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 Ó£»¨¶¯Âþ?
Ó£»¨¶¯Âþ 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
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 (°ä°¿²Ñ±ÊÌý424 Applied Computing 1: Foundations of Programming or °ä³§Ìý415 Introduction to Computer Science I ) is required.
| Code | Title | Credits |
|---|---|---|
| Required Courses | ||
| °ä³§Ìý515 | Data Structures and Introduction to Algorithms | 4 |
| Select one course from the following: | 4 | |
°ä°¿²Ñ±ÊÌý525 | Data Structures Fundamentals | |
°ä³§Ìý416 | Introduction to Computer Science II | |
| Select three courses from the following: 1 | 12 | |
°ä³§Ìý730 | Introduction to Artificial Intelligence | |
°ä³§Ìý750 | Machine Learning | |
°ä³§Ìý753 | Information Retrieval and Generation Systems | |
°ä³§Ìý775 | Database Systems | |
²Ñ´¡°Õ±áÌý645 | Linear Algebra for Applications | |
²Ñ´¡°Õ±áÌý736 | Advanced Statistical Modeling | |
²Ñ´¡°Õ±áÌý738 | Data Mining and Predictive Analytics | |
²Ñ´¡°Õ±áÌý739 | Applied Regression Analysis | |
¶Ù´¡°Õ´¡Ìý750 | Neural Networks | |
¶Ù´¡°Õ´¡Ìý757 | Mining Massive Datasets | |
| Total Credits | 20 | |
- 1
Must select at least one CS and one MATH course. Must select °ä³§Ìý750 Machine Learning or ²Ñ´¡°Õ±áÌý738 Data Mining and Predictive Analytics.