Data Science Minor
Students minoring in data science receive exposure to computational and applied statistical techniques. This minor provides a strong foundation in applied data techniques and methods essential for quantitative research and analysis.
Requirements for the Minor in Data Science
Students take 2 foundational courses, 4 core courses (the 3 Data Science sequence courses and Data Visualization (STAT 302-0)), and 1 approved elective. For details see course lists, below.
| Course | Title |
|---|---|
| Minor Requirements (7 units) | |
| 2 foundational courses: | |
| Introduction to Programming for Data Science | |
| Introduction to Computer Programming | |
(students who do not take STAT 201-0 are responsible for independently learning content not covered in alternative course) 1 | |
| Introduction to Statistics and Data Science | |
or STAT 210-0 | Introduction to Probability and Statistics |
or STAT 232-0 | Applied Statistics |
or approved introductory statistics course from another department | |
| 4 data science core courses: | |
| Data Science 1 with R and Data Science 2 with R and Data Science 3 with R | |
or | |
| Data Science 1 with Python and Data Science 2 with Python and Data Science 3 with Python | |
| NOTE! Students may receive credit for only one Data Science sequence: either Data Science with R (301 sequence), or Data Science with Python (303 sequence) | |
| Data Visualization | |
| 1 approved elective course (see lists below) | |
- 1
Lists of topics not covered in substitute courses can be found on the department website.
Courses used to fulfill the requirements for the minor in data science may not be used to fulfill the requirements for another major/minor except where permitted by Weinberg College double-counting rules (see FAQ). When necessary (for example when a student plans to complete a major in statistics and a minor in data science), students can consult the Director of Undergraduate Studies for Data Science in the Department of Statistics and Data Science about selection of replacement course(s) to satisfy the credit requirements of the data science minor.
Approved Elective Courses for the Data Science Minor
Students choose 1 course from any of the fields below. For updates please refer to department website list of Data Science Minor Approved Electives. Some courses may have prerequisites; check course descriptions for details.
Anthropology
| Course | Title |
|---|---|
| ANTHRO 322-0 | Introduction to Archaeology Research Design & Methods |
| ANTHRO 324-0 | Archaeological Survey Methods |
| ANTHRO 362-0 | Advanced Methods in Quantitative Analysis |
| ANTHRO 389-0 | Ethnographic Methods and Analysis |
Biological Sciences
| Course | Title |
|---|---|
| BIOL_SCI 323-0 | Bioinformatics: Sequence and Structure Analysis |
| BIOL_SCI 338-0 | Modeling Biological Dynamics |
| BIOL_SCI 341-0 | Population Genetics |
| BIOL_SCI 354-0 | Systems Biology |
| BIOL_SCI 378-0 | Functional Genomics |
Biomedical Engineering
| Course | Title |
|---|---|
| BMD_ENG 311-0 | Computational Genomics |
| BMD_ENG 312-0 | Biomedical Applications in Machine Learning |
Chemical Engineering
| Course | Title |
|---|---|
| CHEM_ENG 379-0 | Computational Biology: Analysis and Design of Living Systems |
| CHEM_ENG 367-0 | Quantitative Methods in Life Cycle Analysis |
Cognitive Science
| Course | Title |
|---|---|
| COG_SCI 345-0 | Presenting Ideas & Data |
Communication Studies
| Course | Title |
|---|---|
| COMM_ST 352-0 | Social Network Analysis |
| COMM_ST 355-0 | Audience Analysis |
| COMM_ST 358-0 | Algorithms and Society |
| COMM_ST 371-0 | Cultural Analytics |
Computer Engineering
| Course | Title |
|---|---|
| COMP_ENG 329-0 | The Art of Multicore Concurrent Programming |
| COMP_ENG 358-0 | Introduction to Parallel Computing |
| COMP_ENG 365-0 | Internet-of-things Sensors, Systems, And Applications |
| COMP_ENG 368-0 | Programming Massively Parallel Processors with CUDA |
Computer Science
| Course | Title |
|---|---|
| COMP_SCI 214-0 | Data Structures & Algorithms |
| COMP_SCI 217-0 | Data Management & Information Processing |
| COMP_SCI 323-0 | Code Analysis and Transformation |
| COMP_SCI 325-0 | Artificial Intelligence Programming |
| COMP_SCI 331-0 | Introduction to Computational Photography |
| COMP_SCI 333-0 | Interactive Information Visualization |
| COMP_SCI 335-0 | Introduction to the Theory of Computation |
| COMP_SCI 336-0 | Design & Analysis of Algorithms |
