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Nov 21, 2024
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2024 - 2025 Undergraduate Catalog
Data Science (BS in Data Science)
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 Return to: Majors & Minors
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Capstone Courses: 3-4 Credits
Students must select one course from 400-level data science courses excluding DATA 491. Capstone courses do not count toward credits needed to fulfill Track Area requirements.
Mathematics Courses: 9 Credits
Track Areas: 9 Credits
Students are required to select a track at the time of major declaration. A track is constituted of three additional methods-oriented courses. Courses selected to fulfill Track Area requirement do not count toward credits needed to fulfill the Capstone requirement.
Artificial Intelligence
The Artificial Intelligence (AI) track is designed to equip students with the knowledge and skills necessary to develop intelligent systems that can simulate human thought processes, learn from data, and make informed decisions. This track focuses on teaching students how to design, build, and implement AI algorithms and models that can analyze complex data sets, recognize patterns, and predict outcomes with high accuracy. Coursework will cover a broad spectrum of AI techniques, including deep learning, generative AI, reinforcement learning, and a range of aligned topics. Students will gain hands-on experience in developing algorithms that can learn from data, adapt to new information, and improve over time without human intervention. This track will prepare graduates for careers in both the public and private sectors, where they can innovate and drive progress in AI technology, formulating and answering novel questions that leverage the power of artificial intelligence.
Data Application:
The purpose of this track is to prepare students for positions in which they will conduct predictive analyses using large, potentially near real-time data sets from a wide range of sensors and sources. The coursework will allow students to build data pipelines to ingest large quantities of data into computational environments quickly and efficiently, integrate these data into common frames of reference, process the data using statistical and computational modeling techniques, and update models dynamically based on real-time information. Students will be well trained for entry level jobs in government or private industry in which they formulate novel questions that can be explored with big data.
Algorithms:
The purpose of this track is to prepare students for positions in which they support the development of new software or algorithms for the ingestion or analysis of large sources of frequently near-real-time data. It provides students with a depth of knowledge on computational efficiency, and teaches the basic theory of how computational bottlenecks might be overcome.
Spatial Data Analytics:
The purpose of this track is to prepare students for positions that require the large-scale analysis of data with a geospatial component, including both satellite and survey information. Students will be exposed to novel modeling techniques that incorporate spatial dependencies, data warehousing and processing techniques unique to spatial data, and techniques for the visualization of spatial data sources. Coursework will train students for positions in which they use Geographic Information Systems (GIS) software packages and spatial data to formulate and answer questions.
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Return to: Majors & Minors
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