Ph.D. Advanced Data Science Option

The Advanced Data Science option aims to educate the next generation of thought leaders who will both build and apply new methods for data science. This option will help to educate and recognize PhD students whose thesis work focuses specifically on building and using advanced data science tools. The goal of this option is not to educate all students in the foundations of data science but rather to provide advanced education to the students who will push the state-of-the-art in data science method.

The Advanced Data Science option replaces the previous Big Data track introduced in 2014. This is an official UW degree option which will appear on your transcript.

Students enrolled in this option can expect to interact with students enrolled in similar Advanced Data Science PhD options in Genome Sciences, Statistics, Oceanography, Chemical Engineering and Astronomy.  Formally, the option is affiliated with an NSF IGERT training award in Big Data , and PhD students in the track are eligible for funding via that award. 

Description

The Advanced Data Science Option is an overlay on top of our regular quals requirements. Hence students must make sure to satisfy the regular quals requirements in addition to the option requirements. The latter impose some extra constraints on the course selection. These constraints apply to both pre-quals and post-quals courses, and are designed to help you organize your coursework towards research in Big Data.

Course requirements

1. Quals-level requirements

Successfully complete the department's PhD qualifying coursework requirements, and  satisfactorily complete three out of four of the following core courses in Big Data ( some of the courses listed below may also be counted towards the qualifying coursework requirements, if allowable in the standard requirements) :

- Data Management : CSE 544 (satisfies the “Programming Systems” quals category).

- Machine Learning : CSE 546 (satisfies the “AI” quals category)

- Data Visualization : CSE512 (satisfies the “Applications” quals category).

- Statistics : STAT 509 (Introduction to Mathematical Statistics).  Alternatively, for a more advanced sequence, you may choose to take STAT 512 (Statistical Inference), but, in this case, we strongly recommend that you also take STAT 513, the second course in this sequence.  

In general, quals course waivers may not be applied in lieu of one of these core Big Data courses.  However, a student may petition to substitute a requirement by *a more advanced course in that area, taken at UW*.  Petitions should be sent to Magda Balazinska.  

2. Post-quals requirements

Satisfactorily complete one additional course with explicit emphasis on advanced “Big Data” techniques:

  • A fourth core course from the list above
  • CSE 547 / STAT 548 - Machine Learning for Big Data
  • STAT 513 - Statistical Inference
  • A new Big Data Management course planned for for the future (Magda Balazinska and Dan Suciu)
  • EE 578 - Convex Optimization
  • STAT 527 - Nonparametric Regression and Classification
  • STAT 538 - Advanced Statistical Learning
  • CSE 552 - Distributed and Parallel Systems Data
  • CSE 599C -  Big Data Management Systems (Spring 2017 offering)

3. eScience Community Seminar

To further expand students’ education and create a campus-wide community, students will register for at least 4 quarters in the weekly eScience Community Seminar .

4. eScience Institute

Students interested in data science should also check out other activities that we are carrying out in the eScience Institute . The eScience Community Seminar is one of those activities. Other relevant activities include various tool and method-oriented workshops as well as speaker series. Visit http://data.washington.edu for more information

In addition, the track is designed to complement the activities of the eScience Institute and to leverage ongoing activities associated with the Moore/Sloan Foundation Data Driven Discovery Initiative , involving the University of Washington, New York University and the University of California, Berkeley.

CSE Ph.D. students who choose to enroll in the Advanced Data Science Option must have approval of their research advisor. Email this approval to the Graduate Program Advisor (Elise Dorough, elised@cs ). There is no additional admission procedure.

If you have any questions about the Advanced Data Science Option, please email Magda Balazinska.

phd data science university of washington

Master’s & Ph.D.

phd data science university of washington

  • Graduate Data Science Option (DSO)

The Data Science Option (DSO) is designed to meet a critical educational gap that views Data Science from an Electrical & Computer Engineering (ECE) viewpoint. With the new DSO option, the ECE graduates will be equipped to tackle modern engineering challenges using large datasets, machine learning, statistical inference and visualization techniques. Building on ECE fundamentals of statistical signal processing and controls, the ECE DSO will provide students with a strong foundation in the field of data science, developing critical knowledge and skills to apply a variety of modern data analysis techniques and tools to advance and accelerate ECE research and applications.

The DSO is intended for students with little or no background in data science, computer science or coding. The option is based on a framework developed by the University of Washington  eScience Institute . The eScience Institute empowers UW researchers and students in all fields to answer fundamental questions through the use of large, complex, and noisy data, providing expertise to leverage data science tools, methods and best practices in their research and education.

Overview of ECE DSO Requirements

The requirements to receive the ECE DSO are as follows:

  • Complete one course from each of the following areas: a. Statistics b. Machine learning c. Data manipulation d. Department-specific requirement
  • Participate in 2 quarters of the 1-hour UW Data Science Seminar
  • Fulfill all of the standard ECE and graduate school degree requirements a. https://www.ece.uw.edu/academics/grad/ b. The Departmental requirements may be fulfilled simultaneously with the Data Science Option requirements.

This new degree option in both the Master of Science program and the Doctor of Philosophy program in Electrical Engineering. The Master of Science in Electrical Engineering program requires a total of 42 credits and the Doctor of Philosophy program requires a total of 90 credits, as outlined here: https://www.ece.uw.edu/academics/grad/

Details of implementation within the current degree requirements are outlined below. In all cases, the option will be associated day-time programs offered at the Seattle campus. The delivery mechanism will be in-person classroom (in some cases using a flipped classroom), unless public health considerations require online delivery.

For both programs, the expected learning outcomes of the Data Science Option include: an understanding of the theory underlying foundational concepts in machine learning, knowledge of experimental practices in data science, practical experience working with current algorithms and large data sets, and an appreciation for ethical issues associated with design and deployment of systems that leverage machine learning. Because the requirements and learning goals are similar, students may not receive the DSO for both a UW MS and ECE Ph.D. degrees.

Degree Requirements for the Master of Science in Electrical Engineering (Data Science):

The proposed Data Science Option shares all requirements of the standard MSEE degree, but contains a total of 17-19 credits of distinct requirements which are not part of the standard MSEE. The full requirements are below, with distinct data science requirements highlighted.

Thesis Option (42 credits total)

The thesis option requires completing 42 credits, including:

  • Excludes seminar courses
  • Excludes EE 599
  • 4-8 credits of EE 599 numerically graded research credits
  • 9-13 credits of EE 700 required
  • 1 credit EE 500 required
  • 2 credits maximum of non-EE seminars
  • 2 credits “Topics in Data Science” Seminar CHEM E 599 required
  • 4 credits maximum at the 300-level
  • 4 credits maximum of ENGR 601
  • At least 30 credits total required at the 5xx level or higher

Machine Learning

Data manipulation.

  • Department-specific Data Science requirements
  • Thesis: A written thesis must be submitted by the student for approval by the Master’s Supervisory Committee, followed by a final oral examination.

Coursework Option

The coursework option requires completing 42 credits, including:

  • 8 credits maximum of EE 599 numerically graded research credits
  • 2 credits “Topics in Data Science” CHEM E 599 required
  • 4 credits maximum at the 300 level

Degree Requirements for the Ph.D. in Electrical Engineering (Data Science):

The proposed Data Science Option shares all requirements of the standard Ph.D. degree, but contains a total of 17-19 credits of distinct requirements which are not part of the standard Ph.D. The full requirements are below, with distinct data science requirements highlighted.

Ph.D. Degree Requirements

  • 90 credits as a graduate student; at least 60 credits must be completed at the University of Washington.
  • EE 500 Seminar: 1 credit is required (4 maximum)
  • EE 599: 5 credits maximum may be applied
  • EE 800: 27 credits dissertation research over at least three quarters
  • Successful completion of the Qualifying Exam, Generals Exam & Dissertation Defense
  • The dissertation should incorporate elements of data science, which may include advances in data science methods such as machine learning and/or application of data science methods to research questions in Electrical and Computer Engineering.

Course Lists for Data Science Option

Students should confirm course availability and credits on the UW time schedule.

Complete at least one course from each of the following classes:

ECE options

Seattle campus options, department-specific requirements as related to data science.

Students should contact the ECE Graduate Program Coordinator to request review and approval for any new or existing courses not on these lists.

UW Data Science Seminar

In addition to the course requirements listed above, students must also participate in 2 quarters of the 1-credit U W Data Science Seminar . This is an informal environment for presentations and discussions. Topics span science, methods, and technology across the mission of the eScience Institute.

  • Contact Advising
  • [email protected]
  • Current ECE students can schedule an appointment with an adviser  online.
  • Admissions Requirements
  • Degree Requirements — MSEE
  • Degree Requirements — Ph.D.
  • Tuition & Fees
  • Prospective Graduate Student Resources

Be boundless

© 2024 University of Washington | Seattle, WA

UW Bioengineering logo

INVENTING THE FUTURE OF MEDICINE

PHD Data Science Option

Students studying together

Data Science Option

PhD students have the opportunity to pursue their PhD with a Data Science option. The Data Science option prepares the next generation of thought leaders to both apply new data science methods and build new data science tools. It recognizes Ph.D. students whose thesis work focuses specifically on advanced data science tools and provides an advanced education to students who will push the state-of-the-art in data science methods, such as developing new machine learning methods.

