Data Science Minor

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 they relate 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). We believe that teaching students how to correctly apply data analysis tools and the techniques of data visualization to convey their conclusions has the power to strengthen social research. No previous programming or statistical background is assumed. See the data science minor form (below) for full requirements. 

Data Science Minor Form      

    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:

    • DATASCI 112: Principles of Data Science (STATS 112)

    • 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: 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)

    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

    Additional Information

    • All courses for the minor must be taken for a letter grade, with the exception of the Data Mining requirement.
      • Data Science will accept a letter grade or credit for all major/minor courses from the 2020-21 academic year.
    • An overall 2.75 grade point average (GPA) is required for courses fulfilling the minor.
    • A note about double counting: 

      • Students may not overlap ("double-count") courses when completing multiple major and/or minor requirements, unless overlapping courses constitute introductory skill requirements (for example, introductory math or a foreign language).

      • For majors & minors with overlapping requirements, the courses that may be double counted are those from the MATH 50 series, CS 106A/B, & STATS 60. Beyond these, students would need to find another suitable course to satisfy the requirements for the minor.
    • Any changes to the initial course of study should be approved in advance by the department.
    • If you have any questions or would like to talk more about the minor, please reach out to the Student Services Specialist.

    Typical Paths to the Minor

    Frosh: 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
    Frosh: (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

    How to Declare the Minor

    1. Use the form to plan your coursework.

    Fill out the data science minor form with your planned course selections. To access the form, you must log-in to your Stanford account; then download the form.

    2. Declare your minor in Axess.

    Keep in mind that you must declare a major in Axess before you will be able to declare a minor.

    3. Email the SSO.

    Email the datasciencemajor-inquiries [at] lists.stanford.edu (Student Services Specialist) with your form attached and let them know you'd like to declare.

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