Jan 18, 2022  
2021-2022 Catalog 
2021-2022 Catalog

Data Science & Analytics - Major

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The part-time Bachelor of Science in Data Science and Analytics program will provide students with the foundational education and tools to collect, manage, and analyze data. Students will develop proficiency in areas like statistics, data visualization, computer science, and data collection that are vital to a multitude of fields, from business and gaming to medicine and public health. Further, students will learn to ask research questions that generate data for actionable insights.

  • Students may transfer into the program at any point but must meet the program requirements to be eligible for graduation. Students are required to complete all other University requirements for graduation. 
  • All students enrolled in the BS in Data Science and Analytics program must complete a minimum of 30 credits at the University in order to graduate. A minimum of 120 approved credits is required to earn a bachelor’s degree. 
  • All USciences undergraduate students enrolled in a degree granting program must complete the general education curriculum.  A description of the General Education  curriculum may be found elsewhere in this catalog. 

General Education (41 credits)

Required Courses in the Major (45 credits)

  • DA 305 Visualization Strategies for Data Analysis: Credits: 3 
  • DA 306 Techniques for Business Data Analytics Credits: 3
  • DA 307 Introduction to Database Design Credits: 3 
  • DA 310 Use of Big Data Credits: 3
  • DA 313 Programming with Python Credits: 3
  • DA 402 Methods of Regression Analysis Credits: 3 
  • DA 403 Fundamentals of Experiment Design and Analysis Credits: 3 
  • DA 405 Data Mining Credits: 3 
  • DA 406 Data Governance Credits: 3 
  • DA 407 Advanced Statistics/Statistical Modeling Credits: 3 
  • DA 408 Modeling and Predictive Analysis Credits: 3
  • DA 450 Data Science Capstone Credits: 3* 

Elective Courses in the Major (15 credits)

  • DA 311 SAS Programming and Data Analysis Credits: 3
  • DA 314 Introduction to Survival Analysis Credits: 3
  • DA 315 Multivariate Data Analysis Credits: 3
  • DA 316 Introduction to Categorical Data Analysis Credits: 3
  • DA 317 Applied Mathematical Modeling Credits: 3 
  • DA 318 Forecasting with Time Series Data Credits: 3
  • DA 319 Introduction to Stochastic Modeling Credits: 3
  • DA 320 Applied Numerical Analysis Credits: 3 
  • DA 321 Applications in Information Security Credits: 3
  • DA 410 Artificial Intelligence Credits: 3
  • DA 412 Advanced SAS Programming Credits: 3
  • DA 413 Statistical Methods for Clinical Trials Credits: 3
  • DA 414 Introduction to Applied Machine Learning Credits: 3 

Free Electives (19 credits)

Total Minimum Credits: 120


*Must be taken as part of the last 9 credits in the program

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