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.

 
Core Requirements
DATA 110IDEAs in Action General Education logo Introduction to Data Science †, H3
DATA 120IDEAs in Action General Education logo Ethics of AI and Societal Decision Making H3
Communications (select one):3
IDEAs in Action General Education logo Public Speaking
IDEAs in Action General Education logo Argumentation and Debate
IDEAs in Action General Education logo Communication for Data Scientists
IDEAs in Action General Education logo Picture This: Principles of Visual Rhetoric
IDEAs in Action General Education logo Scientific and Technical Communication
IDEAs in Action General Education logo Composing for Clients: Technical Communication Practicum
Maps: Geographic Information from Babylon to Google
IDEAs in Action General Education logo 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
IDEAs in Action General Education logo 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. 118
Additional Requirements
MATH 231IDEAs in Action General Education logo Calculus of Functions of One Variable I †, H, F4
MATH 232IDEAs in Action General Education logo Calculus of Functions of One Variable II †, H, F4
MATH 233IDEAs in Action General Education logo Calculus of Functions of Several Variables †, H, F4
or MATH 235 IDEAs in Action General Education logo Mathematics for Data Science
MATH 347IDEAs in Action General Education logo Linear Algebra for Applications †, F3
MATH 381Discrete Mathematics †, H3-4
or STOR 315 IDEAs in Action General Education logo Discrete Mathematics for Data Science
or COMP 283 IDEAs in Action General Education logo Discrete Structures
STOR 120IDEAs in Action General Education logo Foundations of Statistics and Data Science †, H, F3-4
or COMP 110 IDEAs in Action General Education logo Introduction to Programming
or COMP 116 Introduction to Scientific Programming
Total Hours60-63
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 

BIOS 645Principles of Experimental Analysis3
BIOS 664Sample Survey Methodology4
COMP 421Files and Databases3
COMP 488Data Science in the Business World3
COMP 550IDEAs in Action General Education logo Algorithms and Analysis3
COMP 560Artificial Intelligence3
COMP 562Introduction to Machine Learning H3
COMP 586Natural Language Processing3
COMP 664Deep Learning3
COMP 683Computational Biology3
DATA 481IDEAs in Action General Education logo Data Science Practicum3
DATA 493IDEAs in Action General Education logo Internship in Data Science3
DATA 495IDEAs in Action General Education logo Mentored Research in Data Science3
DATA 496Directed Exploration in Data Science3
DATA 543Risk, Data Science and AI3
DATA 520IDEAs in Action General Education logo Research-Methods for Socially Responsible AI: An Ethical Expedition3
DATA 521Foundations in Artificial Intelligence3
DATA 522Practical Deep Learning Systems3
DATA 523Modeling and Data Mining For Artificial Intelligence 3
DATA 593IDEAs in Action General Education logo Internship in Data Science12
DATA 693HIDEAs in Action General Education logo Honors Thesis in Data Science3
DATA 694HIDEAs in Action General Education logo Honors Thesis in Data Science3
DATA 890Special Topics in Data Science (one time only, with permission)3
ENGL 411IDEAs in Action General Education logo Composing for Clients: Technical Communication Practicum3
GEOG 415IDEAs in Action General Education logo Communicating Important Ideas 3
INLS 541Information Visualization3
MATH 521Advanced Calculus I H3
MATH 522Advanced Calculus II H3
MATH 524Elementary Differential Equations3
MATH 528Mathematical Methods for the Physical Sciences I3
MATH 529Mathematical Methods for the Physical Sciences II3
MATH 550Topology3
MATH 560Optimization with Applications in Machine Learning3
MATH 566Introduction to Numerical Analysis3
MATH 577Linear Algebra3
MATH 590Topics in Mathematics (approval based on topic)3
MATH 594Nonlinear Dynamics3
MATH 661Scientific Computation I3
MATH 662Scientific Computation II3
STOR 415Introduction to Optimization H3
STOR 435/MATH 535Introduction to Probability H3
STOR 445Stochastic Modeling3
STOR 455Methods of Data Analysis H3
STOR 512Optimization for Machine Learning and Neural Networks3
STOR 520Statistical Computing for Data Science4
STOR 535Probability for Data Science3
STOR 538Sports Analytics3
STOR 543Dynamic Decision Analytics3
STOR 545Stochastic Models and their Applications3
STOR 555Mathematical Statistics3
STOR 556Time Series Data Analysis3
STOR 557Advanced Methods of Data Analysis3
STOR 565Machine Learning3
STOR 566Introduction to Deep Learning3
STOR 572Simulation for Analytics3
STOR 590Special Topics in Statistics and Operations Research (approval based on topic)3
STOR 612Foundations of Optimization3
STOR 634Probability I3
STOR 712Optimization for Machine Learning and Data Science3
STOR 893Special 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 