Students enrolled in this option can expect to interact with students in similar programs in genome sciences, statistics, oceanography, computer science & engineering and astronomy. The Data Science option is an official UW degree option which will be part of your degree title and appear on your transcript.

The Data Science Option overlays our standard course requirements. In other words, students must satisfy the universal PhD Curriculum requirements, in addition to the Data Science Option requirements. This may impose some extra constraints on course selection. However, note that some of the required data science courses may substitute for required electives.

Required Curriculum:

  • 2 credits of eScience seminar, currently offered as CHEME 599, Topics in Data Science (1 credit, CR/NC).
  • Three courses totaling 9-14 credits from three of the five categories listed below: Scientific Computing, Statistics and Machine Learning, Big Data and Image Processing, Data Visualization, and Data Science. Each category listed includes approved courses offered within Bioengineering and other departments on the UW Seattle campus. The large number of approved courses reflects both the breadth of the data science field, and the fact that space in non-Bioengineering courses is sometimes limited.

Scientific Computing

Ph.D. students who choose to enroll in the Data Science Option must have approval of their research advisor. Email this approval to the Graduate Program Advisor, Kalei Combs, [email protected] . There is no additional admission procedure.

  • Public Lectures
  • Faculty & Staff Site >>

Data Science

The Master of Science in Data Science at the University of Washington gives current and aspiring data science professionals the technical skills to turn large, messy data sets — or big data — into insights people and organizations can use.

Program Website

Degree(s)/Certificate(s) offered

  • Master of Science in Data Science

Program director/interdisciplinary group co-chairs

  • Adrian Dobra, Professor, Department of Statistics and School of Nursing

Interdisciplinary Faculty Group Membership

The following are the core/voting Graduate Faculty members of the interdisciplinary group. For a complete list of faculty active in the program, see the program website.

  • Cecilia Aragon, Professor, Department of Human Centered Design & Engineering 
  • Bob Boiko, Teaching Professor, Information School
  • Brock Craft, Associate Teaching Professor, Department of Human Centered Design & Engineering
  • Lurdes Inoue, Professor, Department of Biostatistics
  • Nathan Kutz, Professor, Department of Applied Mathematics
  • Brian Leroux, Professor, Department of Biostatistics and Department of Oral Health Sciences
  • Abel Rodriguez, Professor, Department of Statistics
  • Eli Shlizerman, Assistant Professor, Department of Applied Mathematics and Department of Electrical Engineering
  • Dan Suciu, Professor, School of Computer Science & Engineering
  • Yuliang Wang, Research Assistant Professor, School of Computer Science & Engineering
  • Nicholas Weber, Assistant Professor, Information School

UW Block W

Data Science Options

  • > graduate
  • > areas
  • > data-science-options

The Data Science options provide students an introduction to the world of data science, giving them the skills to understand a variety of techniques and tools. The goal of this option is to educate all students in the foundations of data science, so they may apply those methods and techniques in current research. There are two options, the Data Science Option (DSO) and the Advanced Data Science Option (ADSO). If you have questions about the DSO/ADSO or getting into classes, please speak with Ariel Rokem ( [email protected] ).

Eligible students for the DSO and ADSO include all full time Ph.D. students in the Psychology program in good standing who have completed the first year statistics requirements and who have permission of their advisors. Please complete the DSO/ADSO form , have your advisor and co-advisor sign it, and return it to the Grad Program Advisor by the time you complete your first DSO/ADSO course. If your advisor will not approve your request to do the DSO/ADSO, a student can write a letter of appeal to the GTC.

In cases where a graduate student does not graduate but instead leaves only after completing the master’s part of the graduate program, the student will retain the “Data Science option” or “Advanced Data Science option” recognition on their transcript if the student finishes all the requirements of the option before leaving the graduate program.

The Data Science Option (DSO)

The Psych DSO is designed for students with little or no background in data science, computer science or coding. The total credit requirement is 6-8 credits in courses (2 courses @ 3-4 credits each) and 2 seminar credits (plus the usual Psych 522-525 sequence). Note that one of the data science courses may count as your third required quantitative course for the Psychology PhD if approved by the Psychology quant area, resulting in only one “extra” course (and 2 quarters of seminar) beyond the usual Psychology quantitative requirements.

The “third” course can also count toward a Quant minor but all other classes must be unique if a student opts for the DSO and Quant minor.

The requirements for the Psych DSO are as follows:

  • Courses from two out of three of the following areas:
  • Software Development for Data Scientists (CSE 583)
  • Software Engineering for Molecular Data Scientists (ChemE 546)
  • Introduction to Machine Learning (CSE 416/STAT416)
  • Nonparametric regression and classification (STAT 527)
  • Machine Learning: (CSE 546 or STAT 535) also serves for the “Advanced Data Science Option”
  • Introduction to Mathematical Statistics (STAT 509) and Statistical Inference (STAT 512-513) also serves for the “Advanced Data Science Option”
  • Introduction to Database Systems (CSE 414)
  • Information for Visualization (HCDE 411/511)
  • Principles of DBMS (CSE 544) also serves for the “Advanced Data Science Option”
  • Data Visualization (CSE 512) also serves for the “Advanced Data Science Option”
  • 2 quarters of the eScience Community Seminar ( https://escience.washington.edu/connect/events/uwdss/ )
  • Fulfillment of the Psychology Department Statistics and General Methodology requirements. These are currently the following (students must achieve a 2.7 or above on each). Psych 522, Psych 523, Psych 524, Psych 525 (If a student places out of one or more of these, they must take a higher-level course in its place and it cannot count as another of the DSO courses). Note that one of the data science courses may count as your third required quantitative course for the Psychology PhD if approved by the Psychology quant area, resulting in only one “extra” course (and 2 quarters of seminar) beyond the usual Psychology quantitative requirements.

Advanced Data Science Option (ADSO)

The “Advanced Data Science” option aims to educate the next generation of thought leaders who will both build and apply new methods for data science. This option will help to educate and recognize PhD students whose thesis work focuses specifically on building and using advanced data science tools. The goal of this option is to provide advanced education to the students who will push the state-of-the-art in data science methods in the domain of Psychology.

The ADSO is designed for students with a significant CS background. It is strongly recommended that you look carefully at class syllabi before requesting the ASDO option.

The total credit requirement is 10-12 credits in courses (3 courses @ 3-4 credits each) and 4 seminar credits (plus the usual Psych 522-525 sequence). Note that one of the data science courses may count as your third required quantitative course for the Psychology PhD if approved by the Psychology quant area, resulting in only two “extra” courses (and 4 quarters of seminar) beyond the usual Psychology quantitative requirements. The course that counts as the third quant course for the Psych PhD can also count toward a Quant minor however all other courses need to be unique for the ADSO and Quant Minor.

The requirements for the Psych ADSO are as follows:

  • Three out of the following four courses:
  • Principles of DBMS: CSE 544.
  • Machine Learning or Statistical Learning, CSE 546 or STAT 535.
  • Data Visualization: CSE 512.
  • Introduction to Mathematical Statistics or Statistical Inference: STAT 509 or STAT 512-513.
  • 4 quarters of the eScience Community Seminar ( https://escience.washington.edu/connect/events/uwdss/ )
  • Fulfillment of the Psychology Department Statistics and General Methodology requirements. These are currently the following (students must achieve a 2.7 or above on each). Psych 522, Psych 523, Psych 524, Psych 525 (If a student places out of one or more of these, they must take a higher level course in its place and it cannot count as another of the DSO courses). Note that one of the data science courses may count as your third required quantitative course for the Psychology PhD if approved by the Psychology quant area, resulting in only one “extra” course (and 2 quarters of seminar) beyond the usual Psychology quantitative requirements.

The option is ‘accredited’ by the graduate school and goes on your official graduate transcript.

The quant minor is a psychology department offering focused on the kinds of statistics utilized within psychology and related fields, whereas the data science option is focused on statistical learning from a machine learning perspective which includes topics like database management and software development.

Most of the 500 level classes in the statistics and machine learning domain of the DSO will ALSO count for credit for the quant minor. Most of the classes in the topics of software development, data management or data visualization will not count towards the quant minor. If you have questions about whether a class will count towards your quant minor please email Brian Flaherty ( [email protected] )

Expect classes to be heavily subscribed. Register early. If you are really running into difficulties, then email Ariel Rokem ( [email protected] ) explaining which class you need to take.

Many classes have pre-requisites. You will need to contact the instructor to ask if you can get those waived or you will need to take the pre-requisites.

Where is the declaration form?

You can download it here . Remember, this form is for you to declare the option with your existing program of study and that you have approval from your advisors (and Ariel Rokem, [email protected] , for the advanced option). There are additional requirements to actually complete the option and have it appear on your transcript. Return the completed form to the Grad Program Advisor.

