Data Science Major, B.S.

The bachelor of science (B.S.) in Data Science prepares students with a strong computational and technical foundation to enter the workforce or pursue an advanced degree. Students learn skills and concepts in the following six competencies:

• Responsible Data Science
• Communication
• Computational Thinking
• Mathematical and Statistical Foundations
• Optimization
• Machine Learning and Artificial Intelligence (AI)

The curriculum also provides opportunities for direct application through four-course concentrations, upper-level electives, mentored research, and internships.

Advising

Students must apply and be accepted to the B.S. in Data Science. After admission and acceptance, each student will receive staff and faculty advisors in the Division of Data Science and Society within the School of Data and Information Sciences, who provide academic advising, career services, and community-building activities.

Preparing for the Bachelor of Science in Data Science

Students seeking to declare the bachelor of science (B.S.) in Data Science must apply and be admitted to the School of Data and Information Sciences (SDIS). The school offers admissions cycles in the fall and spring semesters for the B.S. in Data Science.

The B.S. in Data Science requires seven prerequisites, which students typically complete in the first four semesters. Students are eligible for admission while prerequisites are in progress or outstanding. Many students are admitted having completed four or five of the seven prerequisites. A cumulative minimum G.P.A. of 3.0 is required for admission to the major. Questions regarding admission to the B.S. in Data Science can be directed to SDSSAcademics@unc.edu.

A pre-data science track includes successful completion (defined as earning a final grade of at least a C, not C-) of the following courses (or their equivalents): 

Course List

Code	Title	Hours
DATA 110	Introduction to Data Science H	3
One of the following:
STOR 120	Foundations of Statistics and Data Science H, F
COMP 110	Introduction to Programming H
COMP 116	Introduction to Scientific Programming
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	4
or MATH 235	Mathematics for Data Science
MATH 347	Linear Algebra for Applications F	3
One of the following:		3
STOR 315	Discrete Mathematics for Data Science
COMP 283	Discrete Structures H
MATH 381	Discrete Mathematics H
Total Hours		21

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.

Student Learning Outcomes

The B.S. in Data Science prepares students in six competencies with the 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.

Communication

• The Communication 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 Thinking

• The Computational Thinking competency teaches students how to turn data into information, whether starting at the beginning of the data lifecycle or working with available data at any stage, by exploring the data types, formats, relationships, and cleanliness. Students will evaluate the different tools and how to choose the best one, whether databases, software packages, machine learning, or visualization tools, to solve data science problems. Students will also gain experience evaluating results from pilot studies and extrapolating to scale.

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 concepts to support data-informed decision-making, including calculus, linear algebra, probability theory, inference, modeling, and optimization. These principles are critical for analyzing “big” or “small” datasets of varying complexities.

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 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 

Course List

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:		3
DATA 150	Communication for Data Scientists
Mathematical and Statistical Foundations (2 courses):		6
DATA 441	Statistical and Mathematical Foundations of Data Science
And select one from the following:
BIOS 650	Basic Elements of Probability and Statistical Inference I
STOR 435/MATH 535	Introduction to Probability H
STOR 535	Probability for Data Science
STOR 634	Probability I
Optimization (select one):		3
MATH 560	Optimization with Applications in Machine Learning
STOR 415	Introduction to Optimization H
STOR 612	Foundations of Optimization
Machine Learning and AI (select one):		3
BIOS 635	Introduction to Machine Learning
COMP 560	Artificial Intelligence
COMP 562	Introduction to Machine Learning H
DATA 521	Foundations in Artificial Intelligence
DATA 522	Practical Deep Learning Systems
STOR 565	Machine Learning
STOR 566	Introduction to Deep Learning
Computational Thinking (2 courses):		6
DATA 215	Computational Doing
And select one of the following
BIOS 511	Introduction to Statistical Computing and Data Management
DATA 440	Computational Methods For Data Science
MATH 566	Introduction to Numerical Analysis
STOR 520	Statistical Computing for Data Science
STOR 572	Simulation for Analytics
Choose four upper-division electives (see list below) OR a four-course concentration. 1		12
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	4
or MATH 235	Mathematics for Data Science
MATH 347	Linear Algebra for Applications †, F	3
MATH 381	Discrete Mathematics †, H	3
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		60-61

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 and Information Sciences.  

