy to run into writer's block. Fortunately, statistics students don't have to go it alone. This article will provide students with 20 statistics topics to inspire creativity.

Twenty Topics to Start With

Using Predictive Modeling To Prepare for Disease Outbreaks
Comparing Methods for Generating Probabilistic Forecasts
A Survey of Modern Linear Models
Models For Accurately Extrapolating From Historic Population Data
Analyzing Southeast Asian Typhoons Utilizing Statistical Models
Using Social Network Information In Epidemiology
Achieving Optimum Data Density
How Value-Added Modeling Impacts Test Scores Across Economic Divides
Looking At Climate Change Through Bayesian Probability
Accurately Calculating Percentiles
Modeling Standardized Test Scores Across The Globe
Using Hierarchical Modeling To Estimate Academic Results
Monte Carlo Models And Investment Portfolios
Looking At Health Data Through A Bayesian Lens
Statistical Analysis Of The Peer Review Process
Generating Population Structures From Social Network Data
Accurately Modeling Fragmented Data
Using Statistical Models To Predict Seasonal Spending
Minimizing Noise From Automatically Mined Data
Modeling Wildlife Populations Across Time

Putting these Topics to Use

A few things should stand out after looking at these 20 different titles. The first is that there isn't only one way to go about writing a statistics dissertation. Statisticians can use the tools of their trade to examine just about every aspect of human life. Some of the most productive theses arise from real-world problems. Examining pressing issues using the latest statistical tools is a powerful strategy. This approach allows students to build on something solid, so they don't get too lost in abstract theories. This strategy can also be a good way to prepare for a future career. Students will have the upper hand at companies related to their thesis topic. It's still fine to write about obscure statistical theories. This strategy is a wise choice for academics. Students who aim to join the private market should seek out more practical topics.

Creating Something Bold and New

It's important to remember that these topic ideas are just a starting point. No one should copy these verbatim. Instead, they should act as inspiration. Take them apart, mix them up, add a personal spin, there are a thousand ways to make a topic your own. In the end, a dissertation has to represent the unique skills of the student producing it. In this world, there is nothing new under the sun. Students don't have to reinvent statistics to start off their career. Using ideas like these as a starting point is acceptable. Just make sure you choose a topic that you feel confident about completing.

You might select a bad topic, poorly structure your paper, use inappropriate writing techniques, and so on. Before you start working on your project, you should make sure that each of your step is proper. You can learn everything today.

Thesis proposal about ethics
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UCF Graduate Programs Reach New Heights in U.S. News Rankings Through Research Excellence, Impactful Community Engagement

UCF’s emergency management program ranks No. 1 in the nation, and programs in education and public affairs climbed in U.S. News & World Report ’s Best Graduate Schools rankings.

By Mark Schlueb ’93 ’21MA | April 9, 2024

A man wearing a suit stand by a laptop with a stick that says UCF

UCF is a leading metropolitan research university known for helping students unleash their potential and advancing innovation in our community and state. Led by world-class faculty members with unrivaled industry experience, UCF’s graduate programs continue to earn top national recognitions for accomplishing those goals and more.

More than 9,000 UCF students enroll in UCF’s graduate programs to advance their careers or launch new ones. And many are thriving on campus and after graduation in programs ranked among the best in the nation.

U.S. News & World Report has recognized UCF’s exceptional faculty and graduate programs in its 2024 list of Best Graduate Schools. UCF’s emergency management program ranks No. 1 in the nation, and four programs rank in the top 25. Nine graduate programs placed in the top 50 nationally, including five in public affairs, three in education and one in health. U.S. News will release rankings for the engineering and medicine categories at a later date.

“UCF’s world-class faculty excel at providing our graduate students with the knowledge and skills they need to thrive as innovative leaders and creators,” says President Alexander N. Cartwright. “The U.S. News rankings demonstrate that our students graduate well-prepared to unleash their potential in individual, business, and government sectors that are growing in Florida and vital to our economy, health, and quality of life.”

UCF Grads Shape Emergency Responses Nationwide

UCF has a proven track record in emergency management. The university’s Master of Emergency and Crisis Management program — which is offered the College of Community Innovation and Education — has climbed the rankings over seven consecutive years. The homeland security program and its faculty researchers enable students to navigate increasingly complex manmade and natural disasters, while learning from past disasters to improve their preparedness and response in the future.

