Minor in Data Science (Non-Teaching)
Program Learning Outcomes
- Articulate key elements of the data science cycle (e.g. data acquisition, data storage, data cleaning, data exploration, data visualization, data modeling and data communications).
- Use computational thinking skills to design and implement a program that solves a non-trivial data science problem.
- Use a data visualization programming language library effectively to produce meaningful data visualizations. ;
- Utilize basic mathematical (e.g. linear algebra) and statistical (e.g. statistical models and/or statistical tests) concepts to understand or model data.
| CSCI 127 | Joy and Beauty of Data | 4 |
| CSCI 132 | Basic Data Structures and Algorithms | 4 |
| CSCI 232 | Data Structures and Algorithms | 4 |
| CSCI 246 | Discrete Structures | 3 |
| or M 221 | Introduction to Linear Algebra | |
| or M 242 | Methods of Proof | |
| STAT 216Q | Introduction to Statistics | 3 |
| STAT 337 | Intermediate Statistics with Introduction to Statistical Computing | 3 |
| Choose 3 courses from the following (at least one Computer Science and one Math/Stat course): | 9 | |
| Introduction to Data Science | ||
| Data Mining | ||
| Advanced Algorithm Topics | ||
| Database Systems | ||
| Data Visualization | ||
| Machine Learning | ||
| Computational Biology | ||
| Software Applications in Mathematics | ||
| Numerical Linear Algebra & Optimization | ||
| Statistical Computing and Graphical Analysis | ||
| Methods for Data Analysis I | ||
| Methods for Data Analysis II | ||
| Biostatistical Data Analysis | ||
| Introduction to Categorical Data Analysis | ||
| Experimental Design | ||
| Sampling | ||
| Total Credits | 30 | |
Note 1: Additional relevant, upper-division courses will be added as options as they become available.
Note 2: 490R (Undergraduate Research), 491 (Special Topics), 492 (Independent Study) or 494 (Seminar) credits related to data science also count. These credits must be applied via Degree Works Exceptions.