We've put together some commonly asked questions to give you more information about the online Master of Science in Data Science degree program.



Will there be admission caps?
No, we will not be limiting the size of our classes at this time.

If I meet the admissions criteria, will I be automatically admitted to the program?
No applicant will be automatically admitted to the program. All applications will be reviewed by a faculty committee to make certain those admitted have the ability to succeed in the program.

How much is each course and are there fees?
Current tuition for the program is $10,000, which averages out to $1,000 per course. While we anticipate tuition staying the same, some fees may apply throughout the course of the program.


The application for the MSDS program is now open! Applications are open for the 2021 Spring semester with deadline dates of September 15th (priority deadline) and October 15th (regular deadline). We have created an application checklist to give you the ability to proactively collect all necessary application items in order to apply soon as the application opens. We look forward to reviewing your application!

Is there a difference between priority and regular deadline?
Applicants who meet the priority deadline will be significantly more likely to receive the results of their admissions decisions than applicants who apply by the regular deadline. 

Your admission chances will not be impacted by applying at either date because we are not capping class sizes at this time.

Will there be an application fee?
Yes, $65 for US-based applicants and $90 for international applicants.

What are the requirements for applying?
Admissions will be holistic and will take into consideration the following items: undergraduate GPA, a relevant field of study, competitive GRE scores, work experience, a statement of purpose, TOEFL score (if applicable) and letters of recommendation (optional).

A typical candidate will have a bachelor’s degree in statistics, computer science, computer engineering, mathematics, electrical engineering or similar.

An atypical candidate may not have a bachelor’s degree in a related field but will have a passion for data science and be able to show their functional use of the topic through work experience. This information will need to be conveyed on an applicant’s CV and in their personal statement.

What constitutes a competitive candidate?
Our Master of Science in Data Science program will target enrollees who would like to build their technical competency and receive rigorous training in the field of data science. The ideal candidate will have some technical background, but have a driving interest in both computational methods and statistical inference, and who are excited to advance their career opportunities within industry, government, academia, and nonprofit organizations.​

What prerequisite courses are needed to apply for the program?
Applicants who do not hold a degree in statistics, computer science, computer engineering, mathematics, electrical engineering or similar, will need to have certain course work prior to enrolling in the program:

  • Math (calculus and linear algebra)
    • Multivariable Calculus (eg. MATH 408D) and
    • Linear Algebra (eg. MATH 341 equivalent)
  • Statistics (college level introduction to statistics)
    • Introduction to Statistics (eg. SDS 302, 304, 306 or equivalents)
    • SDS 328M equivalents
  • Some programming experience in at least one of:
    • R, Python, C++

Can I apply for both the MCSO and the MSDS program?
Yes, you may apply to both. Please note that you will have to complete both applications but will only have to pay the fee once as long as it is within the same application cycle (i.e. you are applying for the same semester). If admitted to both programs, the applicant will be asked to choose their preferred program prior to enrolling.​
With the current pandemic, is the GRE required?
As a temporary accommodation in response to COVID-19, the GRE requirement will be waived for all applicants for the spring 2021, summer 2021, fall 2021, and spring 2022 semesters. Students may continue to submit official GRE scores for consideration, but they will not be required to do so. GRE scores when provided, will continue to serve as just one of the many factors considered in our review of a student’s application. Please take into consideration that providing GRE scores can bolster your application if there are aspects of your application that you feel may be not as competitive as others. If you have any questions, please feel free to reach out to us and we can better assist you.
*Please allow up to 72 hours after submitting your application for the GRE to be removed from the “To-Do” items on your MyStatus page.
Can GMAT replace the GRE?
Unfortunately, no, we cannot accept GMAT scores.
Will my GRE scores from a previous UT application carry over?

Quite possibly, please check in with the Graduate School at giatest@austin.utexas.edu to confirm.

Is the TOEFL required for International Students?
International applicants who are from a country where English is the only official language are exempt from this requirement. Additionally, applicants are exempt from the requirement if they possess a bachelor’s degree from a U.S. institution or an institution in a country where English is the only official language. The requirement is not waived for applicants who have earned a master’s—but not a bachelor’s—degree from a similar institution.

What are the minimum TOEFL/IELTS scores acceptable for admission by the Graduate School?

  • TOEFL: 79 on the Internet-based test (iBT)
  • IELTS: An overall band of 6.5 on the Academic Examination

It is the responsibility of the applicant to be prepared for the program prior to starting and to convey that preparedness in their CV and personal statement.

Will I need to provide a translated version of my transcript?
Yes, transcripts written in a language other than English must be accompanied by a translation. 
May I apply even though I hold an MS in Data Science?
Yes, however it is important to know that the Graduate School may require additional information as to why you want to apply to our program when you already hold that degree. They will want to know how the programs compare and how ours is different than what you already have. Providing this information in the personal statement will aid in the admissions & approval process.

