General

Program Title
Statistics (MS)
Degree Type
MS - Master of Science
Undergraduate/Graduate
Graduate
Department(s)
Statistics
Program Overview

The University’s basic requirements for the M.S. degree are discussed in the “Graduate Degrees” section of this bulletin. The following are specific departmental requirements.

The M.S. in Statistics and the M.S. in Statistics, Data Science track, are intended as terminal degree programs and do not lead to the Ph.D. program in Statistics. Students interested in pursuing doctoral study in Statistics should apply directly to the Ph.D. program.

Admission

Prospective applicants should consult the Graduate Admissions and the Statistics Department admissions webpages for complete information on admission requirements and deadlines.

Recommended preparatory courses include advanced undergraduate level courses in linear algebra, statistics/probability and proficiency in programming.

Stanford students interested in the Data Science track (subplan) in Statistics must apply as external candidates. Visit Graduate Admissions to start an application.

Coterminal Master's Program

Stanford undergraduates who want to apply for the coterminal master's degree in Statistics must submit a complete application to the department by the deadline published on the department's coterminal admissions webpage.

Applications are accepted twice a year in autumn and winter quarters for winter and spring quarter start, respectively. The general GRE is not required of coterminal applicants.

Students pursuing the Statistics coterminal master's degree must follow the same curriculum requirements stated in the Requirements for the Master of Science in Statistics section.

University Coterminal Requirements

Coterminal master’s degree candidates are expected to complete all master’s degree requirements as described in this bulletin. University requirements for the coterminal master’s degree are described in the “Coterminal Master’s Program” section. University requirements for the master’s degree are described in the "Graduate Degrees" section of this bulletin.

After accepting admission to this coterminal master’s degree program, students may request transfer of courses from the undergraduate to the graduate career to satisfy requirements for the master’s degree. Transfer of courses to the graduate career requires review and approval of both the undergraduate and graduate programs on a case by case basis.

In this master’s program, courses taken during or after the first quarter of the sophomore year are eligible for consideration for transfer to the graduate career; the timing of the first graduate quarter is not a factor. No courses taken prior to the first quarter of the sophomore year may be used to meet master’s degree requirements.

Course transfers are not possible after the bachelor’s degree has been conferred.

The University requires that the graduate advisor be assigned in the student’s first graduate quarter even though the undergraduate career may still be open. The University also requires that the Master’s Degree Program Proposal be completed by the student and approved by the department by the end of the student’s first graduate quarter.


Program Learning Outcomes

A core foundation in probability theory, theoretical statistics, applied statistics, stochastic processes and five additional Statistics courses must be taken from the Statistics catalog.

External Credit Policies
-

Degree Requirements

Curriculum and Degree Requirements

The department requires that a master's student take 45 units of work from offerings in the Department of Statistics or from authorized courses in other departments. With the advice of the master's program advisors, each student selects his or her own set of electives.

All requirements for a master's degree, including the coterminal master's degree, must be completed within three years after the student's first term of enrollment in the master's program. Ordinarily, four or five quarters are needed to complete all requirements. Honors Cooperative students must finish within five years.

Units for a given course may not be counted to meet the requirements of more than one degree, with the exception that up to 45 units of a Stanford M.A. or M.S. degree may be applied to the residency requirement for the Ph.D., D.M.A. or Engineer degrees. See the "Residency Policy for Graduate Students" section of this Bulletin for University rules.

As defined in the general graduate student requirements, students must maintain a grade point average (GPA) of 3.0 (or better) for courses used to fulfill degree requirements and classes must be taken at the 200 level or higher.

Master's Degree Program Proposal

The Statistics Master's Degree Program Proposal form must be signed and approved by the department's student services administrator before submission to the student's program advisor. This form is due no later than the end of the first quarter of enrollment in the program.

A revised program proposal must be submitted if degree plans change.

There is no thesis requirement.

For further information about the Statistics master's degree program requirements, see the program's webpage.

