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Data Science track (2019-20)

The Department of Statistics has modified its original Data Science track curriculum to meet the interests and demands of it students.

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.


Admissions questions should be addressed to: stat-admissions-ms{at}LISTS[.]STANFORD[.]EDU>>>


This program is not offered as an online degree program.


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 and Statistical Foundations
  • Scientific Computing (includes software development and large-scale computing)
  • Experimentation
  • Applied Machine Learning and Data Science
  • Elective course  in the data sciences
  • Practical Component


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
STATS 203 Introduction to Regression Models and Analysis of Variance 3



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

Scientific Computing  (6 units)

To ensure that students have a strong foundation in programming, 3 units of scientific programming CME212 and 3 units of parallel computing.

ICME offers a placement test that can be used to directly enroll in CME 212. 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
Scientific Computing: (3 units)    
CME 212 Advanced Software Development for Scientists and Engineers (prerequisite: CME 211 ) 3 Win
Large-Scale Computing: (3 units)    
CME 213 Introduction to parallel computing using MPI, openMP, and CUDA 3 Win
CME 323 Distributed Algorithms and Optimization 3 Spr
CS 246 Mining Massive Data Sets 3-4 Win

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, for example, taking CME211 Programming in C/C++ for Scientists and Engineer or equivalent course* with adviser's approval.

Experimentation Elective (3 units)

Courses in this area must be taken for letter grades.

Course Name & number Course TItle Units Term
STATS 204 Sampling 3 Spr
STATS 266 Advanced Statistical Methods for Observational Studies 2-3 Spr
ECON 271 Intermediate Econometrics II 2-5 Win
MS&E 327 Topics in Causal Inference 3 Aut




Applied Machine Learning and Data Science (9 units)

Choose three courses from the following list. Courses outside this list are subject to approval.

Courses in this area must be taken for letter grades.

Course Name & number Course TItle Units Term
CME 364A Convex Optimization I 3 Win
STATS 231 Statistical Learning Theory 3  
STATS 245 Data, Models and Applications to Healthcare Analytics 3 Sum
STATS 315B Modern Applied Statistics: Data Mining 3 Spr
CS 221 Artificial Intelligence: Principles and Techniques 3-4 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


Electives (9 units)

In consultation with the student's program adviser, the student selects courses in scientific or engineering application area of interest.


Practical Component

Students are required to take 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.

  • Project labs offered by Stanford Data Lab: ENGR 150 Data Challenge Lab (3 - 5 units), and ENGR 350 Data Impact Lab (1 - 6 units).

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


Students admitted to the Statistics M.S. program prior to academic year 2018-19 fulfill the requirements in effect at the time of their admission.


The Data Science track schedule typically spans 5 quarters.

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)



  1. Because CME211 is the pre-requisite to CME212, those who take CME211 will be able to count it as an elective.
  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.