Data Science Major, B.S.
The bachelor of science (B.S.) in data science provides students with a strong foundation in preparation for entry to the workforce or pursuit of an advanced degree. The B.S. in data science is comprised of six competencies:
- responsible data science
- communication
- computational thinking
- mathematical and statistical foundations
- optimization
- machine learning and artificial intelligence (AI)
The curriculum provides in-depth exposure to quantitative topics and opportunities for direct application through four-course concentrations, upper-level electives, mentored research, and internship opportunities.
Admission to the Major
Those wishing to declare the bachelor of science (B.S.) in data science must be admitted to the School of Data Science and Society. Students are eligible to apply in the spring semester after completing the majority of or while currently enrolled in the prerequisite courses. Please see the school's website for the most up-to-date information about the admission to the major process.
Student Learning Outcomes
The B.S. in data science is comprised of the following six competencies and corresponding learning outcomes:
Responsible Data Science
- The Responsible Data Science competency focuses on the ethical practice of data collection, analysis, and communication, aiming to generate fair and explainable data-driven insights while minimizing harmful unintended consequences. Students will develop strategies involved in mitigating bias, protecting privacy, and weighing the impact of different data science applications; as well as build a formal framework for understanding the ethical implications of these strategies.
Communications
- The Communications competency equips students to translate complex data analyses into actionable insights that drive decision-making and innovation. Students will develop the ability to convey data findings clearly and persuasively through written, oral, and visual means to both technical and non-technical audiences.
Computational Foundations
- The Computational Foundations competency develops students' abilities to thoughtfully choose levels of abstraction and strategically select from among available tools. Students will evaluate results from pilot studies, extrapolate to scale, and clearly report the reasoning for decisions.
Mathematical and Statistical Foundations
- The Mathematical and Statistical Foundations competency provides a rigorous foundation in the mathematical and statistical principles that underlie the analysis of data. Students will use essential tools to support data-informed decision-making, including calculus, linear algebra, probability theory, inference, modeling, and optimization.
Optimization
- The Optimization competency involves analyzing complex systems with multiple variables to identify optimal solutions. Students will use various optimization techniques to model relationships between variables and apply analytical methods to enhance processes and outcomes.
Machine Learning and Artificial Intelligence (AI)
- The Machine Learning (ML)and Artificial Intelligence (AI) competency involves the creation of algorithms and systems that enable machines to learn from data and aid in decision-making or predictions. Students will develop, implement, and assess AI/ML models across various applications, using both foundational theory and practical skills in these rapidly evolving fields.
Requirements
In addition to the program requirements, students must
- earn a minimum final cumulative GPA of 2.000
- complete a minimum of 45 academic credit hours earned from UNC–Chapel Hill courses
- take at least half of their major core requirements (courses and credit hours) at UNC–Chapel Hill
- earn a minimum cumulative GPA of 2.000 in the major core requirements. Some programs may require higher standards for major or specific courses.
For more information, please consult the degree requirements section of the catalog.