Graduates of the program go on to become leaders in directing and implementing emergency responses in Florida and throughout the country, including in Boston and Washington, D.C. They are saving lives, helping communities prepare as well as possible to navigate disasters, and putting into practice the lessons they learned from outstanding faculty who contribute to national research and regional solutions related to crises .

“Our students are equipped to assist communities and organizations in every phase of emergency management — from preparedness and mitigation to response and recovery,” says Claire Connolly Knox, professor in UCF’s School of Public Administration.

“We are thrilled to be ranked No. 1 and nationally recognized again as a leader in emergency and crisis management,” she adds. “This honor highlights the innovative and community-focused research by our faculty and continuous engagement with community partners invested in our outstanding students and alumni.”

Other highlights include:

12 in Education — Student Counseling and Personnel Services
15 in Public Affairs — Nonprofit Management, up three spots since last year
21 in Public Affairs — Public Management and Leadership, up five spots since last year
27 in Public Affairs — Public Finance and Budgeting
32 in Education — Curriculum and Instruction
41 in Health — Physical Therapy
42 in Best Education Schools, up four spots since last year
47 in Public Affairs
59 in Nursing — Doctor of Nursing Practice

UCF’s many strong rankings are a testament to the excellence of UCF’s faculty, who bring to the classroom extensive experience in academia, industry and research, as well as to the university’s commitment to help students unleash their potential in a culture focused on collaboration and finding solutions that benefit our society.

UCF students who have graduated from the nonprofit management program have gone on to make a big impact by helping communities in Florida and beyond. In one example, more than 12 years ago, program graduate Eric Camarillo ’16 ’19MNM launched faith-based nonprofit organization SALT Outreach Inc. in Central Florida to help provide services to the homeless, including mobile shower trailers, laundry, clothing, haircuts, mail services and help with employment. SALT has grown to more than 30 staff members who help hundreds of people every day.

“Throughout the School of Public Administration, our faculty, staff and advisory boards have worked hard to ensure we are offering students in Central Florida, across the country and around the globe a world-class, innovative education,” says Doug Goodman, professor and school director. “We are honored to be recognized as leaders in emergency management, nonprofit management, public leadership management and public finance and budgeting, fields that are critical to the health and well-being of our citizens and the success of our communities.”

The Best Graduate Education Schools category includes graduate-level educator preparation and advancement programs, such as teacher education, school counseling and psychology, educational leadership, and curriculum and instruction, all offered through the College of Community Innovation and Education. The college offers graduate students numerous opportunities to collaborate closely with expert faculty, from receiving mentorship and support in research and scholarship to engaging in robust internships and field experiences with school district, nonprofit and agency partners. Some faculty members also lead federally funded projects that offer tuition assistance and prepare students to work with students in high-need schools.

UCF’s continued rise has also drawn praise from other outlets:

In February, U.S. News & World Report released its best online program national rankings, which placed UCF tied at No. 7 in the nation for best online bachelor’s programs. Of the 14 UCF national rankings from U.S. News , six online programs made the top 10, two made the top 15 and three were in the top 50. UCF has ranked in the top 20 overall Best Online Programs for the past seven years.
In March, The Princeton Review and PC Gamer recognized UCF’s game design programs among the best in the world. The graduate Florida Interactive Entertainment Academy is ranked No. 1 in the world for the fourth time in five years. The undergraduate game design program, Games and Interactive Media (GaIM) in UCF’s Nicholson School of Communication and Media, achieved its highest ranking ever, advancing to No. 5 in the world.
Sports Business Journal named Orlando the No. 1 Best Sports Business City for event hosting, including the NBA, Orlando City and Orlando Pride Soccer, the nation’s premier tennis center, college football bowl games, the NFL Pro Bowl, U.S. Olympic Marathon Trials, the Arnold Palmer Invitational — and, of course, the UCF Knights. With its inaugural season in the Big 12 Conference in 2023, UCF has skyrocketed to unprecedented success as the youngest Power Four program in the country. With one of the country’s top graduate sports business programs , UCF has also provided a pipeline of talented graduates to some of the nation’s biggest sports brands.

