Data Science Major, B.S.
The bachelor of science (B.S.) in data science provides students with a foundation in data science in preparation for entry to the workforce or pursuit of an advanced degree. The B.S. in data science is comprised of six competencies that include ethics, communications, computational thinking, mathematical and statistical foundations, optimization and multivariate thinking, and machine learning and AI. The curriculum provides high-level coursework, in-depth exposure to quantitative topics, and opportunities for direct application through collaborative teamwork.
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 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
Upon completion of the data science program, students should be able to
Mathematical and Statistical Foundations:
- Use appropriate data analytics and statistical techniques to discover new relationships, deliver insights into research problems or organizational processes, and support decision-making.
Computational Foundations:
- Describe how operating systems and networks are created, organized, and transmit information. Build and understand algorithms for analyzing large data sets and accurate numerical modeling for problems.
Multivariate Thinking and Optimization:
- Analyze and suggest organizational processes for various optimization strategies (e.g., machine learning principles and computational algorithms for analyzing network properties) using a variety of tools originating from advanced mathematical and statistical theory.
Machine Learning and AI:
- Select appropriate classes of machine learning methods for specific problems and use appropriate training and testing methodologies when deploying algorithms.
Communications:
- Convey data analyses through written and oral communication skills as well as visualization techniques.
Responsible Data Science:
- Apply security, privacy protection, governance, and ethical considerations in data management.
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 † | 3 |
| DATA 120 | Ethics of Data Science and Artificial Intelligence | 3 |
| Communications (select one): | 3 | |
| Communication for Data Scientists | ||
| Public Speaking | ||
| Argumentation and Debate | ||
| Picture This: Principles of Visual Rhetoric | ||
| Scientific and Technical Communication | ||
| Writing 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 | ||
| Probability for Data Science | ||
| Probability I | ||
| Optimization and Multivariate Thinking (select one): | 3 | |
| Advanced Calculus II H | ||
| Elementary Differential Equations | ||
| Optimization with Applications in Machine Learning | ||
| Introduction to Optimization | ||
| Foundations of Optimization | ||
| Machine Learning and AI (select one): | 3 | |
| Introduction to Machine Learning | ||
| Introduction to Machine Learning H | ||
| 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 | ||
| Introduction to 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. Any course listed under the above competencies can be counted as an upper-level elective if it is not counted towards the fulfillment of the competency. | 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 347 | Linear Algebra for Applications † | 3 |
| STOR 120 | Foundations of Statistics and Data Science †, F | 3-4 |
| or COMP 110 | Introduction to Programming and Data Science | |
| or COMP 116 | Introduction to Scientific Programming | |
| MATH 233 | Calculus of Functions of Several Variables †, H, F | 4 |
| or MATH 235 | Mathematics for Data Science | |
| MATH 381 | Discrete Mathematics †, H | 3-4 |
| or STOR 315 | Discrete Mathematics for Data Science | |
| or COMP 283 | Discrete Structures | |
| 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.
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 486 | Applications of Natural Language Processing | 3 |
| COMP 488 | Data Science in the Business World | 3 |
| COMP 550 | Algorithms and Analysis | 3 |
| COMP 560 | Artificial Intelligence | 3 |
| COMP 576 | Mathematics for Image Computing | 3 |
| COMP 664 | Deep Learning | 3 |
| COMP 722 | Data Mining | 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 577 | Linear Algebra | 3 |
| MATH 590 | Topics in Mathematics (approval based on topic) | 3 |
| MATH 594 | Nonlinear Dynamics | 3 |
| MATH 662 | Scientific Computation II | 3 |
| STOR 445 | Stochastic Modeling | 3 |
| STOR 455 | Methods of Data Analysis | 3 |
| STOR 515 | Dynamic Decision Analytics | 3 |
| STOR 538 | Sports Analytics | 3 |
| STOR 555 | Mathematical Statistics | 3 |
| STOR 556 | Time Series Data Analysis | 3 |
| STOR 557 | Advanced Methods of Data Analysis | 3 |
| STOR 590 | Special Topics in Statistics and Operations Research (approval based on topic) | 3 |
| STOR 712 | Optimization for Machine Learning and Data Science | 3 |
| STOR 893 | Special Topics (approval based on topic) | 1-3 |
| MATH 662 | Scientific Computation II | 3 |
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 | 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.
The School of Data Science and Society offers support to secure internship and research opportunities.
Dean
Stan Ahalt
