How to Apply
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Application Deadline
The priority application deadline for summer 2026 admission is December 5. The final application deadline is March 1.
Notification of Admission Decision
Applicants are typically contacted with an admission decision six to eight weeks after the final application date, though many applicants receive a decision before that time. All applicants should expect a final admission decision by May 15. While applications are reviewed on a rolling basis, early submission does not guarantee an early decision. Applications completed after March 1 are reviewed on a space-available basis.
Requirements
The application process is entirely online. Please complete and/or upload all of the items listed below to your application account. There is no need to mail items to our office.
Bachelor’s Degree
Applicants who hold a bachelor's degree in any field and have fulfilled the MS in Data Science prerequisites are considered for admission. Applicants with academic or professional backgrounds in math, computer science, engineering, finance, economics or equivalent skills are encouraged to apply.
Prerequisite Requirements
Courses should be completed for a grade on a transcript at an accredited institution. Unofficial transcript copy from an accredited institution needs to show class titles with grades (marks). Certificate coursework (with the exception of the certificates offered through the USF Data Institute and taught by affiliated instructors, listed below) does not meet our requirements but can serve as a supplement to courses taken for college credit. Professional or personal experience may substitute for coursework in some cases, though formal coursework is preferred.
Approved Courses from the USF Data Institute
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- Types of data (e.g., binary, categorical, ordinal, continuous, etc.)
- Elementary probability (e.g., the Law of Total Probability, Bayes’ Theorem, etc.)
- Probability density functions, probability mass functions
- Cumulative distribution functions and their properties
- Random variable and expected value/variance
- Conditional probability distributions and conditional expected value
- Laws of large numbers and Central Limit Theorem
- Confidence intervals, and their interpretations
- Hypothesis testing, and how to correctly interpret p-values
- Common statistical distributions (normal, t, chi-squared, F ratio, etc.)
- Definition of a statistic, and ways of finding statistics (e.g., likelihood functions)
- Regression and correlation
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- Ability to write structure programs in a high-level language (for example: objects, methods, functions)
- Ability to read/write data from files
- Facility with basic control structures (block, conditional, iteration)
- Understanding of variables and data types (numeric, string, boolean)
- Familiarity with basic data structures (sequence, dictionary, set, stack, queue)
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- Systems of linear equations
- Row reduction and echelon forms
- Matrix operations and properties
- General properties of vectors
- Orthogonal bases and orthogonal projections
- Linear transformations in two and three dimensions
- Eigenvalues and eigenvectors
Recommended Courses
In addition to the above requirements, we strongly recommend courses in calculus and data structures.
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1. Limits and Continuity
- Limits: Grasp the concept of approaching a value as a function's input approaches a specific point.
- Continuity: Know when a function is continuous and how it affects the behavior of functions.
2. Derivatives
- Definition of Derivative: Understand the derivative as the rate of change or the slope of a function at any given point.
- Basic Differentiation Rules:
- Power Rule
- Product Rule
- Quotient Rule
- Chain Rule
- Applications: Understand how to apply derivatives to find the slope, rates of change, and for optimization problems.
3. Partial Derivatives
- Multivariable Functions: Know how to compute derivatives for functions with more than one variable.
4. Integrals
- Definite and Indefinite Integrals: Understand how to compute areas under curves and the reverse of differentiation.
- Basic Integration Techniques:
- Substitution Rule
- Integration by Parts
- Understanding the Fundamental Theorem of Calculus
5. Multivariable Calculus
- Gradient and Directional Derivatives: Understand how to calculate the gradient and its significance in optimization problems
- Double and Triple Integrals
6. Vector Calculus
- Vector Fields: Understand the concept of vectors and how they are used in multivariable functions.
- Gradient, Divergence, and Curl
7. Optimization
- Critical Points: Identifying maxima, minima, and saddle points using derivatives.
- Convexity: Understanding the importance of convex functions in optimization problems.
Test Requirements
For international applicants, an English Language exam score is required. For more information on accepted test formats and exceptions please visit the English Language Proficiency page. The GRE general test is optional.
USF school code for GRE and TOEFL is 4850.
Transcript (Academic Record)
Upload a copy of your transcript from each university and college attended. Please ensure the institution’s name and your name is on the document you upload. Transcripts from any schools and study abroad programs must be submitted since Bachelor's Degree institutions typically do not list transfer and study abroad courses with grades. If still enrolled in your Bachelor's program, submit a copy of your transcript showing in-progress courses. If admitted, an official transcript with proof of graduation is required.
Statement of Purpose
The statement of purpose is a one-to-two page statement that briefly describes your educational and work experience as it relates to the MS in Data Science program. The majority of the essay should focus on your career goals, and why the USF MS in Data Science program is a good fit with your goals. You may also use this statement to explain any deficiencies in your academic record (e.g., failing or low grades in quantitative courses such as math, economics, computer science, or engineering; a low GPA in one semester, year, or degree program; lack of relevant coursework that is compensated by self-study or work experience, etc.), gaps in educational or employment history, and any other topics in your background and experience that you would like to address.
Resume or CV
Your current resume or CV lists and describes your educational history, work or job history, coursework related to this graduate program, and any experiences relevant to your pursuit of graduate studies.
Letters of Recommendation
A minimum of two recommendation letters are required for your application (four maximum). Preferably, one letter is from an academic reference, and the other from a professional context. The letters should focus on your relevant experience and an overall fit for graduate studies. Contributions to teamwork are also appreciated.
Follow the instructions in the online application to provide the contact information for your recommenders. We require that letters are signed (electronic signature is fine) and/or on official company or university letterhead in order to be considered. Please keep this in mind when contacting your references.
You may submit your application before your recommenders upload their letters.
Admission Interview
Applicants whose applications are selected by our admission committee for further review will be contacted for an admission interview. The interview will cover foundational topics in statistics, linear algebra and computer programming and is conducted by an MSDS faculty member. Successful completion of an interview is required for admission.
How to Prepare for the Interview
Programming
Interviewers will tend to share sample Python code during the interview and ask about the expected behavior of the given code (or functions defined through code) and any inputs or outputs. Questions may cover topics such as function definitions, data types, operations, conditional statements, iterations, error handling, etc.
Probability and Statistics
Interviewers will tend to ask questions of three different types. The first type of question involves remembering a basic definition (e.g., of the sample median of set of data) or a basic fact from probability and statistics (e.g., the sample median is less sensitive to the presence of outliers than the sample mean). The second type of question requires a greater level of interpretational sophistication (e.g., explain the interpretation of a p-value, given some null hypothesis and alternative hypothesis in the context of some set of data). The third type of question is much more challenging and is more open-ended (e.g., explain the meaning of the Central Limit Theorem in your own words).
Linear Algebra
Interviews will tend to ask questions of three different types. The first type of question involves remembering a basic definition (e.g., what does it mean for a set of vectors to be linearly independent). The second type of question requires a greater level of interpretational sophistication (e.g., explain what the relationship between an eigenvalue and eigenvector is, and what their geometric interpretation is). The third type of question might require you to listen to a mathematical statement and then determine whether or not it is true or false. You would then have to explain why the statement is true or give a counterexample to explain why it is false.
International Applicants
There are additional items and instructions for international applicants.
Data Science, MS
101 Howard Street
San Francisco, CA 94105