Data Science
Overview
Data is increasingly central to understanding and shaping the world around us—from scientific discovery and historical interpretation to public policy, business decisions, and social trends.
Engaging with data requires not only technical skill, but also critical thinking, ethical reflection, and a deep appreciation of context and complexity. The data science minor prepares students to be informed and capable participants in a data-rich world, regardless of their primary field of study.
Data science is an interdisciplinary field of study that uses quantitative scientific methods and computational processes together with field-specific expertise to extract knowledge and insights from data. Through interdisciplinary coursework, students will develop the following core competencies:
- Data preparation: Students will develop the ability to clean and wrangle data, preparing it for analysis using appropriate tools and techniques.
- Data analysis and interpretation: Students will conduct statistical analyses, create clear and effective data visualizations, and interpret results accurately.
- Data science theory: Students will develop an understanding of the theoretical foundations of data science, including algorithms, statistics, modeling, and method selection.
- Applying and communicating data science: Students will apply data science methods to real-world, discipline-specific problems while communicating and critically evaluating the implications, limitations, and ethics of data use.
Requirements
The curriculum is designed to provide students with a broad understanding of data science, with training in multiple core competencies and in several disciplines. Students must complete 1 core course in computer science, 1 core course in statistics, and 12 additional units in data science electives for a total of 20 units of coursework. The 20 units of coursework must come from at least 3 different departments. The electives must span at least 3 out of the 4 core competencies outlined above.
Minor
Required Course:
| COMP 131 | Fundamentals of Computer Science | 4 units |
Students who pass out of
COMP 131, demonstrating proficiency without receiving credit (e.g., through the self-placement test) are required to take an additional 4 units of credit from the data science electives.
One of the following statistics courses:
| BIO 268 | Biostatistics | 4 units |
| COMP 146 | Statistics | 4 units |
| MATH 150 | Statistical Data Analysis | 4 units |
| POLS 300 | Research Methods in Politics and Public Policy | 4 units |
| PSYC 201 | Statistics in Psychological Science | 4 units |
| SOC 305 | Quantitative Research Methods | 4 units |
Students may take a second statistics course from the list above and count it as an elective. A maximum of two of the listed statistics courses can be applied to the minor.
Electives:
Three elective courses from the following, satisfying at least 3 out of the 4 core competencies across electives. There is no limit to the number of core competencies satisfied by a given elective.
Students may petition the Data Science minor chair to count up to 4 credits from directed research, independent study, or special topics courses toward the minor.
Data Preparation Core Competency
| BIO 260 | Biodiversity and Organization of Marine Ecosystems | 4 units |
| BIO 268 | Biostatistics | 4 units |
| BIO 326 | Molecular Phylogenetics | 4 units |
| BIO 373 | Computational Biology | 4 units |
| BLST 263/SOC 263 | Du Boisian Social Theory and Analysis | 4 units |
| CHEM 201 | Analytical and Instrumental Chemistry | 4 units |
| CHEM 300 | Physical Chemistry: Thermodynamics - Kinetics | 4 units |
| COGS 201 | Empirical Methods in Cognitive Science | 4 units |
| COGS 255 | Data Analysis and Visualization | 4 units |
| COMP 331 | Natural Language Processing | 4 units |
| COMP 373 | Databases | 4 units |
| ECON 272 | Applied Econometrics | 4 units |
| ECON 306 | Advanced Econometrics | 4 units |
| ECON 307 | Economic Data Analysis | 4 units |
| LING 351 | Phonetics | 4 units |
| MATH 332 | Mathematical Statistics | 4 units |
| PHYS 107 | Waves | 4 units |
| PHYS 110 | Introductory Mechanics | 4 units |
| PHYS 115 | General Physics I | 4 units |
| PHYS 125 | General Physics II | 4 units |
| PHYS 230 | Introductory Electricity and Magnetism | 4 units |
| PHYS 240 | Foundations of Modern Physics | 4 units |
| PHYS 315 | Advanced Laboratory I | 2 units |
| PHYS 316 | Advanced Laboratory II | 2 units |
| POLS 300 | Research Methods in Politics and Public Policy | 4 units |
| PSYC 200 | Methods in Psychological Science | 4 units |
| PSYC 201 | Statistics in Psychological Science | 4 units |
| SOC 305 | Quantitative Research Methods | 4 units |
| UEP 150 | Geographic Information Science I | 4 units |
| UEP 160 | Spatial Analysis with Geographic Information Science | 4 units |
| UEP 305 | Urban Data Analysis | 4 units |
Data Analysis and Interpretation Core Competency
| BIO 260 | Biodiversity and Organization of Marine Ecosystems | 4 units |
| BIO 268 | Biostatistics | 4 units |
| BIO 326 | Molecular Phylogenetics | 4 units |
| BIO 373 | Computational Biology | 4 units |
| BLST 263/SOC 263 | Du Boisian Social Theory and Analysis | 4 units |
