Data Science, BS

Required Credits: 40-41

Core Courses: 19 Credits

DATA 141 - Programming for Data Science Credits: (4) or CSCI 140 - Computational Problem Solving in Data Science Credits: (4) or CSCI 141 - Computational Problem Solving Credits: (4)
DATA 146 - Introduction to Data Science Credits: (3)
DATA 202 - Ethics in Data Science Credits: (3) or PHIL 215 - Right and Wrong in the Contemporary World Credits: (3) or PHIL 303 - Ethics Credits: (3) or PHIL 330 - Ethics and Data Science Credits: (3)
DATA 211 - Data Visualization Credits: (3)
DATA 310 - Applied Machine Learning Credits: (3)
DATA 311 - Databases Credits: (3)

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.

DATA 410 - Advanced Applied Machine Learning Credits: (3)
DATA 431 - Spatial Data Discovery Credits: (3)
DATA 440 - Special Topics in Data Science Credits: (3-4) (selected topics fulfill COLL 400)
DATA 442 - Neural Networks & Deep Learning Credits: (3)
DATA 444 - Agent-Based Modeling: Simulating Human Development Processes from Neighborhood to Regional Scales Credits: 3
DATA 490 - Independent Research in Data Science Credits: (3-4)
GOVT 403 - Legal Data in Comparative Context Credits: (4)

Mathematics Courses: 9 Credits

MATH 211 - Linear Algebra Credits: (3) (prerequisites: MATH 111 and MATH 112 or MATH 131 and MATH 132 )
MATH 351 - Probability and Statistics for Scientists Credits: (3) or MATH 451 - Probability Credits: (3)
MATH 352 - Statistical Data Analysis Credits: (3) or MATH 452 - Mathematical Statistics Credits: (3)

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.

Students must select three of the following courses:
DATA 330 - Applied Time Series Analysis Credits: (3)
DATA 340 - Topics in Data Science Credits: (3)
DATA 410 - Advanced Applied Machine Learning Credits: (3)
DATA 431 - Spatial Data Discovery Credits: (3)
DATA 440 - Special Topics in Data Science Credits: (3-4)
DATA 442 - Neural Networks & Deep Learning Credits: (3)
DATA 444 - Agent-Based Modeling: Simulating Human Development Processes from Neighborhood to Regional Scales Credits: 3

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.

CSCI 241 - Data Structures Credits: (3)
CSCI 243 - Discrete Structures of Computer Science Credits: (3) or MATH 214 - Foundations of Mathematics Credits: (3)
CSCI 303 - Algorithms Credits: (3)

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.

Students must select three of the following courses:
DATA 431 - Spatial Data Discovery Credits: (3)
GIS 201 - Introduction to Geographic Information Systems and Spatial Analysis Credits: (3)
GIS 405 - Geovisualization & Cartographic Design Credits: (3)
GIS 410 - Introduction to Remote Sensed Imagery and Analysis Credits: (3)
GIS 420 - Advanced GIS Analysis & Programming Credits: (3)