Data Science (BS in Data Science)

Required Credits Hours:

The curriculum for the Bachelors of Science in Data Science requires 36 credit hours to complete. A minimum of 120 credits are required to fulfill all degree requirements; information on other degree requirements can be found in the catalogue section “Requirements for Degrees ”.

Core Courses: 18 Credits

DATA 101 - Reasoning with Generative AI Credits: (3)
or CSCI 141 - Introduction to Programming
DATA 201 - Introduction to Data Science Credits: (3)
DATA 202 - Ethics in Data Science Credits: (3)
DATA 301 - Applied Machine Learning Credits: (3)
DATA 302 - Databases Credits: (3)
DATA 303 - Data Visualization Credits: (3)

Capstone Courses: 3 Credits

Students must select one 400 level DATA course to fulfill the capstone requirement. DATA 491 and 495 may not count towards this requirement. For students successfully completing an honors project, DATA 496 may be used to fulfill this requirement. Capstone courses do not count toward credits needed to fulfill Track Area requirements.

Mathematics Courses: 6 Credits

DATA 209 - Applied Linear Algebra & Calculus Credits: (3) or
MATH 211 - Linear Algebra Credits: (3) (prerequisites: MATH 111 and MATH 112 or MATH 131 and MATH 132 )
AND
DATA 210 - Research Design and Statistics Credits: (3) or
MATH 352 - Statistical Data Analysis Credits: (3) (prerequisites: MATH 351 )

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.

Students must select three of the following courses:
DATA 440 - Special Topics Credits: (1-4)
DATA 441 - Advanced Applications of AI Credits: (3)
DATA 442 - Neural Networks & Deep Learning Credits: (3)
DATA 446 - Generative AI Credits: (3)
DATA 447 - Bayesian Reasoning in Data Science Credits: (3)
DATA 448 - Reinforcement Learning Credits: (3)

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 DATA courses numbered 340 or higher which have a significant component of coursework dedicated to the analysis of datasets using Data Science techniques. Special topics courses (DATA 340 or 440) may be repeated as long as the topics are different. DATA 480, DATA 481 and 491 cannot be used to fulfill this requirement. For students pursuing honors projects, DATA 495 may count towards this requirement.

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 Remotely Sensed Imagery and Analysis Credits: (3)
GIS 420 - Advanced GIS Analysis & Programming Credits: (3)

Major Computing Requirements:

The departmental computer proficiency requirement is satisfied through the completion of course work demonstrating programming ability. This is typically satisfied by completion of DATA 101 or 201. Students may petition the Data Science Undergraduate Committee to satisfy the requirement by another course that demonstrates computational proficiency.

Major Writing Requirements:

The departmental writing requirement proficiency is satisfied through the completion of course work demonstrating written communications ability. This is typically satisfied by completion of DATA 301. Students may petition the Data Science Undergraduate Committee to satisfy the requirement by another course that demonstrates written communications ability.