Subscribe to our updates

  • Directories

IGERT PhD Program in Big Data and Data Science at the UW

The path to deep scientific discoveries is changing rapidly. Most disciplines, from physical to life sciences, have entered an era, where discovery is now no longer limited by the collection and processing of data, but by the management, analysis, and visualization of this information. From studying the building blocks of life, to understanding the nature of our universe, transformative breakthroughs are increasingly dependent on our ability to interrogate complex data streams from instruments distributed on a global scale.  

To be successful, the next generation of scientists needs to be deep in both their own field as well as computer science and statistics. Similarly, the next generation of computer scientists and statisticians needs to understand deeply the real needs of domain scientists. Students can no longer develop tools and models in isolation, because the resulting “hammers” fail to meet the growing needs of the data-enabled sciences.

To address these challenges and to educate the next generation of scientists, the University of Washington offers PhD programs specialized in Big Data and Data Science in various departments as well as an integrative program that crosses department boundaries.

Learn more :

Integrative Graduate Education and Research Traineeship (IGERT) in Big Data and Data Science

Participating Departments and Department-Specific Programs

IGERT Program Pre-requisites (visit this page to learn about possible background courses in preparation for the main IGERT courses).

IGERT Leadership

IGERT Students

Current students

Ph.d.–advanced data science option.

Doctor of Philosophy (Mechanical Engineering: ADV DATA SCI) provides students an introduction to the world of data science, giving them the skills to understand a variety of techniques and tools. This option will help to educate and recognize PhD students whose thesis work focuses specifically on building and using advanced data science tools.

The requirements for the Doctor of Philosophy (Mechanical Engineering: ADV DATA SCI) are as follows:

I. Three out of the following four courses

Ii. escience community seminar.

  • 4 quarters of the eScience Community Seminar  OR ME Data Drive seminar with Professor Steve Brunton. Seminar credits do not count towards graduation requirements. 

III. Fulfillment of the Mechanical Engineering requirements

In addition , all students are required to take at least one additional course in quantitative methods (statistics, applied mathematics, mathematics, or computational science) OR in a methodology directly relevant to their area of focus. Such courses are to be specified in each student’s Individualized Training Plan.

Students may not count any course toward both the ME coursework requirements and the Data Science requirements. For example, if students take ME 574 and count it toward the computational or numerical analysis requirement, they cannot use this course to fulfill the Data Science requirement. Students must ensure that there’s a minimum of 9 distinct credits taken for the Data science option.

  • Directories

Search form

You are here.

  • Programs & Courses
  • Graduate Student Guide

PhD Advanced Data Science Option

Updated on March 7, 2022

PhD students who wish to participate in the Advanced Data Science Option must meet the following requirements, in addition to the requirements for the PhD :

Additional Requirements

  • Pass three of the following four courses:
  • Either STAT 509 : Introduction to Mathematical Statistics, or for a more advanced approach, STAT 512 : Statistical Inference. In the second case, students should consider also taking STAT 513 , the next course in this sequence.
  • CSE 546 / STAT 535 : Foundational Machine Learning
  • CSE 544 : Data Management
  • CSE 512 : Data Visualization
  • Students must register for four quarters in the weekly UW Data Science Seminar , also known as CHEM E 599 .

Recommended Study

It is recommended that students take a graduate-level course in linear algebra.

Declaring This Option

Talk with your advisor and then email [email protected] stating your intent to pursue this option.

  •   News Feed
  •   Alumni Update
  •   Mailing List

Current students

Data science option.

The Data Science Option (DSO) equips Ph.D. students to tackle modern civil and environmental engineering challenges using large datasets, machine learning, statistical inference and visualization techniques.

The DSO is designed to meet a critical educational gap at the intersection of Civil & Environmental Engineering (CEE) and data science allowing Ph.D. students to hone modern data analysis skills that are critical for advancing research and other applications.

The DSO is intended for students with little or no background in data science, computer science or coding. The option is based on a framework developed by the University of Washington eScience Institute . The eScience Institute empowers UW researchers and students in all fields to answer fundamental questions through the use of large, complex, and noisy data, providing expertise to leverage data science tools, methods and best practices in their research and education.

CEE DSO requirements

The requirements for the CEE DSO are as follows:

Software development for data science

Statistics and machine learning, data management and data visualization.

  • Department specific requirement
  • Participate in two quarters of the 1-hour eScience Community Seminar
  • Departmental requirements may be simultaneously fulfilled with Data Science Option requirements.

Eligibility

Full-time students in the CEE Ph.D. program who are in good standing are eligible for the DSO. At this time, the DSO is not available for the master’s degree program.

Ph.D. students may declare interest in pursuing the DSO by contacting the CEE graduate adviser . The student’s primary research adviser must approve the application.

A minimum of 11 credits is required (3 credits from three out of four areas and 2 credits for an eScience seminar). The 11 total credits for the DSO will count toward the minimum of 90 credits required for the CEE Ph.D. program .

Students complete courses from three out of four areas, which are detailed below. Each area lists current courses offered within CEE as well as other departments on the UW Seattle campus that satisfy the requirement.

CEE options

Seattle campus options, department specific requirement as related to data science.

Students should contact the CEE DSO oversight committee to request review and approval for any new or existing courses not on the lists above.

eScience Community Seminar

In addition to the course requirements listed above, students must also participate in 2 quarters of the 1-credit eScience Community Seminar. This is an informal environment for presentations and discussions. Topics span science, methods, and technology across the mission of the eScience Institute. See eScience Community Seminar for additoinal details.

University of Washington

eScience Institute

  • Data Science at UW
  • Learning Data Science /

phd data science university of washington

Data Science Education at the UW: A University-Wide Approach

Our society’s ability to generate data is growing at an unprecedented scale and rate. Advances in scientific instruments, the growing availability and sophistication of environmental sensors, the explosion of social media and web content, the growth in mobile devices and web applications that drive them, and access to increasingly powerful and inexpensive compute and storage resources are driving a growing number of disciplines to become data rich. Data analysis is at the heart of many scientific advances and industrial innovations and  data science  has emerged as a new discipline that college graduates must master.

At the University of Washington, we are deeply committed to ensuring the success of  all  our graduates and we consider data science to be a critical component of student education whatever major they choose to pursue. We have identified a core set of skills that form the heart of data science education but also recognize that data science education needs vary across disciplines.

Our vision has lead us to adopt an approach to data science education that takes the form of  specializations within existing majors . These specializations are called “options” and are transcriptable in the sense that the “Data Science” specialization is listed on student transcripts. At UW, we expect students to pick their discipline of interest and, in the context of that discipline, acquire all necessary skills and knowledge to both utilize data science methods and contribute to the state-of-the-art in data science.

While not all majors on campus offer data science specializations today, the number of such disciplines is growing quickly.

Additionally, at the University of Washington, we offer data science specializations at both the undergraduate and graduate levels in addition to also offering a separate data science master’s degree. Find a list of our course offerings  here  on our website.

Inter-Disciplinary Data Science Specializations 

To develop data science specialization in a flexible yet coordinated fashion, faculty from different departments, colleges, and schools on campus worked together under the umbrella of the eScience Education Working Group. The developed programs are such that new departments can easily join and offer data science specializations within their majors.See below for the undergraduate and graduate specialization details.

How to Join 

The process for new departments to join the program and offer the data science specializations below at the graduate and undergraduate levels is straightforward. Interested departments should contact  David Beck  or  Sarah Stone .

phd data science university of washington

Undergraduate

The University of Washington offers several pathways for undergraduates interested in learning about data science. The Data Science Option brings together departments to create a template for scalable, university-wide program for a data science option for undergraduates. The Data Science Minor helps students leverage familiarity with data science, and gain skills and fluency to work with data in their major domain of study.

Departments offer two complementary data science specializations. The Data Science Option targets students who seek to learn data science methods and how to use data science tools. The Advanced Data Science Option targets students who seek to develop new data science methods and tools

phd data science university of washington

Professional Masters

University of Washington’s new Master of Science in Data Science gives current and aspiring data science professionals the technical skills needed to extract insights from large, noisy and heterogeneous datasets – and the practical skills to make analytics easy to understand and use.

Tutorials and Bootcamps

We offer ongoing tutorials, bootcamps, hackathons, and other short education events. We’re also a regional partner with  Software Carpentry , a non-profit volunteer organization whose  members  teach researchers basic software skills.

phd data science university of washington

  • Make a Gift
  • Directories

Search form

You are here.

  • Programs & Courses
  • Ph.D. Program

Ph.D. in Chemistry with a Data Science Option (Chem-DSO)

The Data Science Option (DSO) is a set of extra requirements for students interested in data science. If completed, your degree title will be Doctor of Philosophy (Chemistry: Data Science). The goal of this option is to educate all students in the foundations of data science, so they may apply those methods and techniques in current research. The Chemistry DSO is designed for students with little or no background in data science, computer science or coding.