| Code | Title | Hours |
|---|---|---|
| Core Requirements | ||
| DATA 110 |  Introduction to Data Science †, H | 3 |
| DATA 120 | Ethics of AI and Societal Decision Making H | 3 |
| Communications (select one): | 3 | |
| Public Speaking | ||
| Argumentation and Debate | ||
| Communication for Data Scientists | ||
| Picture This: Principles of Visual Rhetoric | ||
| Scientific and Technical Communication | ||
| Composing for Clients: Technical Communication Practicum | ||
| Maps: Geographic Information from Babylon to Google | ||
| Communicating Important Ideas | ||
| Information Visualization | ||
| Future Vision: Exploring the Visual World | ||
| Mathematical and Statistical Foundations (select one): | 3 | |
| Basic Elements of Probability and Statistical Inference I | ||
| Advanced Calculus I H | ||
| Introduction to Probability H | ||
| Probability for Data Science | ||
| Probability I | ||
| Optimization (select one): | 3 | |
| Advanced Calculus II H | ||
| Elementary Differential Equations | ||
| Optimization with Applications in Machine Learning | ||
| Introduction to Optimization H | ||
| Foundations of Optimization | ||
| Machine Learning and AI (select one): | 3 | |
| Introduction to Machine Learning | ||
| Introduction to Machine Learning H | ||
| Foundations in Artificial Intelligence | ||
| Machine Learning | ||
| Introduction to Deep Learning | ||
| Computational Thinking (select one): | 3-4 | |
| Introduction to Statistical Computing and Data Management | ||
| Data Science Basics | ||
| Foundations of Programming | ||
| Introduction to Numerical Analysis | ||
| Scientific Computation I | ||
| Methods and Models of Data Science | ||
| Statistical Computing for Data Science | ||
| Simulation for Analytics | ||
| Choose six upper-division electives (see list below) OR a four-course concentration and two upper-division electives. 1 | 18 | |
| Additional Requirements | ||
| MATH 231 | Calculus of Functions of One Variable I †, H, F | 4 |
| MATH 232 | Calculus of Functions of One Variable II †, H, F | 4 |
| MATH 233 | Calculus of Functions of Several Variables †, H, F | 4 |
| or MATH 235 | Mathematics for Data Science | |
| MATH 347 | Linear Algebra for Applications †, F | 3 |
| MATH 381 | Discrete Mathematics †, H | 3-4 |
| or STOR 315 | Discrete Mathematics for Data Science | |
| or COMP 283 | Discrete Structures | |
| STOR 120 | Foundations of Statistics and Data Science †, H, F | 3-4 |
| or COMP 110 | Introduction to Programming | |
| or COMP 116 | Introduction to Scientific Programming | |
| Total Hours | 60-63 | |
| 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. |
- †
Must be completed to apply to the School of Data Science and Society.
- 1
One course cannot fulfill two core requirements. For example, one course cannot satisfy a competency requirement and an upper-level elective requirement. Students are limited to one DATA 890 as an upper-level elective.
Upper-Division Electives
| Code | Title | Hours |
|---|---|---|
| BIOS 645 | Principles of Experimental Analysis | 3 |
| BIOS 664 | Sample Survey Methodology | 4 |
| COMP 421 | Files and Databases | 3 |
| COMP 488 | Data Science in the Business World | 3 |
| COMP 550 | Algorithms and Analysis | 3 |
| COMP 560 | Artificial Intelligence | 3 |
| COMP 562 | Introduction to Machine Learning H | 3 |
| COMP 586 | Natural Language Processing | 3 |
| COMP 664 | Deep Learning | 3 |
| COMP 683 | Computational Biology | 3 |
| DATA 481 | Data Science Practicum | 3 |
| DATA 493 | Internship in Data Science | 3 |
| DATA 495 | Mentored Research in Data Science | 3 |
| DATA 496 | Directed Exploration in Data Science | 3 |
| DATA 543 | Risk, Data Science and AI | 3 |
| DATA 520 | Research-Methods for Socially Responsible AI: An Ethical Expedition | 3 |
| DATA 521 | Foundations in Artificial Intelligence | 3 |
| DATA 522 | Practical Deep Learning Systems | 3 |
| DATA 523 | Modeling and Data Mining For Artificial Intelligence | 3 |
| DATA 593 | Internship in Data Science | 12 |
| DATA 693H | Honors Thesis in Data Science | 3 |
| DATA 694H | Honors Thesis in Data Science | 3 |