ECON 400IDEAs in Action General Education logo Introduction to Data Science and Econometrics 1, H4
ECON 470IDEAs in Action General Education logo Econometrics 1, H3
Select one of the following options:3
IDEAs in Action General Education logo Advanced Econometrics 1
IDEAs in Action General Education logo 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
IDEAs in Action General Education logo Advanced Financial Economics 1
IDEAs in Action General Education logo Advanced Industrial Organization 1
IDEAs in Action General Education logo Advanced Health Econometrics 1
IDEAs in Action General Education logo Economics of Education 1
IDEAs in Action General Education logo The Economics of Health Care Markets and Policy 1
IDEAs in Action General Education logo Advanced Labor Economics 1
Total Hours13
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

POLI 381Data in Politics II: Frontiers and Applications 13
POLI 480IDEAs in Action General Education logo Experimenting on Politics H3
Select one of the following options:3
IDEAs in Action General Education logo Analyzing Public Opinion H
IDEAs in Action General Education logo Peace Science Research 1
IDEAs in Action General Education logo Networks in International Relations
Game Theory 1
Select one of the following options:3
IDEAs in Action General Education logo Internship in Political Science 1
IDEAs in Action General Education logo Mentored Research in Political Science (for 3 credits)
Total Hours12
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

Select one of the following:3
IDEAs in Action General Education logo Cities of the Past, Present, and Future: Introduction to Planning
IDEAs in Action General Education logo Solving Urban Problems
IDEAs in Action General Education logo Planning the City: Possibilities, Participants, and Change
IDEAs in Action General Education logo 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
IDEAs in Action General Education logo Urban Transportation Planning
IDEAs in Action General Education logo Public Transportation
Development Planning Techniques
Planning Methods 1
Development Impact Assessment 1
Transportation Planning Models 1
Total Hours12
1

700-level courses are listed in the proposal and undergraduates will need special permission to register for courses above 600. 

Sports Analytics Concentration 

STOR 538Sports Analytics3
STOR 590Special 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
IDEAs in Action General Education logo 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 Hours12
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

COMP 586Natural Language Processing 13
LING 401IDEAs in Action General Education logo Introduction to Computational Linguistics 13
LING 460IDEAs in Action General Education logo Making Sense of Big Data: Textual Analysis with R3
LING 540IDEAs in Action General Education logo Mathematical Linguistics 13
Total Hours12
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

STOR 415Introduction to Optimization H3
STOR 445Stochastic Modeling3
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 Hours12
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

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 Hours12

Decision Analytics Concentration

STOR 445Stochastic Modeling3
STOR 572Simulation for Analytics3
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 Hours12
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

STOR 555Mathematical Statistics3
STOR 565Machine Learning3
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 Hours12

Advanced Artificial Intelligence and Machine Learning Concentration

DATA 520IDEAs in Action General Education logo Research-Methods for Socially Responsible AI: An Ethical Expedition3
DATA 521Foundations in Artificial Intelligence3
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 Hours12
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

CHIP 725Electronic Health Records 13
CHIP 690Intermediate Selected Topics 23
CHIP 710Systems Analysis in Healthcare 13
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 Hours12
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

Minor

Courses 

School of Data Science and Society

Visit Program Website

211 Manning Drive, CB# 3177

Director of Undergraduate Studies

David Adalsteinsson

david@unc.edu

Assistant Dean for Academic Affairs

Johanna Foster

Johanna_Foster@unc.edu

Executive Director of Undergraduate Programs

Katie Smith

smithkw@unc.edu

Student Services Manager

Blake Rahn

cblaker@unc.edu

Dean

Stan Ahalt

sdss@unc.edu

Senior Associate Dean for Academic and Faculty Affairs

Amarjit Budhiraja

budhiraj@email.unc.edu