The requirements for the Chemistry DSO are as follows:

1)   All students must take the following three classes which are scheduled to be offered in an autumn/winter/spring sequence:

  • CHEM 541/MSE 542 Data Science and Materials Informatics (3 credits, autumn)
  • CHEM 542/MSE 543 Materials and Device Modeling (3 credits, winter) OR CHEM 565 – Computational Chemistry ( 3 credits , winter )
  • CHEM 543/MSE 544 Big Data for Materials Science (3 credits, spring)

2)   Students must register for and attend at least three quarters of the following interdisciplinary and community seminars in order to further expand their education and exposure to a diverse range of research topics at the nexus of Chemistry and Data Science:

  • MOLENG 520/CHEM 597 Molecular Engineering Institute Seminar
  • MOLENG 599A Seminar in Clean Energy (Clean Energy Institute Seminar
  • CHEME 599 Current Topics in Data Science (eScience Community Seminar)

A student may choose any combination of the three they wish, and requirements from other training programs (e.g., Clean Energy Institute (CEI) graduate fellows required to take the CEI seminar) may count toward this requirement.

 TOTAL CREDITS

 The total credit requirement for the Chem-DSO is 9 credits in graded coursework (3 courses with 3 credits each). The current Ph.D. requirements in Chemistry include 18 credits of graded coursework. Depending on the specific sub-division requirements, these 18 credits include a 9 or 12 credit core course sequence (3 or 4 classes) and 9 or 6 credits “for breadth” from a variety of options.

 Courses taken for the Chem-DSO are in addition to the 18 credit requirement for the Chemistry Graduate Ph.D. Program (see Table 1 below).

  Full requirements to earn the Doctor of Philosophy (Chemistry: Data Science)

 Research progress requirements

  • 2 nd -year qualifying examination
  • General examination
  • D. dissertation (written thesis and oral defense)

 90 credits of coursework (minimum)

  • Required coursework (18 credits): including 9-12 credit core course sequence and 6-9 credits from breadth options
  • CHEM 541/MSE 542
  • CHEM 542/MSE 542 OR CHEM 565
  • CHEM 543/MSE 544
  • Alternative courses available subject to approval by advisor
  • Department of Chemistry general seminar (1 credit/quarter, max 18, no total credit requirement) CHEM 590 Seminar in General Chemistry
  • Department of Chemistry  divisional seminars (1 credit/quarter, max 18, no total credit requirement)
  • CHEM 591 Seminar in Inorganic Chemistry
  • CHEM 592 Seminar in Analytical Chemistry
  • CHEM 593 Seminar in Organic Chemistry
  • CHEM 595 Seminar in Physical Chemistry
  • Department of Chemistry  current research seminars (1 credit/quarter, max 18, no total credit requirement)
  • CHEM 571 Current Research Topics in Inorganic Chemistry
  • CHEM 573 Current Research Topics in Organic and Biological Chemistry
  • CHEM 574 Current Research Topics in Spectroscopy
  • CHEM 575 Current Research Topics in Theoretical and Computational Chemistry
  • CHEM 578 Current Research Topics in Materials Chemistry
  • Data Science seminars (3 credits)
  • MOLENG 520/CHEM 597 Molecular Engineering Institute Seminar (1 credit/quarter, max 30)
  • MOLENG 599A Seminar in Clean Energy (1 credit/quarter, max 30)
  • CHEME 599 Current Topics in Data Science (1 credit/quarter, max 12)
  • Preparation for 2 nd year examination (9 credits) CHEM 581 (3 credits/quarter)
  • Research Credit (27 credits minimum)
  • CHEM 600: Independent Study or Research (no minimum credit requirement)
  • CHEM 800: Doctoral Dissertation (27 credits over 3 quarters, enroll after completing general exam)

Table 1. Typical path to 90 credits

Alternative Course Options

A course for the Chem-DSO may be replaced by an equivalent or more advanced course in the same area upon approval. A student must submit an email petition to their Faculty Adviser and the Graduate Program Coordinator for approval. The joint Chem/MSE course sequence is relatively new (as of 2021), and we expect that some current students will have completed equivalent courses offered by other departments. Appropriate courses include (but are not limited to) the following:

General CHEME 545/CHEM 545 – Data Science Methods for Clean Energy Research (3 credits)

Software Development CHEME 546/CHEM 546 – Software Engineering for Molecular Data Scientists (3 credits) CHEME 547/CHEM 547 – Data Science Capstone Project (3 credits) CSE 583 – Software Development for Data Scientists (4 credits) AMATH 581 – Scientific Computing (5 credits) AMATH 583 – High-Performance Scientific Computing (5 credits); Prerequisite: AMATH 581

Statistics and Machine Learning CSE 416/STAT 416 – Introduction to Machine Learning (4 credits); Prerequisites: (CSE 143 or CSE 160) and (STAT 311 or STAT 390) STAT 435 – Introduction to Statistical Machine Learning (4 credits); Prerequisites: either STAT 341, STAT 390/MATH 390, or STAT 391; recommended: MATH 308 CSE 546 – Machine Learning (4 credits) STAT 535 Statistical Learning: Modeling, Prediction, and Computing (3 credits) STAT 509 – Introduction to Mathematical Statistics: Econometrics I (5 credits) STAT 512-513 – Statistical Inference (4 credits each) AMATH 515 - Fundamentals of Optimization (5 credits) ATM S 552 – Objective Analysis (3 credits)

Data Visualization & Data Management CSE 414: Introduction to Database Systems (4 credits); Prerequisites: CSE 143 CSE 544 – Principles of DBMS (4 credits) CSE 442 – Data Visualization (4 credits); Prerequisite: CSE 332 CSE 412 – Introduction to Data Visualization (4 credits); Prerequisites: CSE 143 or CSE 163 CSE 512 – Data Visualization (4 credits) IMT 562 – Interactive Information Visualization (4 credits) INFO 474 – Interactive Information Visualization (5 credits); Prerequisites: INFO 343 or CSE 154; and CSE 143; and either Q METH 201, Q SCI 381, STAT 221/CS&SS 221/SOC 221, STAT 311, or STAT 390/MATH 390 HCDE/DATA 511 – Information Visualization/Data Visualization and Exploratory Analytics (4 credits) HCDE 411 – Information Visualization (5 credits) Prerequisites: HCDE 308 and HCDE 310 ESS 420 – Introduction to GIS for the Earth Sciences (5 credits) ESS 520 – Application in Geophysical Analysis with Python for the Earth Sciences (4 credits) AMATH 582 – Computational Methods for Data Analysis (5 credits) Prerequisite: either MATLAB and linear algebra OCEAN 502 – Marine Geospatial Information Science (3 credits) BIOL 519 – Data Science for Biologists (4 credits)

  •   Newsletter
  •   News Feed

Data Science Specialization

The Data Science Option is designed to meet a critical educational gap at the intersection of Biomedical & Health Informatics (BHI) and Data Science.  This option, as an elective part of our research MS and PhD program s, provides students an introduction to the world of data science, giving them the skills with a variety of techniques and tools. The goal of this option is to provide students the opportunity to acquire a strong foundation in data science, so they may apply those methods and techniques to current BHI research and to further their careers. This option is aligned with the campus-wide  eSciences Institute graduate-level educational offerings.

Regular Data Science Option:

To complete the regular data science option, students must pass

  • At least one course from each of the following three areas: (1) Software development for data science, (2) Statistics and machine learning and (3) Data management and data visualization. Courses in each of these categories are listed in the BHI graduate resources page.
  • Two quarters of the eScience Community Seminar  (1cr each quarter).

Advanced Data Science Option:

For students with a strong computer science background, or for those who want a more intensive experience, we also allow for the Advanced Data Science Option . In alignment with the eSciences Institute , those requirements are:

  • CSE 544 (4cr)– Principles of Data Management
  • CSE 546 (4cr)/STAT 535 (3cr) – Machine Learning
  • CSE 512 (4cr) – Data Visualization
  • STAT 509 (4cr)– Econometrics I; Intro to Mathematical Statistics or STAT 512 (4cr)-513 (4cr) (a more in-depth version of 509,4cr)
  • Four quarters of the eScience Community Seminar.

Upcoming Events

  • Graduate Studies
  • Machine Learning and Big Data PhD Track

Machine Learning and Big Data PhD Track Application Details

Before you are a UW Student Students interested in graduate study in Machine Learning and Big Data should apply directly to the  Statistics Department  via the online application located at  https://apps.grad.uw.edu/applForAdmiss/ . Entrance to the Machine Learning and Big Data track only happens after regular admission to the Statistics PhD program.

As a UW Statistics PhD student The PhD students who wish to opt for the MLBD track need to:

  • To apply to the track, please send e-mail to  Ellen Reynolds .
  • The Statistics ML/BD Track Committee will then review your application. Ellen will notify of the Committee's decision.
  • Four quarters of e-science community seminar
  • Tell  Ellen Reynolds  to mark them as ADS after the requirements are satisfied
  • If the requirements for the MLBD track and the additional ADS requirements are satisfied, then the student will have "ADS" listed on their transcript.