| DATA 890 | Special Topics in Data Science (one time only, with permission) | 3 |
| ENGL 411 | Composing for Clients: Technical Communication Practicum | 3 |
| GEOG 415 | Communicating Important Ideas | 3 |
| INLS 541 | Information Visualization | 3 |
| MATH 521 | Advanced Calculus I H | 3 |
| MATH 522 | Advanced Calculus II H | 3 |
| MATH 524 | Elementary Differential Equations | 3 |
| MATH 528 | Mathematical Methods for the Physical Sciences I | 3 |
| MATH 529 | Mathematical Methods for the Physical Sciences II | 3 |
| MATH 550 | Topology | 3 |
| MATH 560 | Optimization with Applications in Machine Learning | 3 |
| MATH 566 | Introduction to Numerical Analysis | 3 |
| MATH 577 | Linear Algebra | 3 |
| MATH 590 | Topics in Mathematics (approval based on topic) | 3 |
| MATH 594 | Nonlinear Dynamics | 3 |
| MATH 661 | Scientific Computation I | 3 |
| MATH 662 | Scientific Computation II | 3 |
| STOR 415 | Introduction to Optimization H | 3 |
| STOR 435/MATH 535 | Introduction to Probability H | 3 |
| STOR 445 | Stochastic Modeling | 3 |
| STOR 455 | Methods of Data Analysis H | 3 |
| STOR 512 | Optimization for Machine Learning and Neural Networks | 3 |
| STOR 520 | Statistical Computing for Data Science | 4 |
| STOR 535 | Probability for Data Science | 3 |
| STOR 538 | Sports Analytics | 3 |
| STOR 543 | Dynamic Decision Analytics | 3 |
| STOR 545 | Stochastic Models and their Applications | 3 |
| STOR 555 | Mathematical Statistics | 3 |
| STOR 556 | Time Series Data Analysis | 3 |
| STOR 557 | Advanced Methods of Data Analysis | 3 |
| STOR 565 | Machine Learning | 3 |
| STOR 566 | Introduction to Deep Learning | 3 |
| STOR 572 | Simulation for Analytics | 3 |
| STOR 590 | Special Topics in Statistics and Operations Research (approval based on topic) | 3 |
| STOR 612 | Foundations of Optimization | 3 |
| STOR 634 | Probability I | 3 |
| STOR 712 | Optimization for Machine Learning and Data Science | 3 |
| STOR 893 | Special Topics (approval based on topic) | 1-3 |
| H | Honors version available. An honors course fulfills the same requirements as the nonhonors version of that course. Enrollment and GPA restrictions may apply. |
Economic Analysis Concentration
| Code | Title | Hours |
|---|---|---|
| ECON 400 | Introduction to Data Science and Econometrics 1, H | 4 |
| ECON 470 | Econometrics 1, H | 3 |
| Select one of the following options: | 3 | |
| Advanced Econometrics 1 | ||
| Machine Learning and Econometrics 1 | ||
| Applied Time Series Analysis and Forecasting 1 | ||
| Select one of the following options: | 3 | |
| Macroeconomic Analysis of the Labor Market 1 | ||
| Advanced Financial Economics 1 | ||
| Advanced Industrial Organization 1 | ||
| Advanced Health Econometrics 1 | ||
| Economics of Education 1 | ||
| The Economics of Health Care Markets and Policy 1 | ||
| Advanced Labor Economics 1 | ||
| Total Hours | 13 | |
| H | Honors version available. An honors course fulfills the same requirements as the nonhonors version of that course. Enrollment and GPA restrictions may apply. |
- 1
Course requires a prerequisite(s) not otherwise counting in the major. Please review prerequisite information carefully when planning your course selection.
Data Science in Politics Concentration
| Code | Title | Hours |
|---|---|---|
| POLI 381 | Data in Politics II: Frontiers and Applications 1 | 3 |
| POLI 480 | Experimenting on Politics H | 3 |
| Select one of the following options: | 3 | |
| Analyzing Public Opinion H | ||
| Peace Science Research 1 | ||
| Networks in International Relations | ||
| Game Theory 1 | ||
| Select one of the following options: | 3 | |
| Internship in Political Science 1 | ||
| Mentored Research in Political Science (for 3 credits) | ||
| Total Hours | 12 | |
| H | Honors version available. An honors course fulfills the same requirements as the nonhonors version of that course. Enrollment and GPA restrictions may apply. |
- 1
Course requires a prerequisite(s) not otherwise counting in the major. Please review prerequisite information carefully when planning your course selection.
