Data Science (DATA)
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.
Prerequisite: STAT 180 or equivalent. This course will introduce students to data visualization methods as well as essential predictive modeling approaches widely used in analytics practice today. Beginning with a foundation in inferential statistics, the course will cover regression, classification, time series, and clustering models. The use of visualization both to explore data and to create narratives around data will also be emphasized.
Prerequisite: DSCI 352, MIST 201 or equivalent and STAT 180 or similar statistics course. This course introduces a variety of Management Science models for use in analysis of “business” problems. A computer software package provides the computational basics for case analysis of problem in linear programming, inventory, waiting lines, PERT/CPM and simulation. Cross-listed with DSCI 353.
Prerequisite: Specified by Instructor. Treatment of selected topics in Data Science. May be repeated for credit with a change in topic.
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.
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. Course previously taught as BUAD 400. Cross-listed as DSCI 402.
Prerequisites: DATA 219, CPSC 219, DSCI 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, gain experience with these techniques using open source software, and learn how to apply them to solve real-world problems. Topics include decision trees, Naïve Bayes, probabilistic retrieval models, clustering, support vector machines, approaches to web mining, and scalable machine learning applications. Cross-listed as CPSC 419.
Prerequisite: DATA 219, CPSC 219, DSCI 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.