Graduate Studies

Applied Statistics and Decision Analytics

Admission | Courses | Program | Requirements | Integrated Degree | Profile

Director:  Jessica Lin
Graduate Committee Chairperson: Kasing Man
Graduate Advisor: Anna Valeva
Office: Stipes Hall 431
Telephone: (309) 298-1152
Location of Program Offering: Macomb, Quad Cities, Online

Graduate Faculty


  • Jessica Lin, Ph.D., Binghamton University
  • Kasing Man, Ph.D., University of Chicago

Associate Professors

  • J. Jobu Babin, Ph.D., University of Memphis
  • Tara Feld, Ph.D., University of South Carolina
  • Shankar Ghimire, Ph.D., Western Michigan University
  • Anna Valeva, Ph.D., University of California-Santa Barbara
  • Rong Zheng, Ph.D., University of Alabama

Assistant Professor

  • Haritima Chauhan, Ph.D., Northern Illinois University

Associate Graduate Faculty

Assistant Professors

  • Mohammed Chowdhury, Ph.D., George Washington University
  • Yevgeniy Ptukhin, Ph.D., Southern Illinois University

Learning Outcomes

For student learning outcomes, please see

 Program Description

The School of Accounting, Finance, Economics and Decision Sciences offers courses leading to the Master of Science degree in Applied Statistics and Decision Analytics. The MS in Applied Statistics and Decision Analytics is a multidisciplinary graduate degree program with a unique focus on applied statistics and decision analytics. This program is intended for graduates from undergraduate programs in the quantitative and biological sciences, mathematics, sociology, psychology, business, computer sciences, physics, engineering, and education, as well as working professionals desiring to sharpen their data analysis and analytical skills and learn advanced statistical methods. The 33 semester hour curriculum provides students with a firm foundation of statistical analysis and modeling commonly used in many fields, including education, science, technology, health care, government, business, or social science research. The graduates of the program will be trained on industry-standard software packages, such as Python, SAS, Tableau, and R, and gain modern analytical skills that are sought after in many fields, particularly in the areas of business and decision analytics or data analytics.

Building on the recommendations of the American Statistical Association (ASA)’s professional panel of experts (see, Amstat News, February 2013, “Preparing Master’s Statistics Students for Success: A Perspective from Recent Graduates and Employers,” graduates of our Master of Science in Applied Statistics and Decision Analytics degree program will be able to:

  1. apply advanced statistical methodologies, including a) descriptive statistics and graphical displays; b) probability models for uncertainty, stochastic processes, and distribution theory; c) hypothesis testing and confidence intervals; d) ANOVA and regression models (including linear, and multiple linear) and analysis of residuals from models and trends; and e) predictive modeling, forecasting, design of experiments, and stochastic models in applied statistics and decision analytics;
  2. derive and understand basic theory underlying these methodologies;
  3. formulate and model practical problems for solutions using these methodologies;
  4. produce relevant computer output using necessary and sufficient programming skills and standard statistical software (e.g., SAS, R, Python, etc.) and interpret the results appropriately;
  5. communicate statistical concepts and analytical results clearly and appropriately to others;
  6. understand theory, concepts, and terminology at a level that supports lifelong learning of related methodologies; and
  7. identify areas where ethical issues may arise in statistics.

Career Opportunities

The need for skilled data professionals is real and growing. According to a study by the McKinsey Global Institute, United States could face a shortage of as many as 190,000 workers with “deep analytical skills” by 2018. This program seeks to combine the course work of statistical decision making and analytic tools to meet the demand for skilled workers in the U.S. and Illinois job markets. With three Fortune 100 companies in the region—John Deere, Caterpillar, and State Farm—the degree program is designed to address strong regional needs and/or a shortage of graduates in the fields of applied statistics and decision analytics. Due to the shortage of skilled data and business analysts, the market demand is strong for graduates in this field. Companies hiring include Caterpillar, John Deere, Hewlett-Packard, Honeywell, Northrop Grumman, Boeing, American Medical Association, Chicago Board of Trade, U.S. Treasury, U.S. Comptroller of the Currency, Tennessee Department of Commerce, Principal Financial Group, Bank of America, Merrill Lynch, Exxon, Illinois Power, Newsweek, and Walmart.

STEM Designation

The Applied Statistics and Decision Analytics degree program at Western Illinois University has been designated by the U.S. Immigration and Customs Enforcement agency within the Department of Homeland Security as a STEM-eligible degree program (CIP code 27.0501). The STEM designation allows eligible graduates on student visas access to an Optional Practical Training (OPT) extension, up to 36 months, as compared to 12 months for non-STEM degrees. As an international student, the longer work authorization term may help you gain additional real-world skills and experience in the U.S.

