Focusing on the theoretical, mathematical and computational foundations of modern data science, our Data Science minor prepares you with the understanding of how to interpret and manipulate data.
The field of analytics and data science impacts nearly all aspects of the economy, society and daily life. This highly interdisciplinary minor emphasizes mathematics and computer science skills—skills that are in high-demand in industries from finance to healthcare to marketing and more.
What is data science?
With an explosion of big data initiatives in organizations worldwide, the demand for data-savvy individuals has never been higher. This program is designed to provide a basic foundation for students interested in the theoretical underpinnings of analytics and data science. Choose from courses in artificial intelligence, neural networks, data mining and big data. Data science is being applied in many organizations within industry, academy and government, and the job demand reflects this growth. With the experience provided by this minor, you’ll gain a competitive advantage in this rapidly growing field.
Why study data science at UNH?
UNH was one of the first universities in the country to offer an undergraduate-level degree in analytics and data science. Our programs take a multidisciplinary approach that incorporates experiential education and projects. You’ll learn to manage, distill and interpret data for industries from finance to healthcare to marketing and advertising. The data science minor is available at the Durham and Manchester campuses.
Potential careers
- Actuary
- Business analyst
- Consultant
- Data engineer
- Data scientist
- Management analyst
- Market research analyst
- Statistician
- Quantitative analyst
Explore Program Details
Explore Program Details
Curriculum & Requirements
Students must complete five courses (20 credits) with a cumulative minimum grade point average of 2.0 and with no grade below a C- grade. Transfer course approval for the minor is limited to at most, two relevant courses successfully completed at another accredited institution, subject to syllabi review and approval. and programming (COMP 424 Applied Computing 1: Foundations of Programming , CS #414 From Problems to Algorithms to Programs or CS 415 Introduction to Computer Science I) is required.
| Code | Title | Credits |
|---|---|---|
| CS 515 | Data Structures and Introduction to Algorithms | 4 |
| Select one of the following: | 4 | |
COMP 525 | Data Structures Fundamentals | |
CS 416 | Introduction to Computer Science II | |
| Select three of the following: 1 | 12 | |
CS 730 | Introduction to Artificial Intelligence | |
CS 750 | Machine Learning | |
CS 753 | Information Retrieval | |
CS 775 | Database Systems | |
MATH 645 | Linear Algebra for Applications | |
MATH 736 | Advanced Statistical Modeling | |
MATH 738 | Data Mining and Predictive Analytics | |
MATH 739 | Applied Regression Analysis | |
DATA #750 | Neural Networks | |
DATA 757 | Mining Massive Datasets | |
| Total Credits | 20 | |
- 1
Must select at least one CS and one MATH course. Must select CS 750 Machine Learning or MATH 738 Data Mining and Predictive Analytics.