Will I be able to transfer credits from another university to this program?
Once a student is accepted into the program and is enrolled, they can provide documentation needed for the approval process for transferring credit. There will be two steps of approval, the first comes from the graduate advisor and the final approval from a degree evaluator with the Office of Graduate Studies. Here is more information on the requirements of this process.

Will there be financial Aid Assistance? Will GI benefits apply?
US-based students in Option III programs are eligible only for federal guaranteed loans and some private sector loans. The Office of Financial Aid can advise Option III students on availability of these loans and required procedures for applying. Please contact Financial Aid. Please note students taking 3 hours are not aid eligible. Please note that with Option III programs students are not eligible for grant loans.

Students in Option III programs are not eligible for Hinson-Hazelwood Act Exemptions (for Texas ex-servicemen and their children), but may be eligible for GI Bill benefits. Please direct questions to gibill@austin.utexas.edu and or UTVeterans@austin.utexas.edu

Does this program offer F1 or OPT status?
This program does not offer visa status.

Am I eligible to apply if I have OPT or F1 status?
International students who are inside the US and have an immigration status, may run into some difficulties with being eligible for our program. Texas Global (the international office at UT) is most concerned with those with an F1 and OPT status because their visa statuses have certain restrictions concerning online study. If you fall into this category, it is important to contact them right away and determine if you are eligible for our program.




Is this program accredited?
Here is UT’s Statement of Accreditation:

The University of Texas at Austin is accredited by the Southern Association of Colleges and Schools Commission on Colleges to award baccalaureate, professional, masters, and doctorate degrees. Contact the Southern Association of Colleges and Schools Commission on Colleges at 1866 Southern Lane, Decatur, Georgia 30033-4097 or call 404-679-4500 for questions about the accreditation of The University of Texas at Austin.

What degree is earned at the end of the program and what will it say on the diploma?
Master of Science in Data Science.

What areas of expertise are covered?
This program consists of a balance of computer-science based and statistics-based data science courses. This program will provide working professionals with an opportunity to develop expertise that combines skills from statistics, data analysis, and machine learning in pursuit of actionable insights that drive decisions and strategy. 

Specifically, courses with a typical statistics background will cover topics such as: data ethics, probability, simulation, confidence intervals, hypothesis testing, linear models, longitudinal data, causal inference, and visualization. Courses with the typical computer science background will cover topics such as: classification, neural networks, deep learning, model optimization, and algorithm design.

What is the curriculum?
This is a 30 hour program. There are 3 core required courses and 7 additional required courses for a total of 10 courses. The core requirement will be satisfied with three foundational courses which will provide students with a broad, foundational understanding of the field and will also establish the basis for some of the prescribed electives. They include: 

  • DSC 381: Probability and Simulation Based Inference for Data Science
  • DSC 382: Foundations of Regression and Predictive Modeling
  • DSC 388G: Algorithms: Techniques and Theory

Non-core requirements include the following courses: 

  • DSC 383: Advanced Predictive Models for Complex Data (Pre-requisite of DSC 382)
  • DSC 384: Design Principles and Causal Inference for Data-Based Decision Making (No Pre-requisite)
  • DSC 385: Data Exploration, Visualization, and Foundations of Unsupervised Learning (No Prerequisite)
  • DSC 391L: Principles of Machine Learning (Recommended prior course: DSC 382)
  • DSC 395T: Advanced Linear Algebra for Computation (No Prerequisite)
  • DSC 395T: Optimization (Pre-requisite of DSC 388G)
  • DSC 395T: Deep Learning (Pre-requisite of DSC 382)

How many credit hours will I need in order to graduate?
30 hours of credit, each course is three hours, 10 courses.

How long will the program take me to complete?
We envision that the 30-hour UT Austin MSDS program will be completed over the course of 18 to 36 months.

Do I have to maintain a certain GPA in order to stay in the program?
Students must earn a grade of B- or better on all data science coursework. To graduate, all students must have a graduate grade point average of at least 3.00. Additionally, candidates for the master’s degree must also have a grade point average of at least 3.00 in courses included on the Program of Work. 

Are people being hired from the outside to teach these courses?
No, UT faculty are teaching our program. Some of the faculty include:


How many courses will I be allowed to take at a time?
A student may take up to five courses each semester. The online master’s courses are of comparable rigor to the traditional courses. For working professionals, we highly recommend taking one to two courses per long semester.

Can you start taking classes in the fall or the spring?
Yes, you can start taking classes in either semester.

When will the semester start and end?
The program will follow the timeline of the traditional semester. The university calendar can be found here.

Course Descriptions:

DSC 381: Probability and Simulation Based Inference for Data Science
The course is designed to introduce students to foundational knowledge of inference, through the simulation process. Topics include probability, exponential families, conditional probabilities and Bayes theorem, inference and maximum likelihood estimation, confidence intervals, and hypothesis testing (emphasis on simulation).