| COMP_SCI 337-0 | Natural Language Processing: Classical Approaches |
| COMP_SCI 339-0 | Introduction to Database Systems |
| COMP_SCI 341-0 | Social Networks Analysis |
| COMP_SCI 344-0 | Design of Computer Problem Solvers |
| COMP_SCI 345-0 | Distributed Systems |
| COMP_SCI 347-0 | Conversational AI |
| COMP_SCI 348-0 | Introduction to Artificial Intelligence |
| COMP_SCI 349-0 | Machine Learning |
| COMP_SCI 351-1 | Introduction to Computer Graphics |
| COMP_SCI 351-2 | Intermediate Computer Graphics |
| COMP_SCI 352-0 | Machine Perception of Music & Audio |
| COMP_SCI 367-0 | Wireless and Mobile Health: Passive Sensing Data Analytics |
Earth and Planetary Science
| Course | Title |
|---|---|
| EARTH 323-0 | Seismology and Earth Structure |
| EARTH 327-0 | Geophysical Time Series Analysis |
| EARTH 340-0 | Physics of Weather & Climate |
| EARTH 343-0 | Earth System Modeling |
| EARTH 353-0 | Mathematical Inverse Methods in Earth and Environmental Sciences |
| EARTH 360-0 | Instrumentation and Field Methods |
| EARTH 361-0 | Scientific Programming in Python |
Economics
| Course | Title |
|---|---|
| ECON 307-0 | Economics of Medical Care |
| ECON 308-0 | Money and Banking |
| ECON 309-0 | Public Finance |
| ECON 310-1 | Microeconomics |
| ECON 310-2 | Microeconomics |
| ECON 311-0 | Macroeconomics |
| ECON 316-0 | Advanced Topics in Macroeconomics |
| ECON 325-0 | Economic Growth & Development |
| ECON 326-0 | The Economics of Developing Countries |
| ECON 327-0 | Economic Development in Africa |
| ECON 329-0 | Experimental Economics |
| ECON 330-0 | Behavioral Economics |
| ECON 331-0 | Economics of Risk and Uncertainty |
| ECON 336-0 | Analytic Methods for Public Policy Analysis |
| ECON 337-0 | Economics of State and Local Governments |
| ECON 339-0 | Labor Economics |
| ECON 340-0 | Economics of the Family |
| ECON 341-0 | Economics of Education |
| ECON 342-0 | Economics of Gender |
| ECON 349-0 | Industrial Economics |
| ECON 350-0 | Monopoly Competition & Public Policy |
| ECON 351-0 | Law and Economics |
| ECON 354-0 | Issues in Urban and Regional Economics |
| ECON 355-0 | Transportation Economics and Public Policy |
| ECON 358-0 | Economics of Art and Culture |
| ECON 359-0 | Economics of Nonprofit Organizations |
| ECON 360-1 | Foundations of Corporate Finance Theory |
| ECON 360-2 | Investments |
| ECON 361-0 | International Trade |
| ECON 362-0 | International Finance |
| ECON 371-0 | Economics of Energy |
| ECON 372-0 | Environmental Economics |
| or ECON 373-0 | Natural Resource Economics |
| ECON 380-1 | Game Theory |
| ECON 381-1 | Econometrics |
| ECON 381-2 | Econometrics |
| ECON 383-0 | Applied Econometrics |
Electrical Engineering
| Course | Title |
|---|---|
| ELEC_ENG 328-0 | Information Theory & Learning |
| ELEC_ENG 331-0 | Introduction to Computational Photography |
| ELEC_ENG 332-0 | Introduction to Computer Vision |
| ELEC_ENG 335-0 | Deep Learning Foundations from Scratch |
| ELEC_ENG 373-0 | Deep Reinforcement Learning |
| ELEC_ENG 375-0 | Machine Learning: Foundations, Applications, and Algorithms |
Engineering Sciences and Applied Mathematics
| Course | Title |
|---|---|
| ES_APPM 344-0 | High Performance Scientific Computing |
| ES_APPM 346-0 | Modeling and Computation in Science & Engineering |
| ES_APPM 370-1 | Introduction to Computational Neuroscience |
| ES_APPM 375-1 | Quantitative Biology I: Experiments, Data, Models, and Analysis |
| ES_APPM 375-2 | Quantitative Biology II: Experiments, Data, Models, and Analysis |
Global Health Studies
| Course | Title |
|---|---|
| GBL_HLTH 320-0 | Qualitative Research Methods in Global Health |
| GBL_HLTH 303-0 | (Re)mixing Qualitative Methods |
Industrial Engineering and Management Sciences
| Course | Title |
|---|---|
| IEMS 308-0 | Data Science and Analytics |
| IEMS 313-0 | Foundations of Optimization |
| IEMS 315-0 | Stochastic Models |
| IEMS 317-0 | Discrete Event Systems Simulation |
| IEMS 340-0 | Qualitative Methods in Engineering Systems |
| IEMS 341-0 | Social Networks Analysis |
| IEMS 351-0 | Optimization Methods in Data Science |
Integrated Marketing and Journalism
| Course | Title |
|---|---|
| IMC 302-0 | Research and Data Analytics |
| IMC 307-0 | Digital, Social and Mobile Marketing |
| JOUR 342-1 | Knight Lab: Studio |
| JOUR 342-2 | Knight Lab: Artificial Intelligence in Media |
| JOUR 377-0 | Introduction to Data Journalism |
