Data Science

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.

353 – Analytics II: Optimization Models (3)

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 problems in linear programming, inventory, waiting lines, PERT/CPM, and simulation.Cross listed as DSCI 353.

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 – Applied Machine Learning (3)

Prerequisite: Grade of C or better in CPSC 220 or DSCI/CPSC 219 or equivalent.  This course provides an introduction to modern machine learning methods with an emphasis on application.  Traditional algorithms for classification, clustering, and regression are covered as well as model development and performance evaluation.  Select deep learning algorithms, including convolutional and LSTM networks are also covered. Examples will come from customer behavior modeling, text and image classification, and other interesting domains. Cross-listed as DSCI 401

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.

491 – Individual Study in Data Science (1-4)

Prerequisite: CPSC 219 or DATA 219 or DSCI 219 or permission of Program Coordinator.  Individual study in Data Science under the direction of a faculty member in an affiliated department.