1. Statistics Core Courses (must complete all four courses):

Course List

Units

Probability

course

Theory of Probability 1

4

Applied Statistics

course

Introduction to Regression Models and Analysis of Variance

3

or course

Applied Statistics I

or course

Introduction to Applied Statistics

Theoretical Statistics

course

Introduction to Statistical Inference

3-4

or course

Theory of Statistics I

or course

A Course in Bayesian Statistics

Stochastic Processes

course

Introduction to Stochastic Processes I 1,2

3

or course

Introduction to Stochastic Processes II

or course

Stochastic Processes

or course

Modern Markov Chains

Students with prior background may replace each course with a more advanced course from the same area, or a more advanced course offered by the department, with consent of the adviser. All must be taken for a letter grade.

2. Statistics Depth:

Five additional Statistics courses must be taken from graduate offerings in the department (at or above the 200-level). During the 2020-21 academic year, three of five courses must be taken for a letter grade (with the exception of courses that may only be offered satisfactory(S)/credit (CR) only).

The following courses that may only be used to fulfill elective credit 3course Workshop in Biostatistics series, course Independent Study, course Industrial Research for Statisticians, and course Consulting Workshop (see list of electives below).

Course List

Units

Courses which may be offered by the department:

course

Data Mining and Analysis

3

course

Introduction to Regression Models and Analysis of Variance (course)

3

course

Sampling

3

course

Introduction to Nonparametric Statistics

3

course

Applied Multivariate Analysis

3

course

Introduction to Time Series Analysis

3

course

Bootstrap, Cross-Validation, and Sample Re-use

3

course

Topics in Causal Inference

3

course

Meta-research: Appraising Research Findings, Bias, and Meta-analysis

3

course

Statistical Models in Biology

3

course

Introduction to Statistical Learning

3

course

Statistical Methods for Longitudinal Research

2-3

course

Machine Learning

3-4

or course

Machine Learning

course

Investment Portfolios, Derivative Securities, and Risk Measures

3

course

Statistical Methods in Finance

3

course

Data-driven Financial Econometrics

3

course

Quantitative Trading: Algorithms, Data, and Optimization

2-4

course

Data, Models and Applications to Healthcare Analytics

3

course

Mathematical Finance

3

course

Design of Experiments

3

course

Advanced Statistical Methods for Observational Studies

2-3

course

A Course in Bayesian Statistics

3

or course

A Course in Bayesian Statistics

course

Applied Bayesian Statistics

3

or course

Applied Bayesian Statistics

course

Massive Computational Experiments, Painlessly

2

course

Computing for Data Science

3

course

Theory of Statistics I

3

or course

Theory of Statistics II

or 

Theory of Statistics III

course

Applied Statistics I

3

or course

Applied Statistics II: Generalized Linear Models, Survival Analysis, and Exponential Families

or course

Applied Statistics III

course

Theory of Probability I

3

or course

Theory of Probability II

or course

Theory of Probability III

course

Information Theory and Statistics

3

or course

Information Theory and Statistics

course

Advanced Statistical Theory

3

course

Modern Applied Statistics: Learning

3

course

Modern Applied Statistics: Data Mining

3

course

Stochastic Processes

3

course

Modern Markov Chains

3

course

Literature of Statistics

1

course

Function Estimation in White Noise

3

course

Multivariate Analysis and Random Matrices in Statistics

3

course

Mathematics and Statistics of Gambling

3

course

Topics in Mathematical Physics

3

or course

Topics in Mathematical Physics

course

Causal Inference ((NEW))

3

course

Theory and Applications of Selective Inference ((NEW))

3

course

Design of Experiments

3

course

Modern Statistics for Modern Biology

3

course

Large Deviations Theory

3

or course

Large Deviations Theory

course

Empirical Process Theory and its Applications

3

course

Methods from Statistical Physics

3

course

Information Theory

3

course

Topics in Information Theory and Its Applications

3

or course

Topics in Information Theory and Its Applications

course

Analyses of Deep Learning

1

3. Linear Algebra Requirement:

Course List

Units

Must be taken for a letter grade, with the exception of courses offered satisfactory/no credit only.

