Skip to content Skip to navigation

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 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, with the exception of the Data Mining requirement. Seven courses are required, equal to at least 22 units. An overall 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 Susie Ementon, Sequoia Hall, Room 222, before they can be approved.

Minor course requirements

Linear Algebra (5 units) select one course

  • Math 51: Linear Algebra and Differential Calculus of Several Variables
  • CME 100: Vector Calculus for Engineers


Programming (3 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)
  • THINK 3: Breaking Codes, Finding Patterns (4 units)


Data Science

  • STATS 101: Data Science 101


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 141: Biostatistics
  • STATS 191: Introduction to Applied Statistics
  • STATS 211: Meta-research: Appraising Research Findings, Bias, and Meta-analysis
  • STATS 216: Introduction to Statistical Learning


Data Mining and Analysis (3 units)

  • STATS 202: Data Mining and Analysis


Data Science Methodology from the cognate field of interest (2-3 units) 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 Susie Ementon,

Typical paths to the minor:

Freshman: STATS 48N, Math 21, CS 106A
Sophomore: STATS 101, Math 51
Junior: Phil 167, English 184E
Senior: STATS 202

Freshman: (AP Calculus), Think3, CS 106A
Sophomore: STATS 101, CME 100
Junior: Econ 102A, PoliSci 350A
Senior: STATS 202

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. 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 Susie Ementon,