Integrated Baccalaureate and Master's Degree Program

Go to for details and program offerings.

 Admission Requirements 

  • A minimum cumulative GPA of 3.0 OR 3.0 or higher GPA for the last two years (60 s.h.) of undergraduate work
  • Undergraduate preparation in a relevant area, such as, mathematics, statistics, economics, quantitative or biological sciences, sociology, psychology, business, computer sciences, physics, engineering, education with at least one semester of calculus and stat theory.
  • Applicants must hold a bachelor’s degree from an institution that is accredited by the appropriate U.S. Department of Education regional accrediting agency.
  • Students whose native language is other than English must demonstrate written and spoken English language proficiency. Evaluation of English language proficiency will be based on the student’s scores on the Test of English as a Foreign Language (TOEFL®). Students must meet institutionally mandated minimum TOEFL® scores.
  • In order to be considered for an assistantship within the School of Accounting, Finance, Economics, and Decision Sciences applicants must submit GRE (or GMAT) scores or have at least 1 full-time semester of study at an AACSB accredited institution. These scores are NOT required for admission but only for those students that wish to apply for a school assistantship.
  • Students that do not meet the 3.0 GPA requirement are encouraged to take the GRE and submit the results to strengthen their respective application in the program.

Admission to any graduate degree program at WIU is contingent upon successful completion of undergraduate coursework specified as a prerequisite. If an applicant is deficient in any or all of the minimum requirements for admission into program, such an applicant may be provisionally admitted into the program subject to the completion of all deficiencies before taking any required courses within the program depending on remaining capacity in the program. The applicants will be duly notified what deficiency courses they need to take at Western Illinois University before they will be allowed to enroll in any of the required courses in the program.

Deficiency courses that an applicant may be asked to complete include one year of calculus (Math 133/134 or Math 137/138), and Introduction to Probability & Statistics (STAT 276/DS 303/DS 503) or equivalent. Students deficient in any of these areas will be required to take one or more courses to remove these deficiencies prior to enrolling in the courses that are part of the program’s core requirements. Students with a 3.25 cumulative GPA do not need the second semester of calculus for admission.

 Degree Requirements

I. Core Courses: 21 s.h.

DS 421G Data Visualization for Decision Making (3)
DS 423G Management Science Techniques and Business Analytics (3)
DS 435G Applied Data Mining for Business Decision Making (3)
DS 480G Predictive Analytics (3)
DS 490G Statistical Software for Data Management and Decision Making (3)
DS 510 Foundations of Business Analytics (3)
DS 560 Categorical Data Analysis Using Logistic Regression (3)

II. Electives: 9 s.h.

A. Topics in Data Science: 6 s.h. (Choose two of the following):

DS 485G Big Data for Business Decision Making (3)
CS 433G Python for Data Explorations (3)
CS 481G Database Programming (3)

B. Topics in Modelling and Analytics: 3 s.h. (Choose one of the following):

3 s.h. of Graduate Level DS Coursework (except DS 500, DS 503, DS 533)
ACCT/FIN 445G Financial Modeling and Spreadsheet Analysis (3)
CS 540 Computer Simulation (3)
ECON 487G Econometrics (3)
ECON 488G Experimental Economics (3)
ECON 506 Econometrics I (3)
PSY 551 Structural Equation Modeling for the Behavioral Sciences (3)
STAT 471G Introduction to Mathematical Statistics I (3)
STAT 478G Analysis of Variance (3)
STAT 553 Applied Statistical Methods (3)

III. Select one of the following exit options: 3 s.h.

A. Thesis Option

DS 601 Thesis (3)
(requires two semesters, 6 s.h., 3 s.h. count as elective in II.B. above)

B. Internship Option

DS 599 Internship (3)

C. Capstone Project Option (choose one of the following):

ECON 507 Econometrics II (3)
DS 521 Advanced Data Visualization (3)
DS 523 Advanced Management Science Techniques & Analytics (3)
DS 535 Advanced Data Mining for Business (3)
DS 580 Advanced Predictive Analytics & Times Series Forecasting (3)

IV. Other Requirements: 0 s.h.

DS 602 Department Seminar (0), two semesters required
DS 604 Applied Statistics and Decision Analytics Assessment (0)

*Upon approval from the program graduate advisor, students may select elective courses listed above under I and II (excluding those courses that are otherwise used to fulfill the requirements under I and II) or from additional program-specific and related electives from Computer Science, Decision Sciences, Economics, Mathematics, Statistics, or other 500-level graduate courses in Research/Quantitative Methods (Techniques), Applied Business Research, etc., from Law Enforcement and Justice Administration, Management, Marketing, Sociology, Psychology, etc.