Frequently Asked Questions What if I change my mind? can I opt out of the MLBD track?   A student can change their mind any time without permission and opt to satisfy the requirements of the standard Statistics PhD track. It is expected that the student discusses this decision with their advisor(s) or with Michael Perlman and Ellen Reynolds, and that they notify Ellen of the change.  When must I make the MLBD choice?   Any time you want. Preferably when you have a clear idea what courses and what research you want to do during your PhD. We strongly encourage first-year students interested in the MLBD track to start with STAT 535.  If I have already taken CS classes elsewhere, can I waive the CS class requirement?   You can do this by asking for approval from the CS instructor for the class that you want waived. For example, Magda Balaszinska for Databases or Jeff Heer for Data Visualization. If your request is granted, forward the email to  Ellen Reynolds . Any appeals should be made in writing via e-mail to  Ellen Reynolds . It will be reviewed by the ML/BD track committee.

Master of Science in Data Science

Join one of the leading data science programs in the nation and accelerate your high-tech career in data science..

The MSDS degree is a professional master’s program designed for students who want to begin or advance their careers in data science. The program is available full-time or part-time. Classes begin every fall quarter and meet in the evenings on the University of Washington campus.

The industry-relevant curriculum gives you the skills to extract valuable insights from big data. In this program, you will learn expertise in statistical modeling, data management, machine learning, data visualization, software engineering, research design, data ethics, and user experience to meet the growing needs of industry, not-for-profits, government agencies, and other organizations.

Industry-Relevant Curriculum

The curriculum consists of eight core courses and a two-quarter capstone project. The capstone project gives students the opportunity to work on a data science challenge facing an external organization.

The MSDS program can be completed full-time or part-time. Full-time students take two courses per quarter and attend classes two evenings per week. The full-time program is 1.5 years in length. Part-time students take one course per quarter and attend class one evening per week. The typical part-time student completes the program in 2.5 years. Approximately 80 percent of MSDS students are full-time and 20 percent are enrolled part-time.

Discover if the full-time program or the part-time program is the best fit for you here .

High-Tech Careers

MSDS alumni work at top companies, including JP Morgan Chase & Co. , Parsons Corporation , IBM , Costco IT , Toyota , Northrop Grumman , and Zillow . Our graduates also pursue careers at leading not-for-profit organizations, such as  Seattle Children’s Hospital  and the  Institute for Health Metrics and Evaluation .

The MSDS program offers dedicated career services to students, including an annual Data Science Career Fair held every February. Learn more about our career outcomes and services on our Careers page .

phd data science university of washington

Diversity in Data Science

In the MSDS program, we have a student body made up of more than numbers. Our students have strong undergraduate grades and technical skills, but we also look for more than that when making a cohort. We admit students who have a diverse set of backgrounds and perspectives. Because of this, our program is able to offer a unique, vibrant experience.

The incoming cohort reflects this diversity. There are over 20 majors represented. Our incoming students have professional experience in a wide range of industries, including aerospace, energy, finance, healthcare, technology, telecommunications, and more. Of the students who will begin our program this fall; women make up almost half of the total. They also come from around the world, with 57% coming from eight different countries. The countries represented include Argentina, Chile, China, Ethiopia, India, Pakistan, Taiwan, and South Korea.

phd data science university of washington

The University of Washington

The University of Washington is one of the world’s preeminent universities. The UW is ranked No. 7 in the world in U.S. News & World Report’s Best Global Universities rankings.

The UW also has deep ties to the tech industry in the Seattle area and beyond. A UW degree provides alumni with a competitive edge on the job market.

Beyond its excellent academics, the University of Washington features one of the most beautiful campuses in the nation. Located just four miles from downtown Seattle, the campus offers stunning views of Mount Rainier and Lake Washington.

Admissions Timelines

Applications for Autumn 2024 admissions are now closed.

Decisions Release Date: Mid-March 2024 (no early decisions granted)

Admissions Updates

Be boundless, connect with us:.

© 2024 University of Washington | Seattle, WA

logo"> Department of Atmospheric Sciences

  • College of the Environment
  • University of Washington

KMS PICO Tool Win&Office

Site Official KMSPICO DOWNLOAD Absolutely FREE!!!

Doctor of Philosophy in Atmospheric Sciences

The degree of Doctor of Philosophy signifies understanding of the nature of knowledge normally attained only through the original solution of a problem of substantial scientific importance.

All students admitted into the Atmospheric Sciences graduate program will be admitted initially to the M.S. track of study. Students in the M.S. track who seek entry into the PhD program will be evaluated by the  COGS  (Committee on Graduate Studies) on the basis of their master’s thesis, defense of master’s research, and coursework. A student must qualify for study toward the PhD by being nominated to COGS by their M.S. Supervisory Committee and then approved by COGS for admission into the PhD. Immediately upon qualifying for PhD study, a student will form a PhD Supervisory Committee with a minimum of five members. The student and the PhD Supervisory Committee will jointly plan the remainder of his/her academic program.

PhD students can elect to pursue a Data Science Option , Advanced Data Science Option , or a dual-title PhD in Atmospheric Sciences and Astrobiology .

To complete the PhD a student must pass COGS, pass their General Exam and pass their Final Exam. Detailed information about these milestones can be found in the sidebar to the left.

To see examples of the theses and dissertations written by graduate students in the Department of Atmospheric Sciences, please see the UW library archives: https://digital.lib.washington.edu/researchworks/handle/1773/4893

Division of Computational & Data Sciences

Program overview.

The Division of Computational & Data Sciences (DCDS) at Washington University in St. Louis trains students interested in problems from across a range of disciplines that share a common reliance on data and computing.

The introduction of now-standard tools from statistical analysis and hypothesis testing transformed the practice of natural and social science in the mid-twentieth century. Emerging tools from computational and data science have the potential to bring about an even larger transformation of scientific practice, especially in the social sciences. The questions raised by data generated by and about human behavior are engaging and profound. However, many, if not most, of these questions can only be tackled using a multi-disciplinary approach that combines deep knowledge of the capabilities and operation of data science techniques, with the domain expertise needed to apply them effectively to the problems under consideration.

Doctoral students in Computational & Data Sciences receive strong methodological training in modern computational and statistical methods, and also acquire expertise in a particular social science application area.

The program is inherently interdisciplinary and brings together leading experts across the university who are using data to solve some of the greatest challenges that our world faces today. Faculty include both data and computing experts as well as domain experts from different application areas.

Meet our PhD Students

DCDS in the News

$3M grant funds training to harness power of AI for social, environmental challenges

$3M grant funds training to harness power of AI for social, environmental challenges

The National Science Foundation (NSF) is investing $3 million over the next five years in the Artificial Intelligence (AI) Advancements and Convergence in Computational, Environmental and Social Sciences (AI-ACCESS) program at Washington University in St. Louis.

TRIADS-funded group studies how AI changes human behavior

TRIADS-funded group studies how AI changes human behavior

WashU faculty Chien-Ju Ho and Wouter Kool are fascinated by the interplay between humans and the AI algorithms that our decisions help to train. They’ve published their first study exploring how people modify their behavior when knowingly interacting with artificial intelligence.

Doctor of Philosophy (Ph.D.)

The Doctor of Philosophy program in the College of Education prepares students for careers in research or scholarly inquiry and teaching at the college level. The program consists of: (1) continuous research, (2) courses in education and related fields designed to develop a comprehensive academic basis for future work in research and teaching, and (3) teaching and other related experiences tailored to individual needs and career goals. Each student works closely with an advisor and a faculty Supervisory Committee to select courses, topics of research and inquiry, and teaching experiences. These three areas will combine to: (1) convey deep scholarly knowledge of education and a specialty outside of education (2) promote a broad understanding of various methods of inquiry in education and develop competency in several of those methods, (3) impart broad knowledge of theory and practice in two supportive cognates, and (4) promote excellence as a college teacher. Our Ph.D. alumni have positions at national research universities, at region and local universities, in community colleges, K-12 school settings, laboratories, foundations, agencies, and private businesses.

Culturally Sustaining Education

Educational policy, organization and leadership, language, literacy and culture, leadership in higher education, learning sciences & human development, measurement and statistics, school psychology (ph.d.), science or math education specialization, social and cultural foundations, special education doctoral, teacher education and teacher learning for justice.

Teacher working with preschool students

Masters in Applied Behavior Analysis (On-campus)

What you can earn, credits earned, time commitment, upcoming deadline, join a fast-growing career that improves lives.

Applied Behavior Analysis (ABA) is a rapidly growing career that creates meaningful change and empowers individuals living with developmental disabilities. Our on-campus program is designed to help working professionals acquire the knowledge and skills to effect real change at both individual and systemic levels.

Upon completion of the program, you will earn your M.Ed. in special education and be fully prepared to take the Behavior Analyst Certification Board (BACB) exam . By becoming a board-certified behavior analyst (BCBA), you will unlock a world of professional opportunities in this rapidly growing field!

Student getting help with reading

What you'll learn

  • Application of ABA principles in real-world situations
  • Designing of evidence-based interventions that are effective and appropriate
  • Collection, analysis and interpretation of data to evaluate intervention effectiveness
  • Collaboration with individuals, families, school staff and care teams
  • Understand the importance of diversity, equity, and inclusion in providing culturally responsive services

After graduation

As the demand for highly qualified BCBAs continues to rise nationwide, graduates of our program will discover a diverse array of professional opportunities.

