Data Science Minor
The Data Science Minor provides students with the skills and theory necessary to analyze and derive insights from large data sets. The curriculum includes techniques from statistics, mathematics and computer science. It encompasses academic topics including descriptive and inferential statistics, machine learning, cluster analysis, data mining, and data visualization.
Goal 1: Students will learn basic statistical and computing skills and be prepared to enter the data science profession.
Outcome 1.1: Students will be able to perform standard data science tasks such as data wrangling, data visualization, statistical modeling, and the application of machine learning models.
Outcome 1.2: Students will be able to write computer programs to solve a problem or perform a task needed for data science.
Outcome 1.3: Students will be able to communicate, orally and in writing, the results of technical data analysis to both specialists and non-specialists.
| Code | Title | Hours |
|---|---|---|
| Six courses are required: | ||
| Three Core Courses: | ||
| CSC 115 | Intro to Computer Science | 3 |
| or CSC 133 | Python Programming for All | |
| DSC 223 | Intro Math of Data Science | 3 |
| DSC 325 | Essentials of Data Science | 3 |
| or CSC 346 | Introduction to Data Science | |
| Three Elective Courses (select from the list below; at least one must be a DSC, CSC or MAT course): | 9 | |
| Data Science for Sports | ||
| Advanced Data Science | ||
or CSC 347 | Advanced Data Science | |
| Regression and Time Series | ||
| Machine Learning/Data Science | ||
| Numerical Analysis | ||
| Mathematical Optimization | ||
| Operations Research | ||
| Mathematical Statistics | ||
| Design of Experiments | ||
| Convex Analysis & Optimization | ||
| Applied Statistical Methods | ||
| Bioinformatics and Bioinformatics Lab | ||
| Artificial Intellig for All | ||
or CSC 362 | Artificial Intelligence | |
| Databases for All | ||
or CSC 351 | Database Management Systems | |
| Generative AI | ||
| Computer Vision | ||
| Image Data Science | ||
| Advanced Machine Learning | ||
| Internet Application Develpmnt | ||
| Big Data and Web Intlgce | ||
| Artificial Intelligence | ||
| Econometrics | ||
| Economic Forecasting | ||
| Research Methods | ||
| Data Wrangling & Visualization | ||
| Data Wrangling: Ethics Int. | ||
| Introduction to Data Mining | ||
| Advanced Business Analytics | ||
| Statistical Programming Lang | ||
| Machine Learning for Bus I | ||
| Machine Learning for Bus II | ||
| Special Topics | ||
| Intro. to Network Science | ||
Any internship course (in any department) that is pre-approved as having sufficient data science content. | ||
| Total Hours | 18 | |