Data Science Minor
The Statistics Department will accept letter grade or credit for all minor courses for 202021 academic year.
The Data Science minor has been designed for majors in the humanities and social sciences who want to gain practical knowhow 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.
Starting on September 1, 2022, the data science minor is now offered through the Data Science program rather than the Statistics department. This change will not impact students’ experience with the minor.
Learning Outcomes
Students are expected to:
 be able to connect data to underlying phenomena and to think critically about conclusions drawn from data analysis
 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 the aekuhn [at] stanford.edu (Student Services Officer) via email or in person at Sequoia Hall, Room 124, before they can be approved. To access the form, you must login to your Stanford account; then 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 (Note: STATS 191 cannot count for both the Statistics and Data Science requirements)

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 (Note: STATS 191 cannot count for both the Statistics and Data Science requirements)
 STATS 211: Metaresearch: Appraising Research Findings, Bias, and Metaanalysis
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 (23 units)
Note that courses may not be offered every year: refer to ExploreCourses. Select 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 the aekuhn [at] stanford.edu (Student Services Officer).
Typical Paths to the Minor:
Not sure if the Data Science minor is the right minor for you?
 Take a look at the Statistics minor and the 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 the aekuhn [at] stanford.edu (Student Services Officer) via email or in person at Sequoia Hall, Room 124.