Dec 02, 2021  
2020 - 2021 Undergraduate Catalog 
    
2020 - 2021 Undergraduate Catalog [ARCHIVED CATALOG]

Data Science, BS


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Required Credits: 40-41


Capstone Courses: 3-4 Credits


Students must select one of following courses (also fulfills COLL 400 requirement). 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.

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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