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 

Course List

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 440	Computational Methods For Data Science	3
DATA 441	Statistical and Mathematical Foundations of Data Science	3
DATA 442	System Design and Engineering	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 510	Data Science Methodologies in Biological and Health Sciences	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 590	Special Topics in Data Science	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 

Course List

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
ECON 571	Advanced Econometrics 1
ECON 573I	Machine Learning and Econometrics 1
ECON 575	Applied Time Series Analysis and Forecasting 1
Select one of the following options:		3
ECON 522	Macroeconomic Analysis of the Labor Market 1
ECON 525	Advanced Financial Economics 1
ECON 545	Advanced Industrial Organization 1
ECON 550	Advanced Health Econometrics 1
ECON 551	Economics of Education 1
ECON 552	The Economics of Health Care Markets and Policy 1
ECON 580	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

Course List

Code	Title	Hours
POLI 381	Data in Politics II: Frontiers and Applications 1	3
POLI 480	Survey Experiments H	3
Select one of the following options:		3
POLI 209	Analyzing Public Opinion H
POLI 350	Peace Science Research 1
POLI 487	Networks in International Relations
POLI 488	Game Theory 1
Select one of the following options:		3
POLI 193	Internship in Political Science 1
POLI 395	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

Course List

Code	Title	Hours
Select one of the following:		3
PLAN 246	Cities of the Past, Present, and Future: Introduction to Planning
PLAN 247	Solving Urban Problems
PLAN 256	Planning the City: Possibilities, Participants, and Change
PLAN 257	Tools for Urbanists
PLAN 714	Urban Spatial Structure 1
Select one of the following:		3
PLAN 562	Seminar on The Ethics and Politics of New Urban Analytics
Select one of the following:		3
PLAN 672	Urban Data Analytics
Select one of the following:		3
PLAN 591	Applied Issues in Geographic Information Systems
PLAN 635	Energy Modeling for Environment and Public Health
PLAN 636	Urban Transportation Planning
PLAN 637	Public Transportation
PLAN 671	Development Planning Techniques
PLAN 720	Planning Methods 1
PLAN 745	Development Impact Assessment 1
PLAN 739	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 

Course List

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
EXSS 221	Introduction to Sport Administration
EXSS 324	Finance and Economics of Sport
EXSS 327	Predictive Analytics in Sport H
STOR 455	Methods of Data Analysis H
STOR 475	Healthcare Risk Analytics
STOR 543	Dynamic Decision Analytics
STOR 555	Mathematical Statistics
STOR 556	Time Series Data Analysis
STOR 557	Advanced Methods of Data Analysis
STOR 565	Machine Learning
STOR 572	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

Course List

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

Course List

Code	Title	Hours
STOR 415	Introduction to Optimization H	3
STOR 445	Stochastic Modeling	3
Choose two of the following:		6
STOR 512	Optimization for Machine Learning and Neural Networks
STOR 543	Dynamic Decision Analytics
STOR 545	Stochastic Models and their Applications
STOR 572	Simulation for Analytics
STOR 641	Stochastic Modeling I
STOR 642	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

