The minor in data science studies the collection, manipulation, storage, retrieval, and computational analysis of data in its various forms, including numeric, textual, image, and video data from small to large volumes.
Note: Khoury minors are only available to non-Khoury majors; students in Khoury-only or Khoury-combined degrees are not eligible for Khoury minors. A student may receive at most one Khoury minor, regardless of how many Khoury minors they qualify for.
Complete all courses listed below unless otherwise indicated. Also complete any corequisite labs, recitations, clinicals, or tools courses where specified.
Required Courses
| Code | Title | Hours |
|---|---|---|
| Computer Science Fundamental Courses | ||
| Complete one of the following options: | 9-10 | |
Fundamentals of Computer Science Option | ||
| Fundamentals of Computer Science 1 and Lab for CS 2500 | ||
| Fundamentals of Computer Science 2 and Lab for CS 2510 | ||
Programming with Data Option | ||
| Programming with Data and Data Science Programming Practicum | ||
| Intermediate Programming with Data and Lab for DS 2500 | ||
| Data Science Required Course | ||
| DS 3000 | Foundations of Data Science | 4 |
Data Science Electives
| Code | Title | Hours |
|---|---|---|
| Complete two of the following. Only one course from the Meaningful Minor list may contribute toward the minor requirements: | 8 | |
| Database Design | ||
DS 2010 to DS 4989 | ||
Meaningful Minor list (see below) | ||
Khoury Meaningful Minors
The concept of “Khoury Meaningful Minors” allows students the chance to personalize a computer science minor to meet individual academic needs and interests. Students may take one elective related to computation or information from a preapproved list of courses offered across the university rather than from within Khoury. This allows students to integrate the minor with a course in their own major or with a course in another area of interest. Students may of course choose to take all electives in the minor within Khoury if they wish.
| Code | Title | Hours |
|---|---|---|
| Arts, Media and Design | ||
| Information Design 1 | ||
| Information Design Studio 1: Principles | ||
| Information Design History | ||
| Research Methods for Design | ||
| Visualization Technologies 1: Fundamentals | ||
| Information Design Studio 2: Dynamic Mapping and Models | ||
| Information Design Studio 3: Synthesis | ||
| Game Design and Analysis | ||
| Data-Driven Player Modeling | ||
| Bouvé Health Sciences | ||
| Introduction to Health Informatics and Health Information Systems | ||
| Data Management in Healthcare | ||
| Personal Health Interface Design and Development | ||
| Evaluating Health Technologies | ||
| D'Amore-McKim—Business | ||
| Applied Financial Econometrics | ||
| Financial Data Analytics | ||
| Machine Learning in Finance | ||
| Advanced Financial Strategy | ||
| Fundamentals of Information Analytics | ||
| Data Management in the Enterprise | ||
| Information Visualization for Business | ||
| Data Mining for Business | ||
| Marketing Research | ||
| Marketing Analytics | ||
| Supply Chain and Operations Management | ||
| Computer and Information Science | ||
| Foundations of Information Assurance | ||
| Security Risk Management and Assessment | ||
| Engineering | ||
| Probability and Engineering Economy for Civil Engineering | ||
| Data Visualization | ||
| Introduction to Machine Learning and Pattern Recognition | ||
| Computer Vision | ||
| Data Mining for Engineering Applications | ||
| Science | ||
| Bioinformatics Computational Methods 1 | ||
| Bioinformatics Computational Methods 2 | ||
| Biostatistics | ||
| Linear Algebra | ||
| Differential Equations and Linear Algebra for Engineering | ||
| Probability and Statistics | ||
| Statistics and Stochastic Processes | ||
| Statistics in Psychological Research | ||
| Social Science and Humanities | ||
| Statistics | ||
| Applied Econometrics | ||
| Intermediate Selected Topics in Microeconomics | ||
| Quantitative Techniques | ||
GPA Requirement
2.000 GPA required in the minor