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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:

  • CME 100: Vector Calculus for Engineers
  • Math 51: Linear Algebra, Multivariable Calculus, and Modern Applications
Programming (5 units)
  • CS 106A: Programming Methodology (CS 106AP and CS 106AJ also satisfy this requirement)
Programming in R (1-4 units)

Select one course:

  • STATS 32: Introduction to R for Undergraduates 
  • STATS 48N: Riding the Data Wave 
  • STATS 195: Introduction to R 
  • THINK 3: Breaking Codes, Finding Patterns 
Data Science (3-5 units)

Select one course:

  • DATASCI 112: Principles of Data Science (STATS 112)
  • DATASCI 154: Solving Social Problems with Data
  • CS 102: Working with Data - Tools and Techniques
  • MS&E 226: Fundamentals of Data Science: Prediction, Inference, Causality
  • 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)
Statistics (3-5 units)

Select one course:

  • ECON 102A: Introduction to Statistical Methods (Postcalculus) for Social Scientists
  • 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 (2-5 units)

Note that courses may not be offered every year: refer to ExploreCoursesSelect at least one course.

Suggested courses include:

  • BIODS210 - Configuration of the US Healthcare System and the Application of Big Data/Analytics
  • BIOMEDIN202 - BIOMEDICAL DATA SCIENCE
  • COMM177I - Investigative Watchdog Reporting
  • CS224W - Machine Learning with Graphs
  • CS279 - Computational Biology: Structure and Organization of Biomolecules and Cells
  • ECON102B - Applied Econometrics
  • ECON102C - Advanced Topics in Econometrics
  • ECON137 - Decision Modeling and Information
  • ECON151 - Tackling Big Questions Using Social Data Science
  • ECON291 - Social and Economic Networks
  • ENGLISH184E - Literary Text Mining
  • ESS171 - Climate Models and Data
  • IMMUNOL206 - Introduction to Applied Computational Tools in Immunology
  • MS&E125 - Introduction to Applied Statistics
  • MS&E135 - Networks
  • MS&E245A - Investment Science
  • POLISCI150B - Machine Learning for Social Scientists
  • SOC10 - Introduction to Computational Social Science
  • SOC126 - Introduction to Social Networks
  • SOC180A - Foundations of Social Research
  • SOC180B - Introduction to Data Analysis
  • SYMSYS1 - Minds and Machines

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.
    • Data Science will also accept courses that are only offered as S/NC (i.e. letter grade option not available). 
  • 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 Math 51, CME 100, 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