  • Providing support to educators, students, and families within educational settings
  • Delivering in-home services to individuals and families
  • Becoming esteemed members of healthcare teams
  • Establishing inclusive and culturally responsive services within community organizations

What's next?  After working for a few years as a BCBA, you might want to consider our PhD in Special Education program .

Let's connect

We're excited that you're interested in ABA! By joining our mailing list, you can receive updates on info sessions, deadlines and more!

Connect with us

Related programs

Need the flexibility of an online program? We also offer an Online Applied Behavior Analysis program with synchronous evening classes.

ABA program mission statement

Our goal is to prepare students to be competent, inclusive, ethical, and professional behavior analysts who work with persons with developmental disabilities and their families.

Students coming out of our program will:

  • Understand and fluently apply the principles of behavior analysis.
  • Have a working knowledge of current evidence-based practices for individuals with developmental disabilities.
  • Select or create contextually appropriate, evidence-based interventions for individuals with whom they work and critically analyze and evaluate the effects of those interventions.
  • Work collaboratively and openly with schools, families and other community stakeholders, always with an understanding of how culture and equity impact service delivery.
  • Ensure that the primary outcome of their work is to improve the quality of life for the individual and their family.

Our ABA Program is guided by five core ethical principles.

Behavior analysts have a responsibility to engage in practices that maximize their clients' well-being and avoid those that cause harm. We understand that behavior analytic services are most likely to benefit our clients when they are provided in the context of a trusting and compassionate relationship. Where conflicts of interest arise between consumers of behavior analysis, we prioritize outcomes for the most vulnerable clients.

Behavior analysts have a responsibility to provide individuals of all backgrounds and abilities access to and authentic participation in meaningful activities that promote relationships, a sense of community, and an improved quality of life.

Behavior analysts have a responsibility to be honest and transparent. We engage in ongoing professional development and analyze our own practices. Professional excellence requires respectful and effective collaboration with individuals from other disciplines while maintaining a commitment to data-based decision-making. Analyzing evidence from different methodologies is encouraged as a way of collaborating with others and improving practice.

Behavior analysts respect clients’ rights and promote client dignity, privacy, and autonomy. We assist clients to set and achieve their own goals, develop their own agency, and make decisions about their own lives.

Behavior analysts have a responsibility to attend to injustice where they see it, avoid perpetuating inequitable systems, and advocate for equitable systems change. We are uniquely qualified to identify controlling and contextual variables that contribute to inequitable educational and service-delivery systems and develop solutions to supplant them.

ABA student working with preschool students

  • Begin and finish the program in a supportive cohort
  • Graduate in two years
  • Four courses each quarter, autumn, winter and spring
  • Classes are held three evenings per week

Our program cultivates a supportive cohort environment. With sequential courses, you'll progress through the curriculum in sync with your peers and complete the program in two years.

Verified course sequences

The Association for Behavior Analysis International has verified the following courses toward the coursework requirements for eligibility to take the Board Certified Behavior Analyst® or Board Certified Assistant Behavior Analyst® examination (via Pathway 2). Applicants will need to meet additional BACB® eligibility requirements, including evidence of residency in an authorized country, before they can be deemed eligible to take the examination.

  • EDSPE 533 Concepts and Principles of ABA-A (3 credits)
  • EDSPE 534 Concepts and Principles of ABA-B (3 credits)
  • EDSPE 535 History and Philosophy of ABA (3 credits)
  • EDSPE 571 Measurement in ABA (3 credits)
  • EDSPE 536 Assessment in ABA-A (3 credits)
  • EDSPE 537 Assessment in ABA-B (3 credits)
  • EDSPE 552 Instructional Strategies in ABA (3 credits)
  • EDSPE 531 Designing Comprehensive Behavioral Interventions (3 credits)
  • EDSPE 511 Methods of ABA Research (3 credits)
  • EDSPE 539 Ethics and Professionalism in ABA-A (3 credits)
  • EDSPE 549 Ethics and Professionalism in ABA-B (3 credits)
  • EDSPE 553 Supervision in ABA (3 credits)

The following courses are required to earn your masters degree in special education.

  • EDSPE 500 Practicum Seminar (18 credits)
  • EDSPE 525 Autism (3 credits)
  • EDSPE 554 Behavior Analysts in Schools (3 credits)
  • EDSPE 563 Collaborating with Families and Educational Teams (4 credits) 
  • 150 supervised fieldwork hours required for the BACB exam
  • 75 hours provided through group practicum at UW
  • 75 hours provided by local agencies or schools

Supervised fieldwork experience is essential for eligibility to sit for the BACB exam and plays a crucial role in applying the strategies and skills learned during coursework. To fulfill this requirement, we use a shared supervision model. You'll engage in a group practicum, providing 75 hours of supervision through the UW, and complete an additional 75 hours of individual supervision through a local agency or school.

This arrangement offers valuable exposure to diverse practicum placements and multiple experiences. Notably, our program offers opportunities to complete fieldwork experiences at the internationally recognized Project Data at the UW Haring Center.

Earning your master's degree and BCBA certification involves two separate examinations:

Board Certified Behavior Analyst examination

After finishing this program, you will have met all requirements to sit for the Behavior Analyst Certification Board (BACB) exam. Our faculty and advisors will help you navigate BACB exam process.

It is important to note that while the BCBA certification is valid in all 50 states, many states also require behavior analysts to be licensed. State laws related to licensure of behavior analysts vary and do not necessarily reflect the same requirements as the BCBA certification. Please research the requirements for the state you plan to apply for licensure in. For more information, visit BACB's page on state licensure .

IMPORTANT: Only individuals residing in the United States, Canada and UK may apply for BACB certification. Visit BACB's page on international development .

Master’s examination

During your final quarter, you will need to pass a comprehensive master's examination that covers all ABA content covered throughout the program. This examination is a University of Washington requirement. 

Admission requirements and process

We highly value candidates who have experience working with individuals with disabilities. A minimum of one year of direct applied behavior analysis experience is preferred. Whether you've worked as a tutor, paraeducator or teacher in an ABA-focused program, your practical experience is valuable. Candidates with additional ABA experience are given preference.

We do not have specific requirements regarding the field of your bachelor's degree. Instead, we consider all relevant experiences when evaluating applicants.

If you already hold a master's degree in another field, you can apply to the program as a graduate non-matriculated student. This pathway allows you to take a reduced course load per quarter while still completing the program within two years. We welcome master's degrees from any field of study and value the diverse experiences you bring to the program. If you have any questions about the sufficiency of your master's degree toward certification, please contact the Behavior Analyst Certification Board .

Your degree can be in-progress when applying but must be completed before the program starts.

  • Include one from each institution from which you've earned a degree and one from every institution you have attended in the previous 5 years.
  • Your transcripts must include your name, coursework and degree (if completed)
  • If you are offered admission , the UW Graduate School will request an official transcript from your most recent degree earned

The UW Graduate School requires a cumulative GPA of 3.0, or 3.0 for your most recent 90 graded quarter credits (60 semester credits). However, we review your application holistically. If your GPA is below 3.0, contact us at [email protected] for advice on how to strengthen your overall application.

At least one letter should speak to your work experience in applied behavior analysis. During the online application process, you will be given instructions for adding your recommenders and getting their letters submitted electronically. All recommenders must submit their letters online.

A current academic and professional resume or vita is required. In addition to educational degrees and professional experience, you should include a listing of all relevant awards, publications, presentations or other achievements that will help us evaluate your application. We are looking for at least one year of experience with applied behavior analysis

The admissions committee uses your statement of purpose, along with other evidence, to determine whether your goals are well-matched with our programs. Your statement should answer the following questions:

  • What in your past experience has influenced your interest in applied behavior analysis?  
  • What in your academic and work experience has prepared you for graduate school in ABA?
  • Why is the program at the University of Washington a good match for your academic and career goals?
  • Diversity, equity, and inclusion are important to our ABA program. Please speak to the unique experiences and perspectives you would bring to our program.

Your statement should be 2 pages, double-spaced.

While optional, you can add to your application by submitting a personal history statement with each application. This statement should address your intellectual growth and development, inclusive of and beyond your academic goals. Speak to topics like:

  • Educational, cultural and economic opportunities and disadvantages you've experienced
  • Ways these experiences affected the development of your special interests, career plans and future goals.
  • Any additional topics requested on a specific program's page

Statements should be no longer than two pages long. And while there are no standard formatting requirements, we encourage double-spaced text with a legible font.

  • Gather all required documents
  • Visit the Graduate School website
  • Log into your account or create a new profile if you are a first-time applicant
  • Complete all steps in application process and upload your documents
  • You may request a fee waiver during the application process
  • Submit your application

When completing your application, you will select the following options:

  • Graduate if you want to earn your master’s degree
  • Graduate Non-matriculated if you already have your master’s degree
  • Education - Seattle (MEd - Special Education - Applied Behavior Analysis)

Here is our general timeline for decisions. Have questions about the process? Visit our graduate admissions page .

