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Course of Study

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. Concentrations still under development.

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.

 

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.