Data Science Minor
Overview
The data science minor at Carolina is a multidisciplinary program launched in fall 2021 and offered by the College of Arts & Sciences. The minor has been designed to introduce students from any discipline to data science methods and applications, while simultaneously providing opportunities to explore its complex interactions with modern society. To achieve these goals, the minor is structured to allow students to choose their coursework from many different departments, encouraging them to explore the use of data science within their main field of study.
To satisfy the core requirements, a student must choose one course from each of the three categories:
Data and Computational Thinking
This core requirement will provide you with an introduction to the computing tools and coding methods needed to gather, manipulate, visualize, and analyze data. Taught in Python and/or R.
Data and Statistical Thinking
This core requirement will provide you with an introduction to data-driven statistical analysis, focusing on a hands-on approach to making inferences and predictions to learn from data. Taught in Python and/or R.
Data, Culture, and Society
This core requirement focuses on the social, political, cultural, and/or ethical dimensions of data.
In addition to the program requirements listed below, students must:
- take at least nine hours of their minor "core" requirements at UNC–Chapel Hill
- earn a minimum cumulative GPA of 2.000 in the minor core requirements. Some programs may require higher standards for minor or specific courses.
For more information, please consult the degree requirements section of the catalog.
| Code | Title | Hours |
|---|---|---|
| Core Requirements | ||
| Data and Computational Thinking (one course) | 3 | |
 Introduction to Programming and Data Science H | ||
| Introduction to Scientific Programming | ||
| Introduction to Spatial Data Science | ||
| Data in Politics I: An Introduction | ||
| Data and Statistical Thinking (one course) | 3-4 | |
| Making Sense of Big Data: Textual Analysis with R | ||
| Computational Sociology | ||
| Foundations of Statistics and Data Science F | ||
| Data, Culture, and Society (one course) | 3 | |
| Quantifying the Past: Big Data and Big Questions in Classical Archaeology | ||
| Technology, Ethics, & Culture H | ||
| Maps: Geographic Information from Babylon to Google | ||
| The Rhetoric of Data | ||
| Algorithms and Society | ||
| Geography for Future Leaders | ||
| Seminar on The Ethics and Politics of New Urban Analytics (Previously offered as PLAN 673) | ||
| Analyzing Public Opinion H | ||
| Two additional elective courses from the list below 1 | 6-8 | |
| Total Hours | 15-18 | |
| H | Honors version available. An honors course fulfills the same requirements as the nonhonors version of that course. Enrollment and GPA restrictions may apply. |
| F | FY-Launch class sections may be available. A FY-Launch section fulfills the same requirements as a standard section of that course, but also fulfills the FY-SEMINAR/FY-LAUNCH First-Year Foundations requirement. Students can search for FY-Launch sections in ConnectCarolina using the FY-LAUNCH attribute. |
- 1
Core courses may not be used to satisfy elective requirements.
Elective List
| Code | Title | Hours |
|---|---|---|
| ANTH 419 | Anthropological Application of GIS | 3 |
| ANTH/LING 520 | Linguistic Phonetics | 3 |
| ANTH 680 | Quantitative Methods in Archaeology | 3 |
| ASTR 502 | Modern Research in Astrophysics | 3 |
| ASTR 519 | Observational Astronomy | 4 |
| BCB/COMP 555 | Bioalgorithms | 3 |
| BIOL 525 & 525L | Analysis and Interpretation of Sequence-Based Functional Genomics Experiments and Analysis and Interpretation of Sequence-Based Functional Genomics Experiments Laboratory | 4 |
| BIOL 534/MATH 564 | Mathematical Modeling in the Life Sciences | 3 |
| BIOL/MATH 553 | Mathematical and Computational Models in Biology | 3 |
| BIOL 554 | Introduction to Computational Neuroscience | 3 |
| BIOL/ENEC 562 | Statistics for Environmental Scientists | 4 |
| BIOL/ENEC 563 | Statistical Analysis in Ecology and Evolution | 4 |
| BMME/COMP 576 | Mathematics for Image Computing | 3 |
| BUSI 410 | Business Analytics | 3 |
| BUSI/COMP 488 | Data Science in the Business World | 3 |
| CLAR 411 | Method and Theory in Classical Archaeology | 3 |
| COMP 210 | Data Structures and Analysis | 3 |
| COMP 388 | Advanced Cyberculture Studies | 3 |
