Please refer to the graduate catalog for detailed program information and course requirements.
Core Courses, 21 s.h.
DS 435G Applied Data Mining for Business Decision Making, 3 s.h.
This course provides an introduction to data mining methods for business applications. Students will learn the basics of data selection, preparation, statistical modeling, and analysis aimed at the identification of knowledge fulfilling organizational objectives.
DS 490G Statistical Software for Data Management and Decision Making, 3 s.h.
This course provides students with the basic concepts of statistical computing. Students will gain experience with statistical software packages, such as SAS or SPSS, and their applications. Methods of data preparation and validation, analysis, and reporting will be covered. This course can be repeated for a maximum of 6 s.h.
DS 510 (cross-listed with MATH 510) Foundations of Business Analytics, 3 s.h.
A survey of topics in calculus, applied linear algebra, probability and statistics useful for business decision making. The main objective is to lay the foundation required for advanced studies in applied statistics and business analytics.
DS 521 Data Visualization, 3 s.h.
This course focuses on the process and methods of visualizing information for the purpose of communicating actionable findings in a decision-making context. Hands-on experience with software for sourcing, organizing, analyzing, comprehending, reducing and visualizing data, resulting in a clear message.
DS 523 Management Science Techniques and Business Analytics, 3 s.h.
Applications of management science tools and techniques for effective decision making with emphasis on model building. Topics include PERT/CPM, transportation models, linear, goal, integer and dynamic programming, and queuing theory.
DS 560 Categorical Data Analysis Using Logistic Regression, 3 s.h.
This course covers the most commonly used statistical methods for analyzing categorical data. Topics include the use of exact methods, generalized estimating equations, and conditional logistic regression. The statistical package SAS and the freeware package R will be used.
DS 580 Business Analytics and Forecasting, 3 s.h.
This course introduces analytical models and tools used for continuous iterative exploration and investigation of past business performance to gain insight and drive decision. Predictive modeling, forecasting, and design of experiments will be covered.
Directed Electives, 6 s.h.
DS 485G Big Data for Business Decision Making, 3 s.h.
This course provides an introduction to big data analytics tools and methods for business applications. Topics include exploration, classification, dimension reduction, structured and unstructured data. Statistical software will be used to analyze business data.
DS 489G Seminar in Contextual Business Analytics, 3 s.h.
An industry, case study, focused course that explores topics relevant to applying business analytics models and theories to current corporate projects. Exact topics will change based on instructor expertise and market trends.
DS 490G Statistical Software for Data Management and Decision Making. (3, repeatable to 6 for different titles)
This course provides students with the basic concepts of statistical computing. Students will gain experience with statistical software packages, such as SAS, SPSS, or R and their applications. Methods of data preparation and validation, analysis, and reporting will be covered.
DS 500 Introduction to Business Analytics, 1 s.h.
Business analytics generally refer to the use of statistical and quantitative analysis for data-driven decision-making. This course introduces students to the foundations of business analytics problems and applications. Lectures will be supplemented with current business world examples.
DS 501 Independent Research, 1-3 s.h.
Independent research and study of selected topics in decision sciences.
DS 535 Advanced Data Mining for Business, 3 s.h.
This course furthers the study of data mining methods and techniques for business applications. Students will develop more advanced techniques for data preparation, information retrieval, statistical modeling and analysis aimed at the production of decision rules for specific business goals.
DS 540 Applied Stochastic Models in Business Analytics, 3 s.h.
This course introduces stochastic models for studying phenomena in management science, operations research, finance, actuarial science, and engineering. Heuristic minded approach aimed at developing “probabilistic thinking” is taken in the treatment of probability concepts, stochastic processes, model simulation, and applications.
DS 599 Decision Sciences Internship, 1-6 s.h., not repeatable
Integrates decision sciences theories with application to actual business practices. Students are exposed to a variety of positions within the business firm during the semester. All internships are supervised by a faculty coordinator and an executive in the business firm. Analytic reports of work accomplished by each student are presented to the coordinator. Graded S/U only.Only 3 s.h. counts toward the ASDA degree.
To have a Summer internship reviewed, please navigate to https://form.jotform.com/jllin/afed-internship-application to apply to have your internship approved. You will then be contacted by the AFED office, and they add it to your schedule if approved. Any academic integrity violations will automatically disqualify you from approval.
DS 605 Data Analytics Competition, 0 s.h.
Preparation for national/international team competitions in data analytics focused on specific complex case challenges. The course builds on existing technical and cognitive skills and develops the ability to conduct all stages in the data analytics process within team environments.
ECON 487G Econometrics, 3 s.h.
Extensions of the single equation regression model, estimation, and testing; multicollinearity, heteroskedasticity, and errors in variables; maximum likelihood estimation and binary response models; simultaneous equation models and estimation. Interpretation and application of econometric models and methods are emphasized.
CS 540 Computer Simulation, 3 s.h.
Statistical techniques used in computer simulations. Construction and verification of simulation models. Programming projects.
PSY 551 Structural Equation Modeling for the Behavioral Sciences
Structural equation modeling (SEM) and related analytical approaches employed in the behavioral sciences will be explored, with an emphasis on interpretation. Multiple regression and factor analysis will be reviewed. Hands-on training with contemporary SEM software will be provided.
STAT 471G Introduction to Mathematical Statistics I
The mathematical foundations of probability and statistics, principles of probability, sampling, distributions, moments, and hypothesis testing
STAT 478G Analysis of Variance
A study of analysis of variance and covariance with applications. Includes experimental design.
STAT 553 Applied Statistical Methods
Introduction to probability and statistics with a significant lean toward applications. Topics include probability, probability distributions, Central Limit Theorem, sampling distributions (t, F, Chi-Square), parameter estimation, hypothesis testing, nonparametric statistics, ANOVA, and linear regression.
Exit Options, 3 s.h.
- DS 601 Thesis
- DS 599 Internship and one additional elective
- ECON 507 Econometrics II OR DS 535 Advanced Data Mining for Business and one additional elective
Other Requirements, 0 s.h.
DS 602 Department Seminar 0 s.h., two semesters required
A survey of contemporary theoretical and applied statistics and analytics research
DS 604 Applied Statistics and Decision Analytics Assessment
All students in the Applied Statistics and Decision Analytics program are required to satisfactorily complete the assessment examination prior to graduation. This course also offers career preparation guidance and therefore should be taken during the student’s last semester on campus.
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