Data Science Course Offerings
Data Science course offerings will be found under the 4 letter code of DATA in the course listings.
101 – Introduction to Data Science (3)
A hands-on introduction to the field of Data Science and its applications. Covers a wide range of topics to provide an overview of the use of data in different fields. Provides hands-on practice with basic tools and methods of data analysis. Prepares students to use data in their field of study and in their work and to effectively communicate quantitative findings. Cross-listed as DSCI 101.
219 – Foundations for Data Science (3)
Prerequisite: DATA 101. Skills and tools in acquiring, parsing, manipulating, and preparing data for statistical analysis. Cross-listed as CPSC 219 and DSCI 219.
370 – Special Topics in Data Science (3)
Prerequisite: Specified by Instructor. Treatment of selected topics in Data Science. May be repeated for credit with a change in topic.
401 – Foundation and Applications of Data Analytics (3)
Prerequisite: Grade of C or better in CPSC 220 or DSCI/CPSC 219 or equivalent. This course develops an overview of the challenges of developing and applying analytics for insight and decision making. Examples and cases will come from customer relation management, price modeling, social media analytics, location analysis and other business domains. Cross-listed as DSCI 401. Course previously taught as BUAD 403.
402 – Analytics Applications and Development (4)
Prerequisite: Grade of C or better in CPSC 220 or DSCI/CPSC 219 or equivalent. A course in programming and data manipulation techniques for constructing analytics-based applications. Topics include SQL or no-SQL databases, using web service API’s to acquire data, introduction to Hadoop and MapReduce, and use of third-party analytic component API’s. Cross-listed as DSCI 402. Course previously taught as BUAD 400.
419 – Data Mining (3)
Prerequisite: DATA 219, DSCI 219, CPSC 219, or CPSC 220. Practical knowledge of data mining, machine learning, and information retrieval. Students will examine the theoretical foundations of a variety of techniques, gaining experience with these techniques using open source software, and learn how to apply them to solve real-world problems. Topics include decision trees, Naive Bayes, probabilistic retrieval models, clustering, support vector machines, approaches to web mining, and scalable machine learning applications. Cross-listed as CPSC 419.
420 – Modeling and Simulation (3)
Prerequisite: DATA 219, DSCI 219, CPSC 219, or CPSC 220. A robust introduction to techniques of mathematical modeling and computational simulation applied to practical problems. Topics include system dynamics approaches, discrete-event simulation, and agent-based models. Students complete small projects on topics as diverse as population growth, epidemic transmission, queuing theory, and forest fire outbreaks. Cross-listed as CPSC 420.