| CHEM 201 | Analytical and Instrumental Chemistry | 4 units |
| CHEM 300 | Physical Chemistry: Thermodynamics - Kinetics | 4 units |
| COGS 201 | Empirical Methods in Cognitive Science | 4 units |
| COGS 243 | Computational Neuroscience: Minds, Math and Machines | 4 units |
| COGS 255 | Data Analysis and Visualization | 4 units |
| COGS 346 | Cognition and Deep Learning | 4 units |
| COMP 146 | Statistics | 4 units |
| COMP 331 | Natural Language Processing | 4 units |
| COMP 347 | Machine Learning | 4 units |
| ECON 272 | Applied Econometrics | 4 units |
| ECON 306 | Advanced Econometrics | 4 units |
| ECON 307 | Economic Data Analysis | 4 units |
| LING 351 | Phonetics | 4 units |
| MATH 150 | Statistical Data Analysis | 4 units |
| MATH 330 | Probability | 4 units |
| MATH 332 | Mathematical Statistics | 4 units |
| MATH 370 | Numerical Analysis | 4 units |
| PHYS 107 | Waves | 4 units |
| PHYS 110 | Introductory Mechanics | 4 units |
| PHYS 115 | General Physics I | 4 units |
| PHYS 125 | General Physics II | 4 units |
| PHYS 230 | Introductory Electricity and Magnetism | 4 units |
| PHYS 240 | Foundations of Modern Physics | 4 units |
| PHYS 315 | Advanced Laboratory I | 2 units |
| PHYS 316 | Advanced Laboratory II | 2 units |
| POLS 300 | Research Methods in Politics and Public Policy | 4 units |
| PSYC 200 | Methods in Psychological Science | 4 units |
| PSYC 201 | Statistics in Psychological Science | 4 units |
| SOC 305 | Quantitative Research Methods | 4 units |
| UEP 150 | Geographic Information Science I | 4 units |
| UEP 160 | Spatial Analysis with Geographic Information Science | 4 units |
| UEP 305 | Urban Data Analysis | 4 units |
Data Science Theory Core Competency
Applying and Communicating Data Science Core Competency
| BIO 260 | Biodiversity and Organization of Marine Ecosystems | 4 units |
| BIO 268 | Biostatistics | 4 units |
| BIO 326 | Molecular Phylogenetics | 4 units |
| BIO 373 | Computational Biology | 4 units |
| CHEM 201 | Analytical and Instrumental Chemistry | 4 units |
| COGS 201 | Empirical Methods in Cognitive Science | 4 units |
| COGS 255 | Data Analysis and Visualization | 4 units |
| COGS 346 | Cognition and Deep Learning | 4 units |
| COMP 331 | Natural Language Processing | 4 units |
| COMP 347 | Machine Learning | 4 units |
| ECON 272 | Applied Econometrics | 4 units |
| ECON 306 | Advanced Econometrics | 4 units |
| ECON 307 | Economic Data Analysis | 4 units |
| MATH 150 | Statistical Data Analysis | 4 units |
| MATH 214 | Linear Algebra | 4 units |
| MATH 330 | Probability | 4 units |
| MATH 332 | Mathematical Statistics | 4 units |
| MATH 370 | Numerical Analysis | 4 units |
| PHIL 331 | Philosophy of Technology | 4 units |
| PHYS 107 | Waves | 4 units |
| PHYS 110 | Introductory Mechanics | 4 units |
| PHYS 115 | General Physics I | 4 units |
| PHYS 125 | General Physics II | 4 units |
| PHYS 230 | Introductory Electricity and Magnetism | 4 units |
| PHYS 240 | Foundations of Modern Physics | 4 units |
| PHYS 315 | Advanced Laboratory I | 2 units |
| PHYS 316 | Advanced Laboratory II | 2 units |
| POLS 300 | Research Methods in Politics and Public Policy | 4 units |
| PSYC 200 | Methods in Psychological Science | 4 units |
| PSYC 201 | Statistics in Psychological Science | 4 units |
| SOC 305 | Quantitative Research Methods | 4 units |
| UEP 150 | Geographic Information Science I | 4 units |
| UEP 160 | Spatial Analysis with Geographic Information Science | 4 units |
| UEP 305 | Urban Data Analysis | 4 units |
Preparation for Careers in Data Science
Students interested in pursuing technical careers or graduate study in data science are encouraged to prioritize electives that have a strong quantitative focus: COMP 229, COMP 331, COMP 347, COMP 373, ECON 272, ECON 306, MATH 214, MATH 330, MATH 332.
Transfer Credit Policies
See the Data Science minor chair.
Faculty
Diana Ngo,chair
Associate Professor, Economics
B.S., Harvard University; Ph.D., University of California, Berkeley
Advisory Committee:
Treena Basu
Associate Professor, Mathematics
B.S. Jogamaya Devi College: University of Calcutta; M.S. University of Texas-Pan American; M.S. Bengal Engineering and Science University; Ph.D. University of South Carolina
Justin Li
Associate Professor, Computer Science & Cognitive Science
B.S., Northwestern University; Ph.D., University of Michigan
Daniel Snowden-Ifft
Ezra Frederick Scattergood Professor, Physics
B.A., Swarthmore College M.A., Ph.D., University of California, Berkeley
Affiliated Faculty:
Jamie Amemiya
Assistant Professor, Psychology
B.S., University of California, Irvine; M.S., Ph.D., University of Pittsburgh
Jeffrey Cannon
Associate Professor, Chemistry
A.B., Occidental College; Ph.D., University of California, Irvine
Isaac Hale
Assistant Professor, Politics
A.B., Occidental College; Ph.D., Northwestern University
Caroline Heldman
Professor, Gender, Women and Sexuality Studies
B.A., Washington State University; M.A., Ph.D., Rutgers University
Carmel Levitan
Professor, Cognitive Science
B.A., Stanford University; Ph.D., University of California, Berkeley
Alexandria Pivovaroff
Assistant Professor, Biology
B.A., Whittier College; Ph.D., University of California, Riverside