Data Science Major, B.S. < University of North Carolina at Chapel Hill (2024)

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 throughcollaborative 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 theschool'swebsitefor 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

Course List

Code	Title	Hours
Core Requirements
DATA110	Introduction to Data Science †	3
DATA120	Ethics of Data Science and Artificial Intelligence	3
Communications (select one):		3
DATA150	Communication for Data Scientists
COMM113	Public Speaking
COMM171	Argumentation and Debate
ENGL119	Picture This: Principles of Visual Rhetoric
ENGL303	Scientific and Technical Communication
ENGL411	Writing for Clients: Technical Communication Practicum
GEOG115	Maps: Geographic Information from Babylon to Google
GEOG415	Communicating Important Ideas
INLS541	Information Visualization
MEJO102	Future Vision: Exploring the Visual World
Mathematical and Statistical Foundations (select one):		3
BIOS650	Basic Elements of Probability and Statistical Inference I
MATH521	Advanced Calculus I H
STOR 435/MATH535	Introduction to Probability
STOR535	Probability for Data Science
STOR634	Probability I
Optimization and Multivariate Thinking (select one):		3
MATH522	Advanced Calculus II H
MATH524	Elementary Differential Equations
MATH560	Optimization with Applications in Machine Learning
STOR415	Introduction to Optimization
STOR612	Foundations of Optimization
Machine Learning and AI (select one):		3
BIOS635	Introduction to Machine Learning
COMP562	Introduction to Machine Learning H
STOR565	Machine Learning
STOR566	Introduction to Deep Learning
Computational Thinking (select one):		3-4
BIOS511	Introduction to Statistical Computing and Data Management
BIOS512	Data Science Basics
COMP301	Foundations of Programming
MATH566	Introduction to Numerical Analysis
MATH661	Scientific Computation I
STOR320	Introduction to Data Science
STOR520	Statistical Computing for Data Science
STOR572	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
MATH231	Calculus of Functions of One Variable I †, H, F	4
MATH232	Calculus of Functions of One Variable II †, H, F	4
MATH347	Linear Algebra for Applications †	3
STOR120	Foundations of Statistics and Data Science †, F	3-4
orCOMP110	Introduction to Programming and Data Science
orCOMP116	Introduction to Scientific Programming
MATH233	Calculus of Functions of Several Variables †, H, F	4
orMATH235	Mathematics for Data Science
MATH381	Discrete Mathematics †, H	3-4
orSTOR315	Discrete Mathematics for Data Science
orCOMP283	Discrete Structures
Total Hours		60-63

†

Must be completed to apply to the School of Data Science and Society.

Upper-Division Electives

Course List

Code	Title	Hours
BIOS645	Principles of Experimental Analysis	3
BIOS664	Sample Survey Methodology	4
COMP421	Files and Databases	3
COMP486	Applications of Natural Language Processing	3
COMP488	Data Science in the Business World	3
COMP550	Algorithms and Analysis	3
COMP560	Artificial Intelligence	3
COMP576	Mathematics for Image Computing	3
COMP664	Deep Learning	3
COMP722	Data Mining	3
MATH528	Mathematical Methods for the Physical Sciences I	3
MATH529	Mathematical Methods for the Physical Sciences II	3
MATH550	Topology	3
MATH577	Linear Algebra	3
MATH590	Topics in Mathematics (approval based on topic)	3
MATH594	Nonlinear Dynamics	3
MATH662	Scientific Computation II	3
STOR445	Stochastic Modeling	3
STOR455	Methods of Data Analysis	3
STOR515	Dynamic Decision Analytics	3
STOR538	Sports Analytics	3
STOR555	Mathematical Statistics	3
STOR556	Time Series Data Analysis	3
STOR557	Advanced Methods of Data Analysis	3
STOR590	Special Topics in Statistics and Operations Research (approval based on topic)	3
STOR712	Optimization for Machine Learning and Data Science	3
STOR893	Special Topics (approval based on topic)	1-3
MATH662	Scientific Computation II	3

Economic Analysis Concentration

Course List

Code	Title	Hours
ECON400	Introduction to Data Science and Econometrics 1, H	4
ECON470	Econometrics 1, H	3
Select one of the following options:		3
ECON571	Advanced Econometrics 1
ECON573	Machine Learning and Econometrics 1
ECON575	Applied Time Series Analysis and Forecasting 1
Select one of the following options:		3
ECON522	Macroeconomic Analysis of the Labor Market 1
ECON525	Advanced Financial Economics 1
ECON545	Advanced Industrial Organization 1
ECON550	Advanced Health Econometrics 1
ECON551	Economics of Education 1
ECON552	The Economics of Health Care Markets and Policy 1
ECON580	Advanced Labor Economics 1
Total Hours		13

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
POLI381	Data in Politics II: Frontiers and Applications 1	3
POLI480	Experimenting on Politics	3
Select one of the following options:		3
POLI209	Analyzing Public Opinion H
POLI350	Peace Science Research 1
POLI487	Networks in International Relations
POLI488	Game Theory 1
Select one of the following options:		3
POLI193	Internship in Political Science 1
POLI395	Mentored Research in Political Science (for 3 credits)
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.

The School of Data Science and Society offers support to secure internship and research opportunities.