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Academics

Frequently Asked Questions (FAQ)

See below for additional information about our unique graduate programs. Contact Ken Kritikos via email kkritikos34@uchicago.edu for additional questions.

  • What is the difference between the Master’s in Data Science (MSDS) and the Master’s in Applied Data Science programs?

    The Master’s in Data Science (MSDS) was created to help students explore research opportunities and theoretical components within the emerging field of data science while the Master’s in Applied Data Science equips students with the skills, tools, and industry insights to establish and build a technical career in data science. 

    The Master’s in Applied Data Science program offers full-time and part-time degree options, both in-person at our Downtown campus as well as online. The MSDS program, by comparison, is meant to be completed fully in-person at our Hyde Park campus. For additional details about the structure and focus of the Master’s in Applied Data Science, please click here.

  • What kind of background do I need to have in order to be considered for the MSDS?

    We encourage students from all educational and professional backgrounds to apply for this program; applicants will only need to have their undergraduate degree from an accredited college or university conferred onto them prior to starting the MSDS. A specific major or degree focus is not required in order to apply.

  • Is there a timeline for degree completion?

    Students can finish the MSDS program in as little as 9 months or up to 2 years depending on your individualized plan of study.

  • Is this program offered in-person, online, or both?

    The MSDS program is offered fully in-person at UChicago’s historic Hyde Park campus.

  • What prerequisites are needed for entry into this program? Do I need to have them completed before I apply?

    There are no prerequisite courses required for entry into the MSDS. Upon review of your application materials, our faculty committee will assess your academic transcripts to determine your proficiency in quantitative courses such as linear algebra, statistics, calculus, and others. In some cases, admitted students may be required to complete up to three of the following foundational courses as a way to fully prepare for the rigors and baseline knowledge required for this program:

    • Computational Foundations for Data Science
    • Mathematical Foundations for Data Science
    • Statistical Foundations for Data Science

    Each of these classes will be offered exclusively online in the summer prior to the start of the term you have been admitted for. Not all incoming students are required to complete the courses above; students with a proficient mathematical or technical background may be able to waive out of these foundational courses.

  • Is relevant work experience required or recommended?

    While relevant professional, internship, or research experience can be a helpful addition to your overall application, it is not a requirement in order to be considered for admission.

  • When is the deadline to apply?

    You can click here for the most up-to-date information regarding upcoming admission cycles and application deadlines.

  • Are the GRE, GMAT, or other standardized test scores required for admission?

    We do not require any standardized test scores as part of our admission process. If you’ve previously taken a standardized exam, you are welcome to submit those scores as part of your application. Our GRE school code is 1832.

    Please be advised that GRE scores are only valid if the exam was taken within five years prior to the application deadline.

  • Once I have applied, when can I expect an admission decision?

    While exact timelines can vary each cycle, most candidates should expect an update to their application status within 6-8 weeks following our deadline to apply.

  • What is the total cost of this program?

    For updated information about tuition and fees for this program, we encourage you to visit our website for the Office of the Bursar.

  • Does UChicago offer financial aid for prospective students?

    The Physical Sciences Division (PSD) and the University of Chicago are committed to supporting students throughout their educational journey. We encourage you to explore our website to learn more about financial aid for prospective and current graduate students in all forms. This includes but is not limited to: internal and external scholarship opportunities, grants, loan options, research or teaching assistantships, and much more. Additional details about fund sourcing at UChicago can be found here.

    Newly admitted students to the MSDS program are eligible for some merit-based scholarships on behalf of the Data Science Institute (DSI). Scholarship amounts will vary on a case-by-case basis.

  • Can I get an application fee waiver for the MSDS program?

    For all fee waiver questions, please refer to the Physical Sciences Division fee waiver policy.

  • What makes a PhD in Data Science different from a PhD in Computer Science, Statistics, Applied Math, or another related field?

    Our PhD program was created on the premise that Data Science is an emerging discipline. Similar to how Computer Science grew out of Mathematics and Electrical Engineering, Data Science is emerging from Statistics, Computer Science and Applied Math to address a set of fundamental research questions that no single existing field is focused on solving. Examples include:

    • What are the fundamental properties of data? How do these properties inform the value of data? How do these properties interact with the algorithms that eat them?
    • How is Artificial Intelligence (AI) fundamentally changing science and how is science changing what we know about AI?
    • How should society equitably distribute access to data? What are effective policies and practices to ensure equity?
    • What are the fundamental threats to free society in the age of data and algorithms?
  • Will all PhD applicants earn their Master's of Science (MS) on the way to the PhD?

    Yes, candidates in our PhD program will have the opportunity to earn their MS prior to completion of their doctoral study.

  • What is the timeline for PhD degree completion?

    It is expected that most students will complete their coursework and dissertation requirements within 5 years.

  • Are the GRE, GMAT, or other standardized test scores required for admission?

    We do not require any standardized test scores as part of our admission process. If you’ve previously taken a standardized exam, you are welcome to submit those scores as part of your application. Our GRE school code is 1832.

    Please be advised that GRE scores are only valid if the exam was taken within five years prior to the application deadline.

  • What is the deadline to apply?

    You can click here for the most up-to-date information regarding upcoming admission cycles and application deadlines.

  • Can I get an application fee waiver for the PhD program?

    For all fee waiver questions, we encourage you to refer to the Physical Sciences Division fee waiver policy.

  • Once I have applied, when can I expect an admission decision?

    While exact timelines can vary each cycle, most candidates should expect an update to their application status within 6-8 weeks following our deadline to apply.

  • Does your admissions process include an interview?

    Individual professors within our Committee on Data Science (CODAS) may contact applicants and schedule an interview as a way to better identify those whose research interests best align with our distinguished faculty. Candidates who do not receive an interview request may still be admitted to the PhD track. Given the busy schedules of our faculty, not all candidates will receive a request to interview. 

  • Is it required that I reach out to faculty members directly before submitting my application?

    It is not a requirement that applicants contact faculty members prior to submitting their application. If you have specific questions about the research interests of someone on our Committee on Data Science (CODAS), feel free to connect with them.

  • Will I have an advisor assigned to me once I am accepted into the PhD program?

    As part of the application process, candidates are asked to identify up to three specific faculty members at UChicago with whom they would be interested in working with if admitted to the program. In your candidate statement, we encourage you to provide further insight as to why you’ve selected these faculty members in your application. Upon admission, students will be assigned a primary advisor from our Committee on Data Science (CODAS). As our program follows a committee model, your primary advisor may or may not be your dissertation advisor but rather a resource and mentor to support your initial study in the PhD program.

  • How and when do you select a mentor/thesis committee?

    First-year students will initially work closely with a primary advisor assigned to you before the start of your initial entry term. Students will typically select a thesis advisor by the beginning of their second year. By the end of the third year, each PhD student (after consultation with their advisor) shall establish a thesis committee of no less than three faculty members, including the advisor, with at least half of the members coming from the Committee on Data Science (CODAS).

  • What does the funding model look like for the PhD in Data Science?

    Admission to the PhD program comes with five (5) years of guaranteed financial support. This funding will cover all tuition and fees during that period as well as provide you coverage under our University Student Health Insurance Plan (U-SHIP). In addition, admitted students will receive an annual stipend that will be paid from a research assistantship (RA) along with additional teaching assistant (TA) duties.

    As a requirement of this program, all doctoral students will TA at least two quarters over the duration of their study at UChicago. Additional TA/RA responsibilities will be discussed between you and your advisor to better support your overall study.