Course List

Code	Title	Hours
Analytical Methods. Choose one of the following:		3
MATH 524	Elementary Differential Equations
MATH 528	Mathematical Methods for the Physical Sciences I
MATH 560	Optimization with Applications in Machine Learning
STOR 512	Optimization for Machine Learning and Neural Networks
Algebraic and Computational Techniques. Choose one of the following:		3
MATH 533	Elementary Theory of Numbers
MATH 577	Linear Algebra
MATH 661	Scientific Computation I
MATH 662	Scientific Computation II
Statistical Methods. Choose one of the following:		3
STOR 555	Mathematical Statistics
STOR 556	Time Series Data Analysis
STOR 557	Advanced Methods of Data Analysis
STOR 654	Statistical Theory I
Computational and Stochastic Modeling. Choose one of the following:		3
MATH 564	Mathematical Modeling in the Life Sciences
STOR 445	Stochastic Modeling
STOR 543	Dynamic Decision Analytics
STOR 545	Stochastic Models and their Applications
Total Hours		12

Decision Analytics Concentration

Course List

Code	Title	Hours
STOR 445	Stochastic Modeling	3
STOR 572	Simulation for Analytics	3
Choose two of the following:		6
COMP 562	Introduction to Machine Learning H
STOR 455	Methods of Data Analysis H
STOR 475	Healthcare Risk Analytics
STOR 512	Optimization for Machine Learning and Neural Networks
STOR 543	Dynamic Decision Analytics
STOR 545	Stochastic Models and their Applications
STOR 555	Mathematical Statistics
STOR 565	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

Course List

Code	Title	Hours
STOR 555	Mathematical Statistics	3
STOR 565	Machine Learning	3
Choose two of the following:		6
STOR 512	Optimization for Machine Learning and Neural Networks
STOR 556	Time Series Data Analysis
STOR 557	Advanced Methods of Data Analysis
STOR 566	Introduction to Deep Learning
STOR 654	Statistical Theory I
STOR 655	Statistical Theory II
STOR 664	Applied Statistics I
STOR 665	Applied Statistics II
Total Hours		12

Advanced Artificial Intelligence and Machine Learning Concentration

Course List

Code	Title	Hours
DATA 520	Research-Methods for Socially Responsible AI: An Ethical Expedition	3
Selection one of the following:		3
DATA 521	Foundations in Artificial Intelligence
COMP 560	Artificial Intelligence
Selection two of the following courses:		6
DATA 522	Practical Deep Learning Systems
DATA 523	Modeling and Data Mining For Artificial Intelligence
BIOS 635	Introduction to Machine Learning
COMP 562	Introduction to Machine Learning H
MATH 560	Optimization with Applications in Machine Learning
STOR 565	Machine Learning
STOR 566	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

Course List

Code	Title	Hours
CHIP 708	Foundations of Clinical Data Science	3
CHIP 710	Systems Analysis in Healthcare 1	3
CHIP 725	Electronic Health Records 1	3
Select one of the following elective courses:		3
CHIP 690	Intermediate Selected Topics (Section 295) 2
CHIP 721	Healthcare Systems in the US 1
CHIP 732	Database Systems in Healthcare 1
CHIP 793	Health Informatics Internship (for 3 credits) 1
DATA 510	Data Science Methodologies in Biological and Health Sciences
STOR 475	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. 

Computational Sociology and Data Science

Course List

Code	Title	Hours
SOCI 251	Research Methods (take first)	3
SOCI 318	Computational Sociology	3
Take two of the following, with one being at the 400-level:		6
SOCI 121	Population Problems
SOCI 172	Introduction to Population Health in the United States
SOCI 180	Introduction to Global Population Health
SOCI 277	Societies and Genomics H
SOCI 412	Social Stratification
SOCI 421	Environmental Sociology
SOCI 468	United States Poverty and Public Policy
SOCI 469	Health and Society
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.

Special Opportunities

The B.S. in Data Science offers robust student support through a cohort-based community that includes academic advising and faculty mentoring. The program 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.

Computational Sociology

• The concentration in Data Science in Computational Sociology is intended to provide students with opportunities to apply data scientific tools to sociology research and to synergize data science approaches with toolkits conventionally found in the social sciences. It assumes students have had a chance to build a foundation on basic probability and statistical programming in R prior to starting the concentration.