Linguistics
| Course | Title |
|---|---|
| LING 334-0 | Introduction to Computational Linguistics |
Mathematics
| Course | Title |
|---|---|
| MATH 306-0 | Combinatorics & Discrete Mathematics |
| MATH 308-0 | Graph Theory |
| MATH 310-1 | Probability and Stochastic Processes |
| MATH 310-2 | Probability and Stochastic Processes |
| MATH 310-3 | Probability and Stochastic Processes |
| MATH 311-1 | MENU: Probability and Stochastic Processes |
| MATH 311-2 | MENU: Probability and Stochastic Processes |
| MATH 311-3 | MENU: Probability and Stochastic Processes |
| MATH 314-0 | Probability and Statistics for Econometrics |
| MATH 366-0 | Mathematical Models in Finance |
| MATH 368-0 | Introduction to Optimization |
| MATH 370-0 | Mathematical Logic |
| MATH 386-1 | Econometrics for MMSS |
| MATH 386-2 | Econometrics for MMSS |
Music Theory
| Course | Title |
|---|---|
| MUS_THRY 348-0 | Corpus Studies |
Political Science
| Course | Title |
|---|---|
| POLI_SCI 310-0 | Methods of Political Inference |
| POLI_SCI 312-0 | Statistical Research Methods |
Psychology
| Course | Title |
|---|---|
| PSYCH 345-0 | Presenting Ideas & Data |
| PSYCH 369-0 | Psychological Tests & Measures |
| PSYCH 380-0 | Advanced Statistics & Experimental Design |
| PSYCH 387-0 | Consumer Psychology and Marketing Research |
School of Education and Social Policy
| Course | Title |
|---|---|
| SESP 272-0 | Field Research Methods |
| SOC_POL 330-0 | Economics of Social Policy |
| SOC_POL 331-0 | Economics of Inequality and Discrimination |
| SOC_POL 333-0 | Economics of Health, Human Capital, and Happiness |
Sociology
| Course | Title |
|---|---|
| SOCIOL 303-0 | Analysis and Interpretation of Social Data |
| SOCIOL 329-0 | Field Research and Methods of Data Collection |
| SOCIOL 335-0 | Sociology of Rational Decision Making |
Statistics and Data Science
| Course | Title |
|---|---|
| STAT 320-1 | Statistical Theory & Methods 1 |
| STAT 320-2 | Statistical Theory & Methods 2 |
| STAT 320-3 | Statistical Theory & Methods 3 |
| STAT 328-0 | Causal Inference |
| STAT 344-0 | Statistical Computing |
| STAT 348-0 | Applied Multivariate Analysis |
| STAT 350-0 | Regression Analysis |
| STAT 351-0 | Design and Analysis of Experiments |
| STAT 352-0 | Nonparametric Statistical Methods |
| STAT 353-0 | Advanced Regression |
| STAT 354-0 | Time Series Modeling |
| STAT 356-0 | Hierarchical Linear Models |
| STAT 357-0 | Introduction to Bayesian Statistics |
| STAT 365-0 | Introduction to the Analysis of Financial Data |
The Data Science Minor in Relation to Majors
The minor in Data Science can be completed along with any major. The general Weinberg College policies about major/minor pairings apply. Below is clarifying text about how this works with certain majors, and where particular exceptions to general rules are approved.
The Data Science Minor for Students in the Integrated Science Program
Students complete all requirements for the ISP major, and requirements for Data Science minor are modified as follows:
- Introductory Statistics course requirement: STAT 202-0, STAT 210-0, STAT 232-0 or equivalent is waived
All other Data Science minor requirements must be met.
The Data Science Minor for Students in the Mathematical Methods in the Social Sciences Program
Students will complete all requirements for the MMSS adjunct major, and requirements for Data Science minor are modified as follows:
- Introductory Statistics course requirement: STAT 202-0, STAT 210-0, STAT 232-0 or equivalent is waived
All other Data Science minor requirements must be met.
The Data Science Minor with the Major in Statistics
Students complete all requirements for Statistics major, and requirements for Data Science minor are modified as follows:
- STAT 201-0 (or COMP_SCI 110-0) is replaced with another approved course. In most cases, an introductory calculus course that is necessary for the Statistics major will be used as the replacement.
- STAT 202-0, STAT 210-0, STAT 232-0 or equivalent is replaced with another approved course. In most cases, an introductory calculus course that is necessary for the Statistics major will be used as the replacement.
Note: STAT 301-1,2,3 (Data Science with R), STAT 303-1,2,3 (Data Science with Python), and any 300-level electives being used for the Data Science minor cannot be used to fulfill credit requirements for the Statistics major.
All other Data Science minor requirements must be met.