The capstone courses are fundamental in providing the knowledge and tools necessary in formulating statistical hypotheses and analyzing final results. Students must complete 33 semester hours and may follow either a Thesis or an internship or a Non‑Thesis Option. Consultation with and approval of the program graduate advisor concerning course selection is required to insure completion of all requirements. Students wishing to take a reading, or an independent study, and/or an internship course must receive approval from the Graduate Committee Chair prior to registration. All special permissions or petitions must be approved prior to registration. Transfer and extension credit will be accepted in accordance with current School of Graduate Studies policy.

While all graduate students must complete the required core courses, it is possible to elect courses that will enhance specific career objectives. For further information on elective concentrations consult the program graduate advisor.

Post-Baccalaureate Certificate

The Department of Economics and Decision Sciences also offers a 19 s.h. post-baccalaureate certificate (PBC) in Business Analytics. The Business Analytics PBC offers the technical skills of data mining, statistical modeling, and forecasting for data-driven decision-making and for solving the analytical problems of the contemporary business world. For program details, go to the post-baccalaureate certificates page.

 Course Descriptions

Accounting (ACCT)

445G (cross-listed with FIN 445G) Financial Modeling and Statement Analysis. (3) Students will identify problems, analyze results, and make decisions regarding the impact on financial statements through development of models in electronic spreadsheets. Financial statements, capital budgets, risk, capital structures, takeovers, and other financial topics will be analyzed. Prerequisite: ACCT 341 or FIN 331 or permission of the instructor.

Computer Science (CS)

433G Python for Data Exploration. (3) Programming data-intensive and computational applications in Python. The emphasis is on using Python’s various technical libraries and tools geared toward data manipulation, visualization, and analysis, as well as scientific computing. Relevant case studies are used to hone these skills. Not open to MS in Computer Science students. Prerequisites: (CS 114 or CS 214) and (MATH 128 or STAT 171).

481G Database Programming. (3) Introduction to practical aspects of querying relational databases (using SQL). Creating applications written in high-level, general-purpose programming languages (Python) for interacting with databases. Necessary programming fundamentals, principles of database querying, developing applications that work with databases. Prerequisites: STAT 171 or permission of the instructor.

540 Computer Simulation. (3) Statistical techniques used in computer simulations. Construction and verification of simulation models. Programming projects. Prerequisites: One statistics course and familiarity with two programming languages.

Decision Sciences (DS)

421G Data Visualization for Decision Making. (3) This course provides introduction to the process and methods of visualization 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. Not open to students who have already completed DS 521. Prerequisites: STAT 171 or DS 200 or equivalent; or permission of instructor.

423G Management Science Techniques & Business Analytics. (3) An introduction to management science/operations research techniques. Students are introduced to the theory and applications of linear, integer, goal, and dynamic programming models; transportation, assignment, network and inventory models; PERT/CPM, capital budgeting, and decision theory. Not open to students who have already completed DS 523.Prerequisites: STAT 171 or equivalent.

435G Applied Data Mining for Business Decision-Making. (3) 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. Prerequisite: DS 303 or STAT 276 or permission of instructor.

480G Predictive Analytics. (3) A survey of topics in predictive analytics methods and techniques essential for business analytics. Topics include time series regression, logistic regression, neural networks, decision trees, ensemble models, and simulation models for understanding the effect of uncertainty. Not open to students who have already completed DS 580. Prerequisites: DS 490 or CS 114, and 6 s.h. of either STAT or DS coursework; or permission of the instructor.

485G Big Data for Business Decision Making. (3) 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. Prerequisites: STAT 171, DS 200, and DS 303 or equivalent; and CS 114 or DS 490 or equivalent; or permission of the instructor.

489G Seminar in Contextual Business Analytics. (3) 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. Prerequisites: DS 490 or CS 114, and 6 s.h .of additional DS coursework; or permission of the instructor.

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 or SPSS, and their applications. Methods of data preparation and validation, analysis, and reporting will be covered. Prerequisites: STAT 171 or equivalent, and DS 303 or PSY 223 or SOC 324 or POLS 284 or equivalent; or permission of the instructor.

