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M.S. in Statistics: Data Science

The Department of Statistics Data Science curriculum (2020-21)

This focused M.S. track is developed within the structure of the current M.S. in Statistics and new trends in data science and analytics. Upon the successful completion of the Data Science M.S. degree students will be prepared to continue on to related doctoral program or as a data science professional in industry. Completing the M.S. degree is not a direct path for admission to the Ph.D. program in Statistics.

After reading through the admissions FAQ, admissions questions may be addressed to: stat-admissions-ms{at}LISTS[.]STANFORD[.]EDU>>>

This program is not an online degree program.

2019-20 Data Science Program Curriculum


2020-21 Academic Year Grade Requirements:

The Statistics department’s M.S. program in Data Science has modified its policy concerning 'CR' (credit) or 'S' (satisfactory) grades in degree requirements requiring a letter grade for academic year 2020-21 as follows: Students may take two courses as 'CR' (credit) or 'S' (satisfactory) in Machine Learning and/or Scientific Computing Foundations (up to 6 units).


The Data Science track develops strong mathematical, statistical, computational and programming skills, in addition to providing fundamental data science education through general and focused electives requirement from courses in data sciences and other areas of interest.

As defined in the general Graduate Student Requirements, students have to maintain a grade point average (GPA) of 3.0 or better and classes must be taken at the 200 level or higher. Students satisfying the course requirements of the Data Science track do not satisfy the other course requirements for the M.S. in Statistics

The total number of units in the degree is 45, 36 of which must be taken for a letter grade.

Submission of approved Master's Program Proposal, signed by the master's adviser, to the student services officer by the end of the first quarter of the master's degree program. A revised program proposal is required to be filed whenever there are changes to a student's previously approved program proposal.

There is no thesis requirement.

Data Science Proposal Forms

Students must demonstrate breadth of knowledge in the field by completing coureses in these core areas.

  • Mathematical & Statistical Foundations (15 units)
  • Experimentation (3 units)
  • Scientific Computing (includes software development & large-scale computing) (6 units minimum)
  • Machine Learning Methods & Applications (6 units minimum)
  • Practical Component (3 units)
  • Elective course in the data sciences (remainder of 45 units)

Mathematical and Statistical Foundations (15 units)

Students must demonstrate foundational knowledge in the field by completing the following courses. Courses in this area must be taken for letter grades.

Course Name & number Course TItle Units Term
STATS 200 Introduction to Statistical Inference 3 Aut, Win
or 300A Theory of Statistics I 3 Aut
STATS 203 Introduction to Regression Models and Analysis of Variance 3 Win
or STATS 305A Applied Statistics I 3 Aut
STATS 315A Modern Applied Statistics: Learning 3 Win
CME 302 Numerical Linear Algebra 3 Aut
CME 308 Stochastic Methods in Engineering 3 Spr

Experimentation Elective (3 units)

Courses in this area must be taken for letter grades.

Course Name & number Course TItle Units Term
STATS 263 Design of Experiments 3 Win
ECON 271 Intermediate Econometrics II 3 Win
or MS&E 327 Topics in Causal Inference 3 Aut

Software Development and Scientific Computing  (6 units minimum)

To ensure that students have a strong foundation in programming, 3 units of software development (CME212) and minimum 3 units of scientific computing.

  • Students who do not start the program with a strong computational and/or programming background will take an extra 3 units to prepare themselves by taking CME211* Programming in C/C++ for Scientists and Engineers, or equivalent course with adviser's approval.
  • Summer placement exam for CME 212 will be sent to matriculating students in July. Students who pass this placement test are not required to take CME 211, and may replace the class with an elective.

Courses in this area must be taken for letter grades.

Course Name & number Course TItle Units Term
Software Development: (3 units)    
CME 212 Advanced Software Development for Scientists and Engineers (prerequisite: CME 211* ) 3 Win
Scientific Computing: (36 units)    
CME 213 Introduction to parallel computing using MPI, openMP, and CUDA 3 Win
CME 305 Discrete Mathematics and Algorithms 3 Win
CME  307 Optimization 3 Win
CME 323 Distributed Algorithms and Optimization 3 Spr
CME 364A Convex Optimization I 3 Wint/Sum
CS 246 Mining Massive Data Sets 3-4 Win

Machine Learning Methods & Applications (6–9 units minimum)

Courses in this area must be taken for letter grades. Courses outside this list are subject to approval.

Course Name & number Course TItle Units Term
STATS 231 Statistical Learning Theory 3  
STATS 315B Modern Applied Statistics: Data Mining 3 Spr
CS 221 Artificial Intelligence: Principles and Techniques 3 Aut, Spr
CS 224N Natural Language Processing with Deep Learning 3-4 Win
CS 230 Deep Learning 3-4 Aut, Win, Spr
CS 231N Convolutional Neural Networks for Visual Recognition 3-4 Spr
CS 234 Reinforcement Learning 3 Win
CS 236 Deep Generative Models 3-4 Aut

Practical Component (3 units) OPTIONAL

Students are required to take minimum of 3 units of practical component that may include any combination of:

  • A capstone project, supervised by a faculty member and approved by the student's adviser. The capstone project should be computational in nature. Students should submit a one-page proposal, supported by the faculty member and sent to the student's Data Science adviser for approval (at least one quarter prior to start of project).

    • Master's Research: STATS 299 Independent Study. In consultation with your adviser, independent study/directed reading with permission of statistics faculty. (repeatable).

    •  BIODS 232: Consulting Workshop on Biomedical Data Science (1–2 units)

    • Gain practical industry experience and exposure to the organization, its industry, and the space in which it operates, Build relationships in the organization and industry, and gain an understanding of related career paths. ALP 301 Data-Driven Impact

    •  ENGR 350 Data Impact Lab (1 –6 units, when offered)

    • Other courses that have a strong hands-on and practical component, such as STATS 390 Consulting Workshop (repeatable).

Electives in data science (6–9 units)

In consultation with the student's program adviser, the student selects courses in a scientific or engineering application area of interest, i.e., courses 200 or above in STATS or CME. Minimum 6 units of elective coursework.

Students admitted to the Statistics M.S. program prior to academic year 2019-20 may fulfill the requirements in effect at the time of their admission.

The Data Science track schedule typically spans five quarters (not including summer quarter).

A 5-quarter schedule for most students:
Year 1

  • Aut – CME211, CME 302*, STATS200
  • Wtr – CME212, STATS203, STATS315A
  • Spr – CME308*, STATS204, STATS315B, STATS390 (practicum)

Year 2

  • Aut – CS221, CS230, STATS231
  • Wtr – CME213, CS224N, STATS299 (practicum)

* May postpone until year 2.


  1. Because CME211 is the pre-requisite to CME212, those who take CME211 will be able to count it as an elective. (Placement exam administered in summer.)
  2. Students may use STATS 116 as an elective if they do not place in STATS200 when starting the program.
  3. CME302 requires the equivalent of CME200 as prerequisite.
  4. STATS315A requires the equivalent of STATS200 and (STATS203 or 305A) as a prerequisite.