| COMP 410 | Data Structures | 3 |
| COMP 421 | Files and Databases | 3 |
| COMP 426 | Modern Web Programming | 3 |
| COMP 433 | Mobile Computing Systems | 3 |
| COMP 486/INLS 512 | Applications of Natural Language Processing | 3 |
| COMP 487/INLS 509 | Information Retrieval | 3 |
| COMP/BUSI 488 | Data Science in the Business World | 3 |
| COMP/BCB 555 | Bioalgorithms | 3 |
| COMP 560 | Artificial Intelligence | 3 |
| COMP 562 | Introduction to Machine Learning H | 3 |
| COMP 572 | Computational Photography | 3 |
| COMP 576 | Mathematics for Image Computing | 3 |
| ECON 470 | Econometrics H | 3 |
| ECON 573 | Machine Learning and Econometrics | 3 |
| ECON 575 | Applied Time Series Analysis and Forecasting | 3 |
| EMES 520 | Data Analysis for Earth and Marine Sciences | 3 |
| EMES 561 | Time Series and Spatial Data Analysis | 3 |
| ENEC 305 | Data Analysis and Visualization of Social and Environmental Interactions | 4 |
| ENEC/GEOG 437 | Social Vulnerability to Climate Change | 3 |
| ENEC/ENVR 468 | Temporal GIS and Space/Time Geostatistics for the Environment and Public Health | 3 |
| ENEC/BIOL 562 | Statistics for Environmental Scientists | 4 |
| ENEC/BIOL 563 | Statistical Analysis in Ecology and Evolution | 4 |
| ENGL 480 | Digital Humanities History and Methods | 3 |
| ENGL 482 | Metadata, Mark-up, and Mapping: Understanding the Rhetoric of Digital Humanities | 3 |
| ENVR/ENEC 468 | Temporal GIS and Space/Time Geostatistics for the Environment and Public Health | 3 |
| EPID 600 | Principles of Epidemiology for Public Health | 3 |
| EXSS 327 | Predictive Analytics in Sport H | 3 |
| GEOG 370 | Introduction to Geographic Information | 3 |
| GEOG 392 | Research Methods in Geography | 3 |
| GEOG 414 | Climate Change | 3 |
| GEOG 416 | Applied Climatology: The Impacts of Climate and Weather on Environmental and Social Systems | 3 |
| GEOG/ENEC 437 | Social Vulnerability to Climate Change | 3 |
| GEOG 446 | Geography of Health Care Delivery | 3 |
| GEOG 456 | Geovisualizing Change | 3 |
| GEOG/PLAN 491 | Introduction to GIS | 3 |
| INLS 509/COMP 487 | Information Retrieval | 3 |
| INLS 512/COMP 486 | Applications of Natural Language Processing | 3 |
| LING 202 | Linguistic Variation and Language Change | 3 |
| LING 203 | Language Acquisition and Development | 3 |
| LING 333 | Human Language and Animal Communication Systems | 3 |
| LING 401 | Language and Computers | 3 |
| LING 422 | Research Methods in Phonetics and Laboratory Phonology | 3 |
| LING/ANTH 520 | Linguistic Phonetics | 3 |
| LING 525 | Introduction to Historical and Comparative Linguistics | 3 |
| MATH 210 | Mathematical Tools for Data Science | 3 |
| MATH 553 | Mathematical and Computational Models in Biology | 3 |
| MATH 560 | Optimization with Applications in Machine Learning | 3 |
| MATH 564 | Mathematical Modeling in the Life Sciences | 3 |
| MEJO 570 | Data Driven Journalism | 3 |
| MEJO 571 | Social Media Analytics | 3.0 |
| PHIL 353 | Minds and Machines: Philosophy of Cognitive Science H | 3 |
| PHYS 331 | Numerical Techniques for the Sciences I | 4 |
| PHYS 332 | Numerical Techniques for the Sciences II | 4 |
| PLAN 372 | Introduction to Urban Data Analytics | 3 |
| PLAN/GEOG 491 | Introduction to GIS | 3 |
| PLCY 460 | Quantitative Analysis for Public Policy H | 4 |
| PLCY 505 | Data Science for Public Policy and Decision Making | 4 |
| PLCY 581 | Research Design for Public Policy H | 3 |
| POLI 381 | Data in Politics II: Frontiers and Applications | 3 |
| PSYC 532 | Quantitative Psychology H | 3 |
| PSYC 533 | The General Linear Model in Psychology H | 3 |
| PSYC 559 | Applied Machine Learning in Psychology | 3 |
| ROML 501 | Introduction to Digital Humanities for Romance Languages, Cultures and Heritage Studies | 3 |
| STOR 320 | Introduction to Data Science | 4 |
| STOR 455 | Methods of Data Analysis | 3 |
| STOR 535 | Probability for Data Science | 3 |
| STOR 538 | Sports Analytics | 3 |
| STOR 556 | Time Series Data Analysis | 3 |
| STOR 565 | Machine Learning | 3 |
| STOR 572 | Simulation for Analytics | 3 |
| STOR 557 | Advanced Methods of Data Analysis | 3 |
| H | Honors version available. An honors course fulfills the same requirements as the nonhonors version of that course. Enrollment and GPA restrictions may apply. |
Department Programs
Major
Minor
Graduate Programs
Department of Statistics and Operations Research
318 Hanes Hall, CB# 3260
(919) 843-6024
Chair
Jan Hannig