DSC 382: Foundations of Regression and Predictive Modeling
Course is designed to introduce students to the basics of regression based modeling. Topics include simple and multiple regression, interpretation of models and coefficients, prediction and estimates, regularization processes, and generalized linear models.

Prerequisite: DSC 381: Probability and Simulation Based Inference for Data Science

DSC 383: Advanced Predictive Models for Complex Data
This course extends the course work of Foundations of Regression and Predictive Modeling and introduces students to advanced techniques used in practice for regression based models. Topics include time series and longitudinal data, repeated/mixed models, spatially correlated data, and random forest models.

Prerequisite: DSC 381: Probability and Simulation Based Inference for Data Science and DSC 382: Foundations of Regression and Predictive Modeling

DSC 384: Design Principles and Causal Inference for Data-Based Decision Making
The course is designed to expose students to the field of big data and the rigors of determining applicable design structures from that data. Topics include classic design structures, non-typical data structures and novel design processes, and causal inference.

DSC 385: Data Exploration, Visualization, and Foundations of Unsupervised Learning
This course is designed to expose students to visualization techniques used in practice to discover insights about data. Broad topics include data quality and relevance, data ethics and providence, clustering, dimension reduction, and reproducibility.

DSC 391L: Principles of Machine Learning
Computing systems that automatically improve their performance with experience, including various approaches to inductive classification such as version space, decision tree, rule-based, neural network, Bayesian, and instance-based methods; as well as computational learning theory, explanation-based learning, and knowledge refinement.

Prerequisite: DSC 381: Probability and Simulation Based Inference for Data Science and DSC 382: Foundations of Regression and Predictive Modeling

DSC 395T: Deep Learning
This class covers advanced topics in deep learning, ranging from optimization to computer vision, computer graphics and unsupervised feature learning, and touches on deep language models, as well as deep learning for games.

Prerequisite: DSC 381: Probability and Simulation Based Inference for Data Science and DSC 382: Foundations of Regression and Predictive Modeling

DSC 388G: Algorithms: Techniques and Theory
Advanced topics in algorithm design and analysis including algorithmic paradigms, maximum flow, randomized algorithms, data structures, NP-completeness and approximation algorithms.

DSC 395T: Advanced Linear Algebra for Computation
Linear algebra invariably lies at the core of techniques that are of critical importance to computational and data scientists. In this course, you learn advanced concepts in linear algebra, practical algorithms for matrix computations, and how floating point arithmetic as performed by computers affects correctness.

DSC 395T: Optimization
This class covers linear programming and convex optimization. These are fundamental conceptual and algorithmic building blocks for applications across science and engineering. Indeed any time a problem can be cast as one of maximizing / minimizing and objective subject to constraints, the next step is to use a method from linear or convex optimization. Covered topics include formulation and geometry of
LPs, duality and min-max, primal and dual algorithms for solving LPs, Second-order cone programming (SOCP) and semidefinite programming (SDP), unconstrained convex optimization and its algorithms: gradient descent and the newton method, constrained convex optimization, duality, variants of gradient descent (stochastic, subgradient etc.) and their rates of convergence, momentum methods.

Prerequisite: DSC 388G: Algorithms: Techniques and Theory


Will I have an instructor or TA interaction?
Interaction between instructors and/or TA’s will be available through email, online discussion boards, and virtual office hours.

Are lectures live, will I need to show up at a certain time?
The lectures are pre-recorded and will be made available that week for you to view on your own time. Within a courses’ prescribed timeline, students can move at their own pace following a self-regulating learning process. They complete interactive assessments and receive instant feedback.

What kind of interactions will I have with students and teaching staff?
All interactions, assistance, and correspondence with faculty, TAs, or staff will be conducted through online channels (email, piazza, zoom, etc). Interaction between faculty and students will primarily take place through the edX content delivery platform. Open edX technology allows instructors to create engaging learning sequences, which promote active participation as students alternate between learning concepts, solving exercises to check their understanding, and completing graded assessments. Other platforms may be involved depending upon which course you take. Examples include Canvas, Piazza, slack, etc.

What kind of laptop is needed for this program?
Either a MacBook Pro or a Windows PC will suffice.

MAC: OSX High Sierra 10.13.6 or higher; PC: Windows 10
MAC: Intel / AMD Processor, 8 GB RAM; PC: Dual-core 2.4 Ghz CPU, 4 GB RAM or better
Mozilla Firefox v20.0 or Higher, Google Chrome v25.0 or higher
Javascript Enabled & Third Party Cookies Enabled
800 x 600 resolution or better
Cable Modem, DSL or better (300 kbps download, 250 kbps upload)


Have a question that you can't find the answer to?

Complete our interest form to receive more information about the Master of Science in Data Science as it becomes available.

Tell Me More