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M.S. in Statistics: Data Science

The increasing importance of big data in engineering and the applied sciences motivates the Department of Statistics to offer a M.S. track that trains students in data science with a computational focus.

This focused M.S. track is developed within the structure of the current M.S. in Statistics and current 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 through the general master's core and programming requirements, in addition to providing fundamental data science education through general and focused electives requirement from courses in data sciences and related areas.


2019-20 Data Science program curriculum


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 five core areas.


Requirement 1 : Foundational (12 units) 

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

Course Name & number Course TItle Units
CME 302 Numerical Linear Algebra 3
CME 305 Discrete Mathematics and Algorithms 3
CME 307 Optimization 3
CME 308 Stochastic Methods in Engineering 3
Randomized Algorithms and Probabilistic Analysis 3
STATS 310A Theory of Probability 3

Requirement 2 : Data Science Electives (12 units)

Data Science electives should demonstrate breadth of knowledge in the technical area. The elective course list is defined. Courses outside this list are subject to approval. Courses in this area must be taken for letter grades.

Course Name & number Course TItle Units
STATS 200 Introduction to Statistical Inference 3
or STATS 300A Theory of Statistics I 3
STATS 203 Introduction to Regression Models and Analysis of Variance (spring quarter) 3
or STATS 305A Introduction to Statistical Modeling
STATS 315A Modern Applied Statistics: Learning 2-3
STATS 315B Modern Applied Statistics: Data Mining 2-3
or equivalent courses as approved by the adviser.  

Requirement 3: Advanced Scientific Programming and High Performance Computing Core (6 units)

To ensure that students have a strong foundation in programming, 3 units of advanced scientific programming for letter grade at the level of CME212 and three units of parallel computing for letter grades are required.

Note: Programming proficiency at the level of CME211 is a hard prerequisite for CME212 (students may ONLY place out of 211 with prior written approval*). CME211 can be applied towards elective requirement.

Course Name & number Course TItle Units
Advanced Scientific Programming; take 3 units  
CME 211 Software Development for Scientists and Engineers (can only be used as an elective) 3
CME 212 Advanced Software Development for Scientists and Engineers 3
Parallel Computing/HCP courses: (3 units)  
CME 213 Introduction to parallel computing using MPI, openMP, and CUDA 3
CME 323 Distributed Algorithms and Optimization 3
CME 342 Parallel Methods in Numerical Analysis 3
CS 149 Parallel Computing 3-4
CS 316 Advanced Multi-Core Systems 3
CS 344C, offered in previous years, may also be counted  

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.

Requirement 4 : Specialized Electives (9 units)

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

Course Name & number Course TItle Units
BIOE 214 Representations and Algorithms for Computational Molecular Biology 3-4
BIOMEDIN 215 Data Driven Medicine 3
BIOS 221/STATS 366 Modern Statistics for Modern Biology 3
CS 224W Social and Information Network Analysis 3-4
CS 229 Machine Learning 3-4
CS 231N Convolutional Neural Networks for Visual Recognition 3-4
CS 246 Mining Massive Data Sets 3-4
CS 448 Topics in Computer Graphics 3-4
ECON 293 Machine Learning and Causal Inference 3
ENERGY 240 Geostatistics 3
OIT 367 Business Intelligence from Big Data 3
PSYCH 204A Human Neuroimaging Methods 3
STATS 290 Computing for Data Science 3

Requirement 5 : Practical Component

Students are required to take 6 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 250 Data Challenge Lab, and ENGR 350 Data Impact Lab.

  • Other courses that have a strong hands-on and practical component, such as STATS 390 Consulting Workshop up to 1 unit.

Data Science Sample Schedules

The Data Science track schedule typically spans 5 quarters.

5 quarter schedule for most students:
Year 1:

Aut: CME 200, CME211, STATS200
Wtr: CME212, CME307, STATS200
Spr: STATS203 (18-19 AY), STATS315B, CME308, elective
Year 2:
Aut: CME302, STATS305A, HPC course (or take CME213 in spring), practical
Wtr: CME305, STATS315A, practical, elective

5 quarter schedule for students who are well prepared:
The student must have taken the equivalent of CME200 and STATS200 before starting the program.
Year 1:
Aut: CME211, STATS305A, elective
Wtr: CME212, CME307, STATS200
Spr: CME213, STATS315B, CME308
Year 2:
Aut: CME302, practical, elective
Wtr: CME305, STATS263, STATS315A, elective

4 quarter schedule:
This schedule is very demanding and students typically prefer the experience gained with a 5 quarter schedule.

The student must have taken the equivalent of CME200 and STATS200 (Aut or Wtr) before starting the program.
Year 1:
Aut: CME211, STATS305A, elective
Wtr: CME212, CME305, CME307, STATS315A
Spr: CME213, STATS315B, CME308, practical
Year 2:
Aut: CME302, elective (2), practical


  1. Because CME211 is the pre-requisite to CME212, those who take CME211 will be able to count it as an elective.
  2. CME302 requires the equivalent of CME200 as prerequisite.
  3. STATS305A requires the equivalent of STATS200 as prerequisite.
  4. STATS315A requires the equivalent of STATS200 and (STATS203 or 305A) as prerequisite.