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. Cross-listed with DATA 352.

Prerequisite: DSCI 352, MIST 201 or equivalent and STAT 180 or similar statistics course; and College of Business major or Data Science minor or permission of the Associate Dean for Faculty. This course introduces a variety of Management Science models for use in analysis of “busi ness” 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 with DATA 353.

Prerequisite: DSCI 353 or equivalent; and business administration major or permission of the Associate Dean for Faculty. Operations management is an area of business concerned with the production of goods and services. It involves the study of concepts, theories and techniques relating to the operations functions in both manufacturing and service organizations. Lectures, discussions, and case studies are used to provide a comprehensive knowledge of the theories, current practices, and trends in several topical areas of operations management. Quantitative tools of analysis used to support decision making in the various operations management are surveyed.

Prerequisite: Grade of C or better in CPSC 220 or CPSC 219 or equivalent; and College of Business major or Data Science minor or permission of the Associate Dean for Faculty. 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.

Prerequisite: Grade of C or better in CPSC 220 or 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 with DATA 402.