500 Introduction to Business Analytics. (3) 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. Prerequisite: Graduate standing.

501 Independent Research. (1–3) Independent research and study of selected topics in decision sciences. Prerequisites: Completion of six graduate hours in decision sciences and permission of the Department Chairperson.

503 Business Statistics for Managerial Decision Making. (3) A survey of statistical methods useful for managerial decision making. Topics discussed include descriptive statistics, probability and probability distributions, statistical inference, analysis of variance, regression, contingency tables, and nonparametric statistics.

510 (cross-listed with MATH 510) Foundations of Business Analytics. (3) 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. Prerequisite: Graduate standing.

521 Advanced Data Visualization. (3) 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. Prerequisites: DS 421G, or permission of the instructor.

523 Advanced Management Science Techniques & Analytics. (3) Applications of management science tools and techniques for effective decision making with emphasis on model building. Topics include linear, integer, nonlinear, and dynamic programming, sensitivity analysis, and simulation. Prerequisite: DS 423G, or permission of the instructor.

533 Applied Business Forecasting and Planning. (3) A survey of the basic forecasting methods and techniques essential for modern managers. Topics include moving average and decomposition techniques, ARIMA processes, regression techniques, and technological methods such as Delphi and S-curves. Prerequisite: DS 503 or STAT 171 or equivalent.

535 Advanced Data Mining for Business. (3) 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. Prerequisites: DS 435G or permission of the instructor.

540 Applied Stochastic Models in Business Analytics. (2) 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. Prerequisite: DS 303 or equivalent, or permission of instructor.

560 Categorical Data Analysis Using Logistic Regression. (3) 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. Prerequisite: Graduate standing.

580 Advanced Predictive Analytics and Times Series Forecasting. (3) 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. Prerequisites: DS 480G, or permission of the instructor.

599 Decision Sciences Internship. (1–6, 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. Prerequisites: Completion of six hours of decision sciences courses and written permission of the Department Chairperson.

601 Thesis. (3, repeatable to 6) Research relating to a thesis topic in applied statistics and decision analytics. The grade in DS 601 will remain an incomplete until DS 601, Thesis, in completed. Graded S/U. Prerequisites: Graduate standing and permission of departmental graduate advisor.

602 (cross-listed with ECON 602) Department Research Seminar. (0, repeatable) A survey of contemporary theoretical and applied statistics and analytics research. Graded S/U. Prerequisite: Graduate standing.

604 Applied Statistics and Decision Analytics Assessment. (0) 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. Prerequisite: Enrollment in the Applied Statistics and Decision Analytics program.

605 Analytics Competition. (0) 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. Prerequisites: Graduate standing and permission of the instructor.

Economics (ECON)

487G Econometrics. (3) 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 is emphasized. Prerequisites: ECON 231, ECON 232; DS 303; MATH 137 or ECON 381; or permission of the instructor.

488G Experimental Economics. (3) Overview of scientific methodology relevant to studying economic decision-making. Best practices in collecting, managing, and presenting quantitative economic data and an introduction to the traditions of experimental economic design. Applied focus on software tools and project management. Prerequisites: STAT 171 and ECON 381; or permission of the instructor.

506 Econometrics I. (3) Elements of the theory and practice of econometrics: including univariate and multivariate single equation models, statistical problems such as multicollinearity, special techniques and applications, and an introduction to simultaneous equations models. Students will complete a project involving hypothesis formulation, data collection, analysis using statistical software, and written presentation of results. Prerequisite: ECON 509 or equivalent.

507 Econometrics II. (3) Advanced econometric estimation to include estimating micro and macroeconomic functions through simultaneous equation systems, dummy dependent variable models; and multivariate analysis. Class culminates in an independent research project. Prerequisites: ECON 581 and ECON 506; or permission of the graduate advisor.

Psychology (PSY)

551 Structural Equation Modeling for the Behavioral Sciences. (3) 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. Prerequisites: PSY 501 or an equivalent graduate-level course that covers descriptive statistics, correlation and simple regression; or permission of the instructor.

Statistics (STAT)

471G Introduction to Mathematical Statistics I. (3) The mathematical foundations of probability and statistics, principles of probability, sampling, distributions, moments, and hypothesis testing. Prerequisite: MATH 138 or 231 or equivalent.

478G Analysis of Variance. (3) A study of analysis of variance and covariance with applications. Includes experimental design. Prerequisite: STAT 276 or equivalent.

553 Applied Statistical Methods. (3) 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. Prerequisites: MATH 231 and STAT 276, or equivalents.