Step 1: Application processing

  • Within 7 business days after the deadline, we will check if your application if fully complete
  • We will email you whether your application is complete or incomplete
  • If your application is missing anything, you will have a short amount of time submit these items
  • You can also log into the online application and check your status and see any missing items

Step 2: Application review

  • Committees begin reviewing applications about three weeks after the deadline
  • You will receiving an email when your application has entered the review phase

Step 3: Decision notification

  • The final decision will be emailed to you
  • Your status will also be updated in the online application
  • You may be able to transfer up to six credits toward your UW masters in special education
  • You must petition the BACB to substitute courses toward BCBA exam requirements

If you have previously taken similar courses at another university, you may be eligible to transfer up to six credits toward your UW master's degree. The acceptance of transferred credits is subject to advisor approval in compliance with the Graduate School's policies.

Please note that the University of Washington is not authorized to approve course substitutions that fulfill the BACB's requirements for the BCBA exam. If you believe you have completed a course that adequately substitutes for the BACB's requirements, you must petition the certification board directly. 

We value and welcoming applications from international students! If you are applying from outside the United States, there are additional requirements and application materials.

  • At minimum, you must have the equivalent of a U.S. bachelor's degree (a four-year degree from an institution of recognized standing)
  • The national system of education in the foreign country
  • The type of institution
  • The field of study and level of studies completed
  • International transcripts must be submitted in the original language.
  • Your transcript should include date of graduation and title of the awarded academic degree
  • If your transcript is not in English, you must also provide a certified English translation
  • You do not need to have your transcript evaluated for the degree by an agency

Per  UW Graduate School policy , you must submit a demonstration of English language proficiency if your native language is not English and you did not earn a degree in one of the following countries:

  • United States
  • United Kingdom
  • New Zealand
  • South Africa
  • Trinidad and Tobago

The following tests are accepted if the test was taken fewer than two years ago:

  • Minimum score: 80
  • Recommended score: 92+
  • The UW's 4-digit code is 4854
  • University of Washington All Campuses, Organisation ID 365, Undergrad & Graduate Admis, Box 355850, Seattle, WA, 98105, United States of America
  • Minimum score: 6.5
  • Recommended score: 7.0+
  • School information for submission: University of Washington, All Campuses Undergraduate & Graduate Admission Box 355850 Seattle, WA 98195
  • Minimum score: 105
  • Recommended score: 120+
  • Follow the instructions on the Duolingo website to submit your scores

If apply and are offered admission to UW, you will need to submit a statement of financial ability.

Costs and funding

We are a tuition-based program. Estimated tuition rates are based on your residency: 

  • Washington state residents: $19,584 per year
  • Out-of-state students: $35,352 per year

Estimates are subject to change and may differ due to course load and summer quarter enrollment. Estimates include building fees, technology fees, U-Pass, etc. Additional program-specific fees are not included in this estimate.

View the UW tuition dashboard → Visit the Office of Planning & Budgeting →

Federal financial aid is available for students. Visit the UW Financial Aid website for information and resources. The College of Education also provides scholarship and other funding opportunities.

ABA Diversity Scholarship

Aba on-campus program student data.

The following data is from the 2022-23 annual reporting period

*2021 data, most recently available per the BACB

Program Faculty

Alice Bravo

Alice Bravo

Nancy Rosenberg's professional headshot

Nancy Rosenberg

Ilene Schwartz

Ilene Schwartz

Scott Spaulding

Scott Spaulding

Program affiliated faculty.

Photo not available

Rick Colombo

Katie Greeny

Katie Greeny

elizabeth kelly

Elizabeth Kelly

Yev Veverka

Yev Veverka

UW Microbiology

Ph.d. requirements and curriculum, graduate curriculum requirements for the phd.

The requirements listed below are the minimum requirements to be met by all students in the Ph.D. program.   The student's supervisory committee may require or recommend additional courses as deemed appropriate, based on the student's background and research plans.

Graded Course Requirements

A total of 18 graded credits are required before taking the General Exam, and a minimum 2.7 grade in each course is required. Students are required to take courses in bacteriology, virology, and biostatistics chosen from the following lists, with the required minimum number of credits indicated for each area of study. 

Bacteriology (3 credits) •    CONJ 557 (Spring, 2 credits), Microbial Evolution •    CONJ 558 (Winter, 1.5 credits), Prokaryotic Biology

Virology (3 credits) •    MICROM 540 (Autumn, even years, 3 credits), Virology •    MCB 532 (Autumn, odd years, 3 credits), Human Pathogenic Viruses

Biostatistics (2 credits) •    BIOSTAT 511    (Autumn, 4 credits), Introduction to Statistics in Health Sciences •    UCONJ 510 (Summer, 2 credits), Introductory Laboratory Based Biostatistics

The remaining credits (for a total of 18 graded credits) can come from taking more than the minimum number of credits in each distribution or from additional departmentally approved 500-level graduate courses listed in Appendix 1. If you are interested in a class that is not listed, please petition the Graduate Program Policy and Advising Committee (GPAC) by contacting the chair for permission to have it count towards the degree before you take the class.  Note that courses change, so verify course details online.  Also investigate the anticipated workload, which varies considerably among graduate classes. Program policy limits you to no more than 6 graded credits per quarter.

Program Requirements

  • Attending departmental seminars (MICROM 520) and “work in Progress” (WIP, MICROM 522) is mandatory. Both courses are graded Credit/No-Credit. Departmental seminars are crucial for contributing to the breadth of student knowledge, and students must register for seminar each year of graduate school. WIP serves to develop oral presentation skills. Students must register for WIP through year 5 and will be scheduled and must present in WIP every year of graduate school. The requirement for a grade of “Credit” and the manner in which this requirement will be assessed will be conveyed to students at the beginning of the academic year.  A grade of “No-Credit” will result in a warning, which may escalate to Probation, Final Probation, and Drop (see https://grad.uw.edu/policies/3-7-academic-performance-and-progress/ ) should the deficiency not be addressed satisfactorily. Failure to register for WIP will immediately escalate to Probation.
  • While completing the course requirements, students should register for enough MICROM 500 or MICROM 600 to bring their total credits to 10-18 per quarter in Autumn, Winter, and Spring. Register for exactly 2 credits in Summer, which is either UCONJ 510 (to fulfill the biostatistics requirement) or MICROM 600.  When graded credit requirements have been fulfilled, register for MICROM 600 prior to completing the General Exam and for MICROM 800 after passing the General Exam for a total of 10-18 credits per quarter during the academic year and for exactly 2 credits during summer quarter.
  • BIOETHICS. All of our students must take either 5 lectures and attend 3 discussion groups that are part of the Biomedical Research Integrity (BRI) series in the first or second summer (register at http://depts.washington.edu/uwbri/front ) or Bioethics 101 taught by the Biochemistry Department (register for the Winter Quarter BIOC 533).  
  • TA in at least two lab courses for undergraduates, which is usually satisfied in the first and/or second year.
  • Give at least two formal lectures in an undergraduate course, which is usually satisfied in the fourth or fifth year.
  • Be first author on at least one paper related to thesis research, which is published or accepted for publication in refereed journals prior to the thesis defense.

Graduate School Requirements

(see https://grad.uw.edu/policies-procedures/doctoral-degree-policies/doctoral-degree-requirements/)

  • Completion of a program of study and research as planned by the Graduate Program Coordinator in the student's major department or college and by the Ph.D. Supervisory Committee. At least 18 credits of course work at the 500 level and above must be completed prior to scheduling the General Examination.  Note that this includes classes that are not graded (CR/NC).
  • Presentation of 90 credits, 60 of which must be taken at the University of Washington.
  • Numerical grades must be received in at least 18 quarter credits of course work taken at the University of Washington prior to scheduling the General Examination. The Graduate School accepts numerical grades in departmentally approved 400 and 500 level courses. A minimum cumulative GPA of 3.00 is required for a graduate degree at the University.
  • Completion of a total of 60 credits prior to scheduling the General Examination (a master's degree from the UW or another institution may be used as a substitute for 30 of these credits).
  • Creditable passage of the General Examination.  Registration and completion of credits as a graduate student is required the quarter the exam is taken and candidacy is conferred.
  • The Candidate must register and complete a minimum of 27 credits of dissertation (MICROM 800) over a period of at least three quarters.  At least one quarter must come after the student passes the General Examination. With the exception of summer, when students take 2 credits, students are limited to a maximum of 10 credits per quarter of dissertation (MICROM 800).
  • Creditable passage of a Final Examination, which is usually devoted to the defense of the dissertation in the field with which it is concerned. The General and Final Examinations cannot be scheduled during the same quarter. Registration and completion of credit as a graduate student is required the quarter the exam is taken AND the degree is conferred.
  • Preparation of and acceptance by the Dean of the Graduate School of a dissertation that is a significant contribution to knowledge and clearly indicates training in research.
  • Completion of all work for the doctoral degree within ten years. This includes quarters spent On-Leave or out of status as well as applicable work from the master's degree from the University of Washington or a master's degree from another institution, if used to substitute for 30 credits of enrollment.
  • Registration and completion of credits as a full- or part-time graduate student at the University for the quarter in which the degree is conferred (see detailed information under Final Quarter Registration).

