Decision Sciences (DS)

DS 520     Applied Statistical Modeling     3 Credit Hours

This course explores statistical modeling and analysis techniques for aiding managerial decision making. Topics include: introduction to descriptive statistics, sampling methods and sampling distribution, confidence interval estimation, one sample hypothesis tests, one-way and two-way analysis of variance, simple and multiple linear and nonlinear regressions, and time series forecasting. Selected software packages are used in exercises, projects, and business case examples.

Prerequisite(s): Mathematics Placement with a score of 115 or MATH 104 or MATH 105 or MATH 113 or MATH 115

Restriction(s):
Can enroll if Class is Graduate

DS 570     Management Science     3 Credit Hours

To develop basic competence in introductory management science and operations research. Topics include: problem formulation and model development in optimization, linear programming (LP), duality theory, economic interpretation, and sensitivity analysis, introduction to integer programming (IP), special linear programs, network modeling, and introduction to non-linear programming (NLP). Selected software packages are used in laboratory exercises and in optimization project.

Prerequisite(s): Mathematics Placement with a score of 115 or MATH 104 or MATH 105 or MATH 113 or MATH 115

Restriction(s):
Can enroll if Class is Graduate

DS 630     Applied Forecasting     3 Credit Hours

This course explores various quantitative modeling methods used in forecasting. Topics include: moving averages, various smoothing techniques, trend- and seasonal forecasting, univariate- and multivariate regression based time series analysis (ARMA, ARIMA). Selected software packages are used in laboratory exercises and in an applied forecasting project.

Prerequisite(s): (DS 520 or IMSE 510 or IMSE 514 or STAT 530 or STAT 535 or STAT 555 or STAT 560) and (Mathematics Placement with a score of 115 or MATH 104 or MATH 105 or MATH 113 or MATH 115)

DS 631     Decision Analysis     3 Credit Hours

This course entails study of analytic techniques for rational decision making that address uncertainty, conflicting objectives, and risk attitudes. Topics covered in the course include modeling uncertainty, rational decision making principles, representing decision problems with value trees, decision trees and influence diagrams; solving value hierarchies, decision trees and influence diagrams; defining and calculating the value of information, incorporating risk attitudes into the analysis and conducting sensitivity analysis.

Prerequisite(s): (DS 520 or IMSE 510 or IMSE 514 or STAT 530 or STAT 535 or STAT 555 or STAT 560) and (Mathematics Placement with a score of 115 or MATH 104 or MATH 105 or MATH 113 or MATH 115)

Restriction(s):
Can enroll if Class is Graduate

DS 632     System Simulation     3 Credit Hours

In this course students will learn how to design, model, and implement discrete-event computer simulation models of real or conceptual systems. Simulation studies will be conducted using contemporary software such ProModel. Student will learn random number generation, applying distribution sampling, and conducting output analysis.

Prerequisite(s): (DS 520 or IMSE 510 or IMSE 514 or STAT 530 or STAT 535 or STAT 555 or STAT 560) and (Mathematics Placement with a score of C or MATH 104 or MATH 105 or MATH 113 or MATH 115)

Restriction(s):
Can enroll if Class is Graduate

DS 633     Data Mining for Business Appl     3 Credit Hours

The purpose of this course is to provide students with both quantitative and qualitative exposure to the field of Data Mining, a topic of immense importance and relevant to the study of Business Analytics. Data Mining is the process of discovering meaningful correlations, patterns and trends in large data sets and employs statistical and mathematical techniques. Students will be exposed to theory, computation, tools & techniques to analyz repositories of data from a vast arry of business applications with a view to implement successful business strategies aimed at improved decision-making. The course contents are representative of three primary areas of analytics- prescriptive, predictive, and descriptive that define the core of studies offered in our Masters of Science in Business Analytics program. Selected software packages are used in exercises to solve data mining problems. (F) (W)

Prerequisite(s): (DS 520 or IMSE 510 or IMSE 514 or STAT 530 or STAT 535 or STAT 555 or STAT 560) and DS 570 and (Mathematics Placement with a score of 115 or MATH 104 or MATH 105 or MATH 113 or MATH 115)

Restriction(s):
Can enroll if Class is Graduate

DS 635     Analytics Experience Capstone     3 Credit Hours

The purpose of this course is to provide students with an experience that allows them to demonstrate application of integrative knowledge aimed at addressing an industry relevant decision-making problem by drawing on the breadth and depth of the Business Analytics programmatic curriculum. The plan of studies requires that the student complete this course under the direction and guidance of the Instructor who may enroll the services of an industry expert for advice. Depending on the size and complexity of the problem, one or many students may be assigned to the project. The deliverables for the course are a detailed project report describing evaluation and analysis of the problem to be presented at a public setting. The course can be finished in one or two semesters. (F) (F, W).

Prerequisite(s): (DS 520 or IMSE 510 or IMSE 514 or STAT 530 or STAT 535 or STAT 555 or STAT 560) and DS 570 and DS 630 and DS 631* and DS 632* and (Mathematics Placement with a score of 115 or MATH 104 or MATH 105 or MATH 113 or MATH 115)

Restriction(s):
Can enroll if Class is Graduate
Can enroll if Program is