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# Data Science Minor Requirements 2023-2024

These degree requirements apply to students who declared between September 1, 2023 and August 31, 2024.

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Course Requirements

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Linear Algebra (5 units)

Select one course:

- CME 100: Vector Calculus for Engineers
- Math 51: Linear Algebra, Multivariable Calculus, and Modern Applications

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Programming (5 units)

- CS 106A: Programming Methodology (CS 106AP and CS 106AJ also satisfy this requirement)

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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

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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)

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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

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Data Mining and Analysis (3 units)

Select one course:

- STATS 202: Data Mining and Analysis
- STATS 216: Introduction to Statistical Learning

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Data Science Methodology from the cognate field of interest (2-5 units)

**Note that courses may not be offered every year: refer to ****ExploreCourses****. ***Select 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*

- 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

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Frosh: Programming in R, Math 21, CS 106A

Frosh: Programming in R, Math 21, CS 106A

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Sophomore: Linear Algebra, Data Science course

Sophomore: Linear Algebra, Data Science course

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Junior: Statistics course, Data Science Methodology course

Junior: Statistics course, Data Science Methodology course

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Senior: Data Mining and Analysis

Senior: Data Mining and Analysis

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Frosh: (AP Calculus), Programming in R, CS 106A

Frosh: (AP Calculus), Programming in R, CS 106A

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Sophomore: Linear Algebra, Data Science course

Sophomore: Linear Algebra, Data Science course

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Junior: Statistics course, Data Science Methodology course

Junior: Statistics course, Data Science Methodology course

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Senior: Data Mining and Analysis

Senior: Data Mining and Analysis