Regions & Countries

About half of americans say public k-12 education is going in the wrong direction.

School buses arrive at an elementary school in Arlington, Virginia. (Chen Mengtong/China News Service via Getty Images)

About half of U.S. adults (51%) say the country’s public K-12 education system is generally going in the wrong direction. A far smaller share (16%) say it’s going in the right direction, and about a third (32%) are not sure, according to a Pew Research Center survey conducted in November 2023.

Pew Research Center conducted this analysis to understand how Americans view the K-12 public education system. We surveyed 5,029 U.S. adults from Nov. 9 to Nov. 16, 2023.

The survey was conducted by Ipsos for Pew Research Center on the Ipsos KnowledgePanel Omnibus. The KnowledgePanel is a probability-based web panel recruited primarily through national, random sampling of residential addresses. The survey is weighted by gender, age, race, ethnicity, education, income and other categories.

Here are the questions used for this analysis , along with responses, and the survey methodology .

A diverging bar chart showing that only 16% of Americans say public K-12 education is going in the right direction.

A majority of those who say it’s headed in the wrong direction say a major reason is that schools are not spending enough time on core academic subjects.

These findings come amid debates about what is taught in schools , as well as concerns about school budget cuts and students falling behind academically.

Related: Race and LGBTQ Issues in K-12 Schools

Republicans are more likely than Democrats to say the public K-12 education system is going in the wrong direction. About two-thirds of Republicans and Republican-leaning independents (65%) say this, compared with 40% of Democrats and Democratic leaners. In turn, 23% of Democrats and 10% of Republicans say it’s headed in the right direction.

Among Republicans, conservatives are the most likely to say public education is headed in the wrong direction: 75% say this, compared with 52% of moderate or liberal Republicans. There are no significant differences among Democrats by ideology.

Similar shares of K-12 parents and adults who don’t have a child in K-12 schools say the system is going in the wrong direction.

A separate Center survey of public K-12 teachers found that 82% think the overall state of public K-12 education has gotten worse in the past five years. And many teachers are pessimistic about the future.

Related: What’s It Like To Be A Teacher in America Today?

Why do Americans think public K-12 education is going in the wrong direction?

We asked adults who say the public education system is going in the wrong direction why that might be. About half or more say the following are major reasons:

Schools not spending enough time on core academic subjects, like reading, math, science and social studies (69%)
Teachers bringing their personal political and social views into the classroom (54%)
Schools not having the funding and resources they need (52%)

About a quarter (26%) say a major reason is that parents have too much influence in decisions about what schools are teaching.

How views vary by party

A dot plot showing that Democrats and Republicans who say public education is going in the wrong direction give different explanations.

Americans in each party point to different reasons why public education is headed in the wrong direction.

Republicans are more likely than Democrats to say major reasons are:

A lack of focus on core academic subjects (79% vs. 55%)
Teachers bringing their personal views into the classroom (76% vs. 23%)

A bar chart showing that views on why public education is headed in the wrong direction vary by political ideology.

In turn, Democrats are more likely than Republicans to point to:

Insufficient school funding and resources (78% vs. 33%)
Parents having too much say in what schools are teaching (46% vs. 13%)

Views also vary within each party by ideology.

Among Republicans, conservatives are particularly likely to cite a lack of focus on core academic subjects and teachers bringing their personal views into the classroom.

Among Democrats, liberals are especially likely to cite schools lacking resources and parents having too much say in the curriculum.

Note: Here are the questions used for this analysis , along with responses, and the survey methodology .

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‘Back to school’ means anytime from late July to after Labor Day, depending on where in the U.S. you live

Among many u.s. children, reading for fun has become less common, federal data shows, most european students learn english in school, for u.s. teens today, summer means more schooling and less leisure time than in the past, about one-in-six u.s. teachers work second jobs – and not just in the summer, most popular.

About Pew Research Center Pew Research Center is a nonpartisan fact tank that informs the public about the issues, attitudes and trends shaping the world. It conducts public opinion polling, demographic research, media content analysis and other empirical social science research. Pew Research Center does not take policy positions. It is a subsidiary of The Pew Charitable Trusts .

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