Urban Analytics Concentration
| Code | Title | Hours |
|---|---|---|
| Select one of the following: | 3 | |
| Cities of the Past, Present, and Future: Introduction to Planning | ||
| Solving Urban Problems | ||
| Planning the City: Possibilities, Participants, and Change | ||
| Tools for Urbanists | ||
| Urban Spatial Structure 1 | ||
| Select one of the following: | 3 | |
| Seminar on The Ethics and Politics of New Urban Analytics | ||
| Select one of the following: | 3 | |
| Urban Data Analytics | ||
| Select one of the following: | 3 | |
| Applied Issues in Geographic Information Systems | ||
| Energy Modeling for Environment and Public Health | ||
| Urban Transportation Planning | ||
| Public Transportation | ||
| Development Planning Techniques | ||
| Planning Methods 1 | ||
| Development Impact Assessment 1 | ||
| Transportation Planning Models 1 | ||
| Total Hours | 12 | |
- 1
700-level courses are listed in the proposal and undergraduates will need special permission to register for courses above 600.
Sports Analytics Concentration
| Code | Title | Hours |
|---|---|---|
| STOR 538 | Sports Analytics | 3 |
| STOR 590 | Special Topics in Statistics and Operations Research (For 24-25 Sports Data Analysis Lab) | 3 |
| Select two of the following: | 6 | |
| Introduction to Sport Administration | ||
| Finance and Economics of Sport | ||
| Predictive Analytics in Sport H | ||
| Methods of Data Analysis H | ||
| Healthcare Risk Analytics | ||
| Dynamic Decision Analytics | ||
| Mathematical Statistics | ||
| Time Series Data Analysis | ||
| Advanced Methods of Data Analysis | ||
| Machine Learning | ||
| Simulation for Analytics | ||
| Total Hours | 12 | |
| H | Honors version available. An honors course fulfills the same requirements as the nonhonors version of that course. Enrollment and GPA restrictions may apply. |
Quantitative Language Science Concentration
| Code | Title | Hours |
|---|---|---|
| COMP 586 | Natural Language Processing 1 | 3 |
| LING 401 | Introduction to Computational Linguistics 1 | 3 |
| LING 460 | Making Sense of Big Data: Textual Analysis with R | 3 |
| LING 540 | Mathematical Linguistics 1 | 3 |
| Total Hours | 12 | |
- 1
Course requires a prerequisite(s) not otherwise counting in the major. Please review prerequisite information carefully when planning your course selection.
