The Department of Statistics Data Science curriculum (202021)
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: statadmissionsms{at}LISTS[.]STANFORD[.]EDU>>>
This program is not an online degree program.
201920 Data Science Program Curriculum
202021 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 202021 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).
Coursework
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 & largescale 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: (3–6 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  34  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  34  Win 
CS 230  Deep Learning  34  Aut, Win, Spr 
CS 231N  Convolutional Neural Networks for Visual Recognition  34  Spr 
CS 234  Reinforcement Learning  3  Win 
CS 236  Deep Generative Models  34  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 onepage 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 DataDriven Impact

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

Other courses that have a strong handson 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 201920 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 5quarter 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.
Notes:
 Because CME211 is the prerequisite to CME212, those who take CME211 will be able to count it as an elective. (Placement exam administered in summer.)
 Students may use STATS 116 as an elective if they do not place in STATS200 when starting the program.
 CME302 requires the equivalent of CME200 as prerequisite.
 STATS315A requires the equivalent of STATS200 and (STATS203 or 305A) as a prerequisite.