# Undergraduate Degree Details

**Bachelor of Science in Data Science**

**Prerequisites:**

DATA 110: Intro to Data Science

One of the following:

- STOR 120: Foundations of Statistics and Data Science
- COMP 110: Intro to Programming
- COMP 116: Introduction to Scientific Programming

MATH 231: Calculus 1

MATH 232: Calculus II

One of the following:

- MATH 233: Multivariate Calculus
- MATH/STOR 235: Math for Data Science

MATH 347: Linear Algebra

One of the following:

- STOR 315: Discrete Math for Data Science
- COMP 283: Discrete Structures
- MATH 381: Discrete Math

**Upper-Division Requirements**

*Responsible Data Science*

DATA 120: Ethics of Data Science and Artificial Intelligence

*Communication:
One of the following*

DATA 150: Communication for Data Scientists

Any communication course listed for the B.A.

*Mathematical and Statistical Foundations:
One of the following:*

MATH 521: ADV CALC I

STOR 435/MATH 535: Intro to Probability

STOR 535: Probability for Data Science

STOR 634: Probability I

*Optimization and Multivariable Thinking:
One of the following:*

MATH 524: Elem Differ Equation

MATH 522: ADV CALC II

MATH 560: Optimization App Mach Learn

STOR 415: Intro to Optimization

STOR 612: Foundations of Optimization

*Machine Learning and AI:
One of the following:*

BIOS 635: Intro to Machine Learning

COMP 562: Intro to Machine Learning

STOR 565: Machine Learning

STOR 566: Intro to Deep Learning

MATH 560: Optimization with Applications in Machine Learning

*Computational Thinking:
*

*One of the following:*

COMP 301: Foundations of Programming

MATH 566: Numerical Analysis

MATH 661: Scientific Computation I

STOR 320: Introduction to Data Science

STOR 520: Statistical Computing for Data Science

STOR 572: Simulation for Analytics

B.S. Students complete 18 credit hours for electives over six courses:

Six approved electives OR a four-course concentration and two approved electives.

Click for list of B.S. in Data Science electives. *Additional concentrations under development.*

**Economic Analysis Concentration**

ECON 400: Introduction to Data Science and Econometrics

ECON 470: Econometrics

Select one of the following:

ECON 571: Advanced Econometrics

ECON 573: Machine Learning and Econometrics

ECON 575: Applied Time Series Analysis and Forecasting

Select one of the following:

ECON 522: Macroeconomic Analysis of the Labor Market

ECON 525: Advanced Financial Economics

ECON 545: Advanced Industrial Organization

ECON 550: Advanced Health Econometrics

ECON 551: Economics in Education

ECON 552: The Economics of Health Care Markets and Policy

ECON 580: Advanced Labor Economics

**Data Science in Politics Concentration**

POLI 381: Data in Politics II: Frontiers and Applications

POLI 480: Experimenting on Politics

Select one of the following:

POLI 209: Analyzing Public Opinion

POLI 350: Peace Science Research

POLI 487: Networks in International Relations

POLI 488: Game Theory

Select one of the following:

POLI 193: Internship in Political Science

POLI 395: Mentored Research in Political Science (for 3 Credits)

**Bachelor of Arts in Data Science**

*Foundations of Data and Information
*DATA 110: Intro to Data Science

*and one of the following:*

- DATA 130: Critical Data Literacy
- SOCI 318: Computational Sociology
- INLS 201: Foundations of Information Science
- ENGL 480: Digital Humanities History and Methods.
- ENGL 482: Metadata, Mark-up, and Mapping: Understanding the Rhetoric of Digital Humanities

*Responsible Data Science*

DATA 120: Ethics of Data Science and Artificial Intelligence

*Communications:
One of the following:*

- DATA 150: Communication for Data Scientists
- COMM 113: Public Speaking
- COMM 171: Argumentation and Debate
- MEJO 102: Future Vision: Exploring the Visual World
- GEOG 115: Maps: Geographic Information from Babylon to Google
- GEOG 415: Making Your Research Matter: Effective Design and Communication to Help Make an Impact on the World
- ENGL 119: Picture This: Principles of Visual Rhetoric
- ENGL 411: Writing for Clients: Technical Communication Practicum
- ENGL 303: Scientific and Technical Communication
- INLS 541: Information Visualization

*Mathematical and Statistical Foundations*

MATH 210: Mathematical Tools for Data Science

MATH 231: Calculus of Functions of One Variable I

*and one of the following:*

- STOR 320: Introduction to Data Science
- STOR 455: Methods of Data Analysis

*Computational Thinking*

DATA 140: Introduction to Data Structures and Management

*and one of the following:*

- COMP 110: Introduction to Programming and Data Science
- COMP 116: Introduction to Scientific Programming
- STOR 120: Foundations of Statistics and Data Science

B.A. students are required to complete a four-course concentration.

For more information about the B.A. in Data Science, please contact:

dsCAS@unc.edu and visit: https://datascience-college.unc.edu/

**DATA 110. Introduction to Data Science. 3 Credits.**

This course is a broad, high-level survey of the major aspects of data science including ethics, best practices in communication (e.g. data visualization), mathematical/statistical concepts, and computational thinking. Students will gain an understanding of the fundamentals of data science to support more in-depth, advanced coursework that are requirements for the data science majors. **Grading Status:** Letter grade.

**DATA 120. Ethics of Data Science and Artificial Intelligence. 3 Credits.**

In an era of rapid advancements in data science and AI, ethical concerns related to data-intensive technologies are now of utmost importance. This course immerses students in data science ethics, facilitating a comprehensive exploration of the intricate interplay between data and societal values. By nurturing critical thinking grounded in ethical theories, this course provides students with a strong foundation in designing and analyzing data-intensive ecosystems that emphasize values such as fairness, accountability, ethics, and transparency. **Grading Status:** Letter grade.

**DATA 130. Data Literacy Foundations. 3 Credits.**

How do you become data literate? Data literacy is the ability to read, write, and communicate data in context, or in other words: perform data analysis, construct a data visualization, and then communicate that data. It is the story that gets told with the data. Data literacy helps us to understand data, learn about different types and scales of data, and understand why this is important in the world today. **Grading Status:** Letter grade.

**DATA 140. Introduction to Data Structures and Management. 3 Credits.**

Data structures provide a means to manage large amounts of data for use in our databases and indexing services. A data structure is a specialized format for organizing, processing, retrieving and storing data. There are several basic and advanced types of data structures, all designed to arrange data to suit a specific purpose. Data structures make it easy for users to access and work with the data they need in appropriate ways. **Grading Status: **Letter grade.

**DATA 150. Communication for Data Scientists. 3 Credits.**

The ability to collect and analyze data has changed virtually every field, yet data scientists often lack the ability to present their findings in effective formats. This class uses storytelling to help you connect with your audience and present your data in compelling and understandable ways so stakeholders can make the right decisions with data. Through hands-on exercises, you’ll learn the advantages and disadvantages of oral, visual, and written formats. **Grading Status: **Letter grade.