Operations Research Concentration
| Code | Title | Hours |
|---|---|---|
| STOR 415 | Introduction to Optimization H | 3 |
| STOR 445 | Stochastic Modeling | 3 |
| Choose two of the following: | 6 | |
| Optimization for Machine Learning and Neural Networks | ||
| Dynamic Decision Analytics | ||
| Stochastic Models and their Applications | ||
| Simulation for Analytics | ||
| Stochastic Modeling I | ||
| Stochastic Modeling II | ||
| Total Hours | 12 | |
| H | Honors version available. An honors course fulfills the same requirements as the nonhonors version of that course. Enrollment and GPA restrictions may apply. |
Mathematical Foundations Concentration
| Code | Title | Hours |
|---|---|---|
| Analytical Methods. Choose one of the following: | 3 | |
| Elementary Differential Equations | ||
| Mathematical Methods for the Physical Sciences I | ||
| Optimization with Applications in Machine Learning | ||
| Optimization for Machine Learning and Neural Networks | ||
| Algebraic and Computational Techniques. Choose one of the following: | 3 | |
| Elementary Theory of Numbers | ||
| Linear Algebra | ||
| Scientific Computation I | ||
| Scientific Computation II | ||
| Statistical Methods. Choose one of the following: | 3 | |
| Mathematical Statistics | ||
| Time Series Data Analysis | ||
| Advanced Methods of Data Analysis | ||
| Statistical Theory I | ||
| Computational and Stochastic Modeling. Choose one of the following: | 3 | |
| Mathematical Modeling in the Life Sciences | ||
| Stochastic Modeling | ||
| Dynamic Decision Analytics | ||
| Stochastic Models and their Applications | ||
| Total Hours | 12 | |
Decision Analytics Concentration
| Code | Title | Hours |
|---|---|---|
| STOR 445 | Stochastic Modeling | 3 |
| STOR 572 | Simulation for Analytics | 3 |
| Choose two of the following: | 6 | |
| Introduction to Machine Learning H | ||
| Methods of Data Analysis H | ||
| Healthcare Risk Analytics | ||
| Optimization for Machine Learning and Neural Networks | ||
| Dynamic Decision Analytics | ||
| Stochastic Models and their Applications | ||
| Mathematical Statistics | ||
| Machine Learning | ||
| Total Hours | 12 | |
| H | Honors version available. An honors course fulfills the same requirements as the nonhonors version of that course. Enrollment and GPA restrictions may apply. |
Statistical Learning and Data Analysis Concentration
| Code | Title | Hours |
|---|---|---|
| STOR 555 | Mathematical Statistics | 3 |
| STOR 565 | Machine Learning | 3 |
| Choose two of the following: | 6 | |
| Optimization for Machine Learning and Neural Networks | ||
| Time Series Data Analysis | ||
| Advanced Methods of Data Analysis | ||
| Introduction to Deep Learning | ||
| Statistical Theory I | ||
| Statistical Theory II | ||
| Applied Statistics I | ||
| Applied Statistics II | ||
| Total Hours | 12 | |
Advanced Artificial Intelligence and Machine Learning Concentration
| Code | Title | Hours |
|---|---|---|
| DATA 520 | Research-Methods for Socially Responsible AI: An Ethical Expedition | 3 |
| DATA 521 | Foundations in Artificial Intelligence | 3 |
| Selection one of the following courses: | 3 | |
| Practical Deep Learning Systems | ||
| Modeling and Data Mining For Artificial Intelligence | ||
| One additional course from the AI and Machine Learning Competency: | 3 | |
| Introduction to Machine Learning | ||
| Introduction to Machine Learning H | ||
| Optimization with Applications in Machine Learning | ||
| Machine Learning | ||
| Introduction to Deep Learning | ||
| Total Hours | 12 | |
| H | Honors version available. An honors course fulfills the same requirements as the nonhonors version of that course. Enrollment and GPA restrictions may apply. |
Health Informatics Concentration
| Code | Title | Hours |
|---|---|---|
| CHIP 725 | Electronic Health Records 1 | 3 |
| CHIP 690 | Intermediate Selected Topics 2 | 3 |
| CHIP 710 | Systems Analysis in Healthcare 1 | 3 |
| Select one of the following elective courses: | 3 | |
| Health Informatics Internship (for 3 credits) 1 | ||
| Database Systems in Healthcare 1 | ||
| Healthcare Systems in the US 1 | ||
| Healthcare Risk Analytics | ||
| Total Hours | 12 | |
- 1
700-level graduate courses require permission of the instructor for undergraduate students to enroll.
- 2
With approval based on the topic.
The School of Data Science and Society offers robust student support through a cohort-based community that includes academic advising and faculty mentoring. The school also provides career services that include resume preparation, interview practice, and internship support.