Select one of the following:

course

Applied Matrix Theory

3

course

Linear Algebra and Matrix Theory

3

course

Functions of a Real Variable

3

course

Fundamental Concepts of Analysis

3

course

Numerical Linear Algebra

3

course

Convex Optimization I

3

or course

Convex Optimization II

Substitution of more advanced courses in Mathematics, that provide similar skills, may be made with consent of the adviser.

4. Programming Requirement:

Course List

Units

2020-21: May be taken for a letter grade or CR.

Select one of the following:

course

Programming Methodology

3

course

Programming Abstractions

3

course

Programming Abstractions

3

course

Computer Organization and Systems

3-5

course

Introduction to Scientific Computing

3

Substitution more advanced courses in Computer Science (140 - 181), that provide similar skills, may be made with consent of the adviser.

5. Breadth/Elective Courses:

Courses that provide breadth to the degree may be chosen as elective units to complete the degree requirements. List of suggested of courses available from the program's webpage. Other graduate courses (200 or above) may be authorized by the advisor if they provide skills relevant to degree requirements or deal primarily with an application of statistics or probability and do not significantly overlap (repeat) courses in the student's program.

There is sufficient flexibility to accommodate students with interests in applications to business, computing, economics, engineering, health, operations research, and biological and social sciences.

Courses that fulfill elective units may be taken concerning 'CR' (credit) or 'S' (satisfactory).

Course List

Students may enroll in up to 6 units of the following workfshops and training seminars to fulfill elective coursework: 3

course

NeuroTech Training Seminar

1

course

Workshop in Biostatistics

1-2

course

Workshop in Biostatistics

1-2

course

Workshop in Biostatistics

1-2

course

Industrial Research for Statisticians

1

course

Independent Study

1-5

course

Consulting Workshop

1

Courses below 200 level are not acceptable with the following exceptions; however, students are strongly advised to avoid redundancy in coursework:

Course List

Units

course

Introduction to Applied Statistics

3

course

Functions of a Real Variable

3

course

Fundamental Concepts of Analysis

3

course

Programming Methodology

3-5

course

Programming Abstractions

3-5

course

Programming Abstractions

3-5

course

Operating Systems and Systems Programming

3-4

course

Web Applications

3

course

Compilers

3-4

course

Introduction to Computer Networking

3-4

course

Data Management and Data Systems

3-4

course

Introduction to Human-Computer Interaction Design

3-5

course

Introduction to Computer Graphics and Imaging

3-4

course

Parallel Computing

3-4

course

Introduction to the Theory of Computation

3-4

course

Computer and Network Security

3

course

Computational Logic

3

course

Design and Analysis of Algorithms

3-5

course

Stanford Laptop Orchestra: Composition, Coding, and Performance

1-5

course

Computers, Ethics, and Public Policy 4

4

And at most, one of these courses ay be counted as an elective. 4

course

Applied Matrix Theory

3

course

Linear Algebra and Matrix Theory

3

course

Theory of Probability

4

1

Students who replace STATS 116 with STATS 217 must take a second course in Stochastic Processes or Probability.

2

Enrollment in STATS 116 after successful completion of STATS 217, 218, and/or 219, may not be used to fulfill degree requirements, including as an elective.

3

 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.

4

Enrollment in a course that provides redundant coursework cannot be used to fulfill the M.S. degree requirements.


Master of Science in Statistics, Data Science Track

The Data Science track5 develops strong mathematical, statistical, and computational and programming skills through the general master's core and programming requirements. In addition, it provides a fundamental data science education through general and focused electives requirement from courses in data sciences and related areas.  Course choices are limited to predefined courses from the data sciences and related courses group. The final requirement is a practical component to be completed through capstone project, data science clinic, or other courses that have strong hands-on or practical component, such as statistical consulting.

Admission

Prospective applicants should consult the Graduate Admissions and the Statistics Department admissions webpages for complete information on admission requirements and deadlines.

