Data Science Minor
https://www.unh.edu/program/minor/data-science
The objective of this minor is to provide a basic background in data science for those who are more interested in the theoretical underpinnings of analytics and data science.
Students interested in the Data Science minor should contact matthew.magnusson@unh.edu for more information.
Academic policies related to Minors.
| Code | Title | Credits |
|---|---|---|
| Required Courses | ||
| CS 415 | Introduction to Computer Science I | 4 |
| or CS 410C | Introduction to Scientific Programming/C | |
| or CS 410P | Introduction to Scientific Programming/Python | |
| or DS 662 | Programming for Business | |
| Select one course from the following: | 4 | |
| Fundamentals of Statistical Learning I | ||
| Machine Learning | ||
| Data Mining and Predictive Analytics | ||
| Select one course from the following: | 4 | |
| Business Analytics and Statistics | ||
| Applied Biostatistics I | ||
| Introduction to Business Statistics | ||
| Statistical Discovery for Everyone | ||
| Introduction to Statistical Analysis | ||
| Statistics for Engineers and Scientists | ||
| Statistics in Psychology | ||
| Statistics | ||
| Select two courses from the following: | 8 | |
| Fundamentals of Statistical Learning I | ||
| Fundamentals of Statistical Learning II | ||
| Introduction to Artificial Intelligence | ||
| Mobile Robotics | ||
| Machine Learning | ||
| Reinforcement Learning | ||
| Information Retrieval and Generation Systems | ||
| Mathematical Optimization for Applications | ||
| Computer Vision | ||
| Natural Language Processing | ||
| Database Systems | ||
| Data Science for Knowledge Graphs and Text | ||
| Data Science and Scalable Data Systems | ||
| Financial Mathematics | ||
| Linear Algebra for Applications | ||
| Advanced Statistical Modeling | ||
| Statistical Methods for Quality Improvement and Design | ||
| Design of Experiments I | ||
| Survival Analysis | ||
| Time Series Analysis | ||
| Design of Experiments II | ||
| One-Dimensional Real Analysis | ||
| Real Analysis II | ||
| Foundations of Number Theory | ||
| Combinatorics | ||
| Topology | ||
| Data Mining and Predictive Analytics | ||
| Applied Regression Analysis | ||
| Total Credits | 20 | |