Students pursuing the B.S. in data science have the option to pursue a 4-course concentration in a variety of disciplines. Current concentrations include:
Economic Analysis
- The Data Science Concentration in Economic Analysis is intended to prepare students for careers in quantitatively focused occupations at the intersection of economics and data science. The courses for the concentration were selected to offer a rigorous foundation for econometric and data science methodologies commonly used in economic analysis, as well as to develop an understanding of the application of these methods in at least one field within economics (e.g., industrial organization, health, labor, etc.).
Data Science in Politics
- The concentration in Data Science in Politics is intended to provide students with opportunities to apply data science tools to the study of politics, across the various subfields of the discipline. The concentration assumes students have a foundation in basic probability and statistical programming in R prior to starting the concentration, allowing content to focus on applied regression models and experimental methods in political science. Students have the option to pursue a mentored quantitative research project led by a political science faculty member or complete an internship in a political data analytics position with departmental approval.
Urban Data Analytics
- The Urban Data Analytics concentration equips students with the skills necessary to apply data science tools for the public good, specifically to improve the quality of life of people in human settlements. Students will explore the design of the Amercian city, understand the systems to collect and maintain urban data, analyze urban data, and apply urban data analysis to planning practice.
Sports Analytics
- The Sports Analytics concentration equips students to synthesize collected data into meaningful and actionable information that can impact decisions made in the sports industry. Students will learn how to use data and statistics to make predictions about player/team performance. This concentration combines foundational and methodological tools in statistics and analytics with domain expertise in exercise and sports science.
Quantitative Language Science
- This concentration will allow students to explore how data science methods including statistical, mathematical, and computational methods are applied to study theoretical language science/linguistics and natural language processing. This concentration will familiarize students with linguistics concepts in the context of data science methodology and prepare them for jobs or further study in computational linguistics and artificial intelligence.
Operations Research
- This concentration is for students who are particularly interested in operations research, using mathematical modeling and computational techniques to analyze complex systems and make decisions. It is an ideal path for students who are interested in careers in operations research as well as those who are interested in graduate studies in disciplines such as operations research, industrial and systems engineering, operations management, and decision sciences.
Mathematical Foundations
- The Mathematical Foundations concentration is aimed at giving students a deeper mathematical and statistical understanding of general data science techniques and special data features in applications. The selected courses for the concentration highlight quantitative areas relevant to cutting-edge statistical and mathematical methods in the field. In particular, the concentration will allow students to explore the theory behind techniques essential to data science and mathematical modeling.
Decision Analytics
- The Decision Analytics concentration is for students who are interested in focusing their studies on making data-driven decisions in complex systems. While statistical techniques are very helpful for making decisions in data-rich settings, stylized formulations can be more helpful for generating insights and making decisions in the absence of sufficient data. This concentration brings together both statistics and operations research-based courses together to offer students a path for getting a more holistic training in analytical decision making.
Statistical Learning and Data Analytics
- The concentration in Statistical Learning and Data Analysis combines advanced mathematical and statistical training with enhanced computational and data analytic training for students planning careers in information-intensive industries or research. Students will extend their ability to model and analyze data, using mathematical and computational methods to make predictions and decisions in the face of uncertainty. The concentration is focused on fundamental training in mathematics and applied statistics, including specialized courses with an emphasis on statistical computing, and machine learning.
Advanced Artificial Intelligence and Machine Learning
-
The concentration intentionally builds upon core requirements for the B.S. in data science, allowing students to delve deeper into the rapidly advancing technologies and methodologies of artificial intelligence (AI) and machine learning (ML). The concentration has three foci designed to meet workforce demands: foundations in AI, advanced exploration of the ethics of AI, and advanced applications of AI and ML.
Health Informatics
-
The Health Informatics concentration focuses on the conceptual framework of healthcare information systems, exploring data types and structures as well as the systems within which health informatics solutions are typically situated. Students will gain fundamental knowledge in data science techniques, legal and regulatory compliance, and healthcare statistics and findings that inform policy, clinical operations, risk management, and financial management.
Department Programs
Major
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