Applicants apply to the Master of Science degree program in Statistics and subsequently declare their preference for the Data Science track (subplan) within the graduate application ("Department Specialization" option).

Prerequisites

Recommended preparatory courses include advanced undergraduate level courses in linear algebra and probability, and introductory courses in stochastic processes, numerical methods and proficiency in programming (Basic usage of the Python and C/C++ programming languages).

Curriculum and Degree Requirements

As defined in the general graduate student requirements, students must maintain a grade point average (GPA) of 3.0 or better and classes must be taken at the 200 level or higher. Students must complete 45 units of required coursework in Data Science.

Master's Degree Program Proposal

The Statistics (Data Science) Master's Degree Program Proposal form must be signed and approved by the department's student services administrator before submission to the student's program advisor. This form is due no later than the end of the first quarter of enrollment in the program.

A revised program proposal must be submitted if degree plans change.

There is no thesis requirement.

The Data Science track (subplan) is printed on the student transcript and diploma.

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 List

Units

course

Introduction to Statistical Inference

3

or course

Theory of Statistics I

course

Introduction to Regression Models and Analysis of Variance

3

or course

Applied Statistics I

course

Modern Applied Statistics: Learning

3

or course

Machine Learning

course

Numerical Linear Algebra

3

course

Stochastic Methods in Engineering

3

Experimentation (3 units)

Experimental method and causal considerations are fundamental to data science. The course chosen from this area must be taken for letter grades.

Course List

Units

course

Design of Experiments

3

course

Topics in Causal Inference

3

Software Development & Scientific Computing (6 - 9 units)

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

Software Development: (3 units)

Minimum of 3 units in scientific computing. (Additional 3 units for those who need to take CME211.)

ICME offers a placement test Summer Quarter. Students who pass this placement test are not required to take CME 211. Courses in this area must be taken for letter grades.

Course List

Units

course

Advanced Software Development for Scientists and Engineers (prerequisite: course)

3

Programming proficiency at the level of course is a hard prerequisite for course. can be waived with placement exam (summer).

Scientific Computing Foundations and Methods (minimum 3 units)

Course List

Units

course

Introduction to parallel computing using MPI, openMP, and CUDA

3

course

Discrete Mathematics and Algorithms

3

course

Optimization

3

course

Distributed Algorithms and Optimization

3

course

Convex Optimization I

3

course

Mining Massive Data Sets

3-4

Students may take 6 units as CR/S in Scientific Computing or Machine Learning for the 2020-21 academic year.

Machine Learning Methods and Applications (6 - 9 units) 

Ordinarily, courses in machine learning should be taken for letter grades. Students may take two courses as 'CR' (credit) or 'S' (satisfactory) for academic year 2020-21.

Course List

Units

course

Modern Applied Statistics: Data Mining

3

course

Artificial Intelligence: Principles and Techniques

3-4

course

Natural Language Processing with Deep Learning

3-4

course

Deep Learning

3-4

course

Convolutional Neural Networks for Visual Recognition

3-4

course

Reinforcement Learning

3

course

Deep Generative Models

3

Students may take 6 units as CR/S in Scientific Computing or Machine Learning for the 2020-21 academic year.

 Practical Component (3 units)

A Capstone project, supervised by a faculty member and approved by the student's advisor. The capstone project should ideally build on the work done in the student’s coursework. Students should submit a one-page proposal, supported by the faculty member and sent to the student's Data Science advisor for approval (at least one quarter prior to start of project).

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

Course List

Units

course

Analytics Accelerator (Real-world project-based research; Application required; Autumn quarter commitment, winter quarter optional.)

3

course

Data Challenge Lab (https://datalab.stanford.edu/challenge-lab)

3-5

course

Data Impact Lab (https://datalab.stanford.edu/impact-lab)

1-6

course

Independent Study

1-5

or course

Master's Research

course

Consulting Workshop (repeatable)

1


Electives (6 - 9 units)

Courses in data science, machine learning, statistics, advanced programming or practical components, chosen in consultation with the student’s course advisor.