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M.S. in Statistics: Data Science

The Department of Statistics Data Science curriculum

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.

This program is not an online degree program.

 

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

 

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).

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

 

2019-20 Data Science program curriculum


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: (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

3-4

Win

 

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

Must be taken for a letter grade.

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

Courses outside this list are subject to approval.      

Practical Component (3 units)

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.

  • Project labs offered by Stanford Data Lab: ENGR 150 Data Challenge Lab (3 - 5 units), and 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 scientific or engineering application area of interest.
With consent of advisor, 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 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)

 

Notes:

  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.