Decision Sciences (DS)

DS 300     Quantitative Model and Anlys I     3 Credit Hours

To introduce fundamental concepts and methods in data analysis, probability, estimation, and statistical inference for application in management and management science. Topics include: basic probability theory, discrete and continuous random variables and distributions, sampling and data analysis, sampling distributions, estimation, confidence intervals and hypothesis testing, introductory regression analysis and utilization of statistical software packages.

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

Can enroll if Class is Sophomore or Junior or Senior

DS 301     Intro Business Statistics     3 Credit Hours

Introductory concepts and methods in data analysis and probability, together with their applications to business. Students will be introduced to the use of Excel@ to analyze data and communicate data to a business audience through statistical reports. Topics covered are data generation and categorization; visualizing data; numerical descriptive measures; basic probability; random variables (discrete and continuous); and an introduction to sampling methods and sampling distributions. (F,W,S)

Prerequisite(s): (MATH 104 or MATH 105 or MATH 113 or MATH 115 or MPLS with a score of 115) and ITM 120

Cannot enroll if Class is Freshman

DS 302     Advanced Business Statistics     3 Credit Hours

Full Title: Advanced Business Statistics using Excel The continuance of DS301: an introduction to the use of estimation and statistical inference in data analysis using Excel and other appropriate statistical packages, with applications to business. Statistical report writing for a business audience will be emphasized. Topics covered are sampling distributions; confidence interval estimation; hypothesis testing (one-sample tests, two-sample tests, Chi-square test, and analysis of variance); and regression models. (F,W,S)

Prerequisite(s): DS 301

DS 310     Data Mining for Bus Intel     3 Credit Hours

Data Mining offers a suite of analytical techniques to examine large sets of data in order to discover, diagnose, and identify new and valuable information to aid the decision-making process. This course is designed to introduce the core concepts of data mining, its techniques, implementation, benefits and outcomes from this technology. Examples from industries such as, retail, marketing, fraud protection, personal security, health care, web and e-commerce will be presented throughout the course to emphasize usage and application of data mining. Among data mining techniques to be discussed in the course are k-means clustering, principal component analysis, factor analysis, linear and logistical regression, neural networks, decision trees, text and web mining. The class format consists of discussion of published articles/cases, presentations by business professionals, class lectures and discussions, and hands-on work with popular data mining software. (F)

Prerequisite(s): DS 300 or STAT 325

DS 350     Quantitative Model and Anly II     3 Credit Hours

To continue from DS 300, during the first half of the course, the study of the concepts and methods in data analysis and statistical inference, as well as to introduce, in the second half of the course, basic linear optimization methods and models applied in the formulation, quantification, analysis, and solution of management decision problems. Topics include: simple and multiple linear regression, analysis of variance, sampling, correlation, formulation and solution of linear programming problems, transportation and transshipment models, utilization of software packages for statistical analysis and optimization.

Prerequisite(s): DS 300

DS 425     Optimization Modeling and Anly     3 Credit Hours

To continue, from DS 350, the study of optimization methods and models applied in the formulation, quantification, analysis and solution of management decision problems. Topics include: network analysis (including PERT-CPM), goal and multi-objective linear programming, integer programming, dynamic programming, Markovian decision processes, nonlinear programming.

Prerequisite(s): DS 350

DS 426     Introduction to Simulation     3 Credit Hours

To introduce the concepts and methods of discrete-event simulation for the modeling and analysis of complex systems. Topics include: basic simulation modeling, modeling complex systems, simulation languages, selection of input probability distributions, random-number generators, generating random variable values, output data analysis for a single system, statistical techniques for comparing alternative systems, validation of simulation models, variance-reduction techniques, experimental design and optimization.

Prerequisite(s): DS 350

DS 489     Seminar: Decision Sciences     1 to 3 Credit Hours

To provide students with an opportunity for intensive study in current selected areas related to the research activities and/or professional activities of faculty members. Permission of College of Business.

Can enroll if Class is Senior
Can enroll if College is Business

DS 499     Research: Decision Sciences     1 to 3 Credit Hours

To provide the advanced student with the opportunity to undertake a research project under the supervision of a faculty member. At least two weeks prior to registration in the term when such a course is to be elected, an interested student must submit to the dean of the school a written request for permission to elect a research course, on a form available from the school office. The dean will review the proposal with faculty members to ascertain availability of relevant faculty supervision and to establish appropriate credit.

Can enroll if Class is Senior


An asterisk denotes that a course may be taken concurrently.

Frequency of Offering

The following abbreviations are used to denote the frequency of offering: (F) fall term; (W) winter term; (S) summer term; (F, W) fall and winter terms; (YR) once a year; (AY) alternating years; (OC) offered occasionally