Data Science minor

The Statistics Department will accept letter grade or credit for all minor courses for 2020-21 academic year.

The Data Science minor has been designed for majors in the humanities and social sciences who want to gain practical know-how of statistical data analytic methods as it relates to their field of interest. The minor will provide students with the knowledge of exploratory and confirmatory data analyses of diverse data types (e.g. text, numbers, images, graphs, trees, binary input); strengthen social research by teaching students how to correctly apply data analysis tools and the techniques of data visualization to convey their conclusions. No previous programming or statistical background is assumed.

Learning Outcomes

Students are expected to:

  1. be able to connect data to underlying phenomena and to think critically about conclusions drawn from data analysis
  2. be knowledgeable about programming abstractions so that they can later design their own computational inferential procedures
  • All courses for the minor must be taken for a letter grade where offered, with the exception of the Data Mining requirement. 
  • Seven courses are required, equal to at least 22 units. 
  • A grade point average (GPA) of 2.75 is required for courses fulfilling the minor.

After declaring the minor in Axess, students must submit the Data Science minor form to Mason Perez, Sequoia Hall, Room 124, before they can be approved. ***To access the form, students must be logged in on their Stanford account, download the form to your desktop.***

Course Requirements

Linear Algebra (5 units)

Select one course:

  • Math 51: Linear Algebra, Multivariable Calculus, and Modern Applications
  • CME 100: Vector Calculus for Engineers
Programming (5 units)
  • CS 106A: Programming Methodology (CS 106AP and CS 106AJ also satisfy this requirement)
Programming in R

select one course

  • STATS 32: Introduction to R for Undergraduates (1 unit)
  • STATS 48N: Riding the Data Wave (3 units)
  • STATS 195: Introduction to R (1 unit)
  • THINK 3: Breaking Codes, Finding Patterns (4 units)
Data Science (3 units)

select one course

  • STATS 60: Introduction to Statistical Methods: Precalculus (PSYCH 10, STATS 160)

  • STATS 101: Data Science 101
  • STATS 191: Introduction to Applied Statistics*
  • MS&E 226: Fundamentals of Data Science: Prediction, Inference, Causality

  • CS 102: Working with Data - Tools and Techniques

Statistics (3 units)

select one course

  • ECON 102A: Introduction to Statistical Methods (Postcalculus) for Social Scientists
  • PHIL 166: Probability: Ten Great Ideas About Chance
  • STATS 48N: Riding the Data Wave
  • STATS 141: Biostatistics
  • STATS 191: Introduction to Applied Statistics*
  • STATS 211: Meta-research: Appraising Research Findings, Bias, and Meta-analysis
Data Mining and Analysis (3 units)

Select one course

  • STATS 202: Data Mining and Analysis
  • STATS 216: Introduction to Statistical Learning
Data Science Methodology from the cognate field of interest (2-3 units)

(Courses may not be offered every year, refer to explorecoursesselect at least one course

Suggested courses include, but are not limited to:

  • SOC 180A/B: Foundations of Social Research
  • SOC126: Introduction to Social Networks
  • PUBLPOL 105: Empirical Methods in Public Policy
  • PHIL 166: Ten Great Ideas About Chance
  • POLISCI 150B: Machine Learning for Social Scientists
  • LINGUIST 275: Probability and Statistics for linguists
  • MS&E 135: Networks
  • ENGLISH 184E: Literary Text Mining
  • CS 224W: Social and Information Network Analysis
  • ECON 291: Social and Economic Networks

To request consideration for a course not listed here, email the course syllabus to Mason Perez,

*Stats 191 cannot count for both the Statistics and Data Science requirements

Typical Paths to the Minor:

Freshman: Programming in R, Math 21, CS 106A
Sophomore: Linear Algebra, Data Science course
Junior: Statistics course, Data Science Methodology course
Senior: Data Mining and Analysis
Freshman: (AP Calculus), Programming in R, CS 106A
Sophomore: Linear Algebra, Data Science course
Junior: Statistics course, Data Science Methodology course
Senior: Data Mining and Analysis

Not sure if the Data Science minor is the right minor for you?

Take a look at the Statistics minor and MCS minor.

 Comparison of Minors

Any changes to the initial course of study should be approved in advance by the department.

*A note about double counting: For majors & minors with overlapping requirements, the courses that may be double counted are those from the MATH 50 series and CS 106A/B & STATS 60. Beyond these, students would need to find another suitable course to satisfy the requirements for the minor.

Data Science minor inquiries should be addressed to Mason Perez,