Training Grants

All U.S. citizens are strongly encouraged to apply for training grants.  A list of available Training Grants can be found at http://blogs.uw.edu/tgrants/graduate-students/ .

APPENDIX 1:  Other Courses

The following courses are approved to count towards the degree.  Keep in mind that we try to keep this list accurate; however, departmental offerings change from year to year.  And, the quarter in which courses are offered, especially conjoints (CONJ), can vary.  Note that the categories are based on the course title rather than a thorough review of the syllabus.

BIOCHEMISTRY CLASSES:

CELL BIOLOGY CLASSES:

COMMUNICATING SCIENCE AND COMMERCIALIZATION:

IMMUNOLOGY, MEDICINE, PATHOGENESIS, and OTHERS:

* These classes are primarily for Biochemistry graduate students, who take them as a cohort.  Microbiology students can take them with permission of the instructor.

** These are smaller Genome Sciences class, so registration may be difficult.

IMAGES

  1. fully funded phd data science

    phd data science university of washington

  2. MS in Data Science in USA

    phd data science university of washington

  3. University of Washington Computer Science and Engineering Building

    phd data science university of washington

  4. Core Data Science Training for PhD Research

    phd data science university of washington

  5. BS in Data Science

    phd data science university of washington

  6. Life Science Building at University of Washington Designed for the Next

    phd data science university of washington

COMMENTS

  1. Ph.D. Advanced Data Science Option

    1. Quals-level requirements Successfully complete the department's PhD qualifying coursework requirements, and satisfactorily complete three out of four of the following core courses in Big Data ( some of the courses listed below may also be counted towards the qualifying coursework requirements, if allowable in the standard requirements):

  2. Data Science PhD Options

    Overview One option from three of the following four areas: Software Development Machine Learning and/or Statistics Data Management and/or Data Visualization Computational Methods in Biology 2 quarters of the eScience Community Seminar Course Options 1. Software Development (minimum of 3 credits) HIGHLY RECOMMENDED:

  3. Machine Learning and Big Data PhD Track

    The UW Department of Statistics now offers a PhD track in the area of Machine Learning and Big Data. All incoming and current students are eligible to apply. The goal of the PhD track is to prepare students to tackle large data analysis tasks with the most advanced tools in existence today, while building a strong methodological foundation.

  4. Graduate Data Science Option (DSO)

    The DSO is intended for students with little or no background in data science, computer science or coding. The option is based on a framework developed by the University of Washington eScience Institute. The eScience Institute empowers UW researchers and students in all fields to answer fundamental questions through the use of large, complex ...

  5. Data Science at the University of Washington

    Through an NSF IGERT Award to create an innovative interdisciplinary Ph.D. program, a massively open online course, multiple certificate programs, and a catalog of data-focused courses, we are establishing curricula and programs to advance data science in and across every discipline on campus.

  6. PHD Data Science Option

    PhD students have the opportunity to pursue their PhD with a Data Science option. The Data Science option prepares the next generation of thought leaders to both apply new data science methods and build new data science tools.

  7. Data Science

    The Master of Science in Data Science at the University of Washington gives current and aspiring data science professionals the technical skills to turn large, messy data sets — or big data — into insights people and organizations can use. Program Website Degree (s)/Certificate (s) offered Master of Science in Data Science

  8. Data Science for Graduate Students

    The following departments already have a Graduate Data Science Option, offered either through a Masters or PhD program: Astronomy Atmospheric Sciences Bioengineering Biology Biomedical Informatics and Medical Education Chemical Engineering Chemistry Civil & Environmental Engineering Earth and Space Sciences

  9. Data-science-options

    The Data Science Option (DSO) The Psych DSO is designed for students with little or no background in data science, computer science or coding. The total credit requirement is 6-8 credits in courses (2 courses @ 3-4 credits each) and 2 seminar credits (plus the usual Psych 522-525 sequence). Note that one of the data science courses may count as ...

  10. Advancing Data Science Education at the University of Washington

    Students can no longer develop tools and models in isolation, because the resulting "hammers" fail to meet the growing needs of the data-enabled sciences. To address these challenges and to educate the next generation of scientists, the University of Washington offers PhD programs specialized in Big Data and Data Science in various ...

  11. Data Science Options

    Data Science Options . PhD Data Science Option and Advanced Data Science Option. The Department of Atmospheric Sciences (AtmS) has partnered with the eScience Institute at the University of Washington to provide a PhD curriculum in Atmospheric Sciences with the Data Science Option (DSO) or Advanced Data Science (ADSO). PhD students choosing these options will benefit from a formal education in ...

  12. Ph.D.-Advanced Data Science Option

    Doctor of Philosophy (Mechanical Engineering: ADV DATA SCI) provides students an introduction to the world of data science, giving them the skills to understand a variety of techniques and tools. This option will help to educate and recognize PhD students whose thesis work focuses specifically on building and using advanced data science tools. The requirements for the Doctor of Philosophy ...

  13. Advanced Data Science Option for PhD Students

    Thanks to a kick-start from the IGERT Program, a new Advanced Data Science Option has been approved. The IGERT program brings together departments and students to educate an interdisciplinary cohort of scientists.The Advanced Data Science Option is the mechanism by which students get credit on their transcripts for their focus on data science within their majors.

  14. PhD Advanced Data Science Option

    PhD students who wish to participate in the Advanced Data Science Option must meet the following requirements, in addition to the requirements for the PhD: Additional Requirements Pass three of the following four courses: Either STAT 509: Introduction to Mathematical Statistics, or for a more advanced approach, STAT 512: Statistical Inference.

  15. Data Science Option

    The Data Science Option (DSO) equips Ph.D. students to tackle modern civil and environmental engineering challenges using large datasets, machine learning, statistical inference and visualization techniques. The DSO is designed to meet a critical educational gap at the intersection of Civil & Environmental Engineering (CEE) and data science allowing Ph.D. students to hone modern data analysis ...

  16. Data Science at UW

    Graduate Departments offer two complementary data science specializations. The Data Science Option targets students who seek to learn data science methods and how to use data science tools. The Advanced Data Science Option targets students who seek to develop new data science methods and tools Graduate Programs Professional Masters

  17. Ph.D. in Chemistry with a Data Science Option (Chem-DSO)

    The Data Science Option (DSO) is a set of extra requirements for students interested in data science. If completed, your degree title will be Doctor of Philosophy (Chemistry: Data Science). The goal of this option is to educate all students in the foundations of data science, so they may apply those methods and techniques in current research.

  18. Data Science Specialization

    Overview. The Data Science Option is designed to meet a critical educational gap at the intersection of Biomedical & Health Informatics (BHI) and Data Science. This option, as an elective part of our research MS and PhD program s, provides students an introduction to the world of data science, giving them the skills with a variety of techniques ...

  19. Ph.D. Program

    The PhD program prepares students for research careers in theory and application of probability and statistics in academic and non-academic (e.g., industry, government) settings.

  20. Machine Learning and Big Data PhD Track Application Details

    Entrance to the Machine Learning and Big Data track only happens after regular admission to the Statistics PhD program. As a UW Statistics PhD student The PhD students who wish to opt for the MLBD track need to: To apply to the track, please send e-mail to Ellen Reynolds. The Statistics ML/BD Track Committee will then review your application.

  21. Data Science Masters

    The MSDS degree is a professional master's program designed for students who want to begin or advance their careers in data science. The program is available full-time or part-time. Classes begin every fall quarter and meet in the evenings on the University of Washington campus.

  22. PhD Degree

    The student and the PhD Supervisory Committee will jointly plan the remainder of his/her academic program. PhD students can elect to pursue a Data Science Option, Advanced Data Science Option, or a dual-title PhD in Atmospheric Sciences and Astrobiology. To complete the PhD a student must pass COGS, pass their General Exam and pass their Final ...

  23. Division of Computational & Data Sciences

    The Division of Computational & Data Sciences (DCDS) at Washington University in St. Louis trains students interested in problems from across a range of disciplines that share a common reliance on data and computing.

  24. Doctor of Philosophy (Ph.D.)

    Graduate Admissions; Undergraduate Admissions; Tuition & Costs; Funding, Aid and Scholarships ... specialized training in science or mathematics education with emphasis for both elementary and secondary schools. ... University of Washington College of Education • 2012 Skagit Lane, Miller Hall • Box 353600 • Seattle, WA 98195-3600 ...

  25. Masters in Applied Behavior Analysis (On-campus)

    University of Washington All Campuses, Organisation ID 365, Undergrad & Graduate Admis, Box 355850, Seattle, WA, 98105, United States of America; Minimum score: 6.5; Recommended score: 7.0+ School information for submission: University of Washington, All Campuses Undergraduate & Graduate Admission Box 355850 Seattle, WA 98195; Duolingo. Minimum ...

  26. Ph.D. Requirements and Curriculum

    Numerical grades must be received in at least 18 quarter credits of course work taken at the University of Washington prior to scheduling the General Examination. The Graduate School accepts numerical grades in departmentally approved 400 and 500 level courses. A minimum cumulative GPA of 3.00 is required for a graduate degree at the University.