Academic Catalog

Decision Sciences (DSCI)

DSCI 101  - Introduction to Data Science  (3 Credits)  

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 DATA 101.

DSCI 219  - Foundations for Data Science  (3 Credits)  

Prerequisite: DATA 101. Skills and tools in acquiring, parsing, manipulating, and preparing data for statistical analysis. Cross-listed as CPSC 219 and DATA 219.

DSCI 259  - Applied Statistics and Business Research  (3 Credits)  

Prerequisite: STAT 180 or similar statistics course. This course introduces students to the scientific method to facilitate their understanding of what constitutes good and bad research and enable them to design and conduct research studies. In addition, the course provides students with skills necessary to analyze, synthesize and evaluate statistical information in order to make informed and appropriate decisions in the workplace and to prepare students for research courses in graduate school. Students may elect to conduct the group project on an individual basis to also complete the university's learning requirement.

DSCI 352  - Analytics I: Predictive Models  (3 Credits)  

Prerequisite: STAT 180 or equivalent; and College of Business major or Data Science minor or permission of the Associate Dean for Faculty. 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.

DSCI 353  - Analytics II: Optimization Models  (3 Credits)  

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 as DATA 353.

DSCI 363  - Operations Management  (3 Credits)  

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.

DSCI 401  - Applied Machine Learning  (3 Credits)  

Prerequisite: Grade of C or better in CPSC 220 or DSCI 219 / 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.

DSCI 402  - Analytics Applications and Development  (4 Credits)  

Prerequisite: Grade of C or better in CPSC 220 or DSCI 219 / 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 DATA 402. Course previously taught as BUAD 400.