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Business Analytics

Spanning a diverse range of research and applications, business analytics is integral to solving business problems. This certificate focuses on learning about methods to understand and evaluate data to build models that predict action or behavior. You’ll learn to find and quantify patterns in the data using statistics, complex mathematical models and visual techniques to predict outcomes.

The completion of this certificate provides a record of academic accomplishment in the business analytics area. This certificate is open to current MBA students, MBA alumni or any post-baccalaureate degree holder.  Admission requires an accredited bachelor’s degree with a minimum 3.0 GPA. 

Students in the Working Professional MBA program who take the business analytics advanced core could add the business analytics certificate by taking an additional two courses.

Prerequisite courses for the business analytics concentration:  An introductory statistics class or DSCI 701.

Required courses

DSCI 712 Advanced Statistics I
This class covers the process of building statistical models from observed data: including identifying an appropriate model, checking the fit of the model to observed data, and using the model to make inferences. An emphasis is upon development of models that can help answer questions of interest. The class covers multiple regression, variable selection in multiple regression, analysis of variance and logistic regression. Prerequisite: DSCI 701 or an introductory statistics class. Enrollment restricted. LEC.

The class is not offered for the Fall 2017 semester.

DSCI 713 Advanced Statistics II
This is a continuation of Advanced Statistics I. The topics covered in this class include time series models, sampling, classification, and clustering models. There will be an emphasis on development of statistical models that can be used to help solve business problems. Prerequisite: DSCI 712. Enrollment restricted. LEC.

The class is not offered for the Fall 2017 semester.

DSCI 716 Business Analytics Models
This course will cover construction and solution of analytic models for problems in the business domain using spreadsheet software. Examples of analytic models are linear, nonlinear, and integer programming models. Examples of business problems are advertising, workforce scheduling, aggregate planning, blending, production process, financial, transportation, assignment, network flows, and shortest path determination. LEC.

The class is not offered for the Fall 2017 semester.

DSCI 714 Data Visualization
The ability to accurately and effectively analyze and communicate business data is paramount in today's world of business analytics and big data. Increasingly, professionals are being ask use business data as a crucial component of everyday business decisions--competitive advantage is gained in companies that can analyze and visualize data successfully to help them make business decisions. This course will focus on data visualization through applied, hands-on projects that will help students produce, analyze, and communicate data in a variety of business sectors. A principle focus of the course will be the use of data analysis programs and associated visualization techniques. LEC.

The class is not offered for the Fall 2017 semester.

IST 713 Data Management
Businesses today collect and manage large volume of data in a variety of types, forms, and sources for myriad uses. To provide necessary skills to data scientists and analysts, this course introduces traditional data management concepts and techniques as well as contemporary information retrieval and processing innovations. Topics include database fundamentals, database query techniques, data marts and data warehouse, information retrieval and Web search, NoSQL movement and practices, data management for analytics, distributed data management and cloud computing, and other emerging trend and techniques. Enrollment restricted. LEC.
Fall 2017
Type Time/Place and Instructor Credit Hours Class #
LEC Fan, Lidan
W 06:00-09:40 PM BEST 135 - EDWARDS
2 21521
DSCI 715 Managing for Quality Improvement
This course will take a closer look at various aspects of the total quality theory and its practices. Particular attention will be paid to the foundations of total quality theory including Deming's 14 principles of management and key tenants of Juran and Crosby. Additional topics include an examination of the continual improvement process in theory and action, strategies for getting started, and issues to address during a transformation into a total quality model of operation. Prerequisite: DSCI 702. Enrollment restricted. LEC.

The class is not offered for the Fall 2017 semester.

MKTG 723 Marketing Research
This course engages students in a broad examination of marketing research as practiced in marketing organizations today. Topics include defining business problems and establishing research objectives, choosing among research approaches (survey, qualitative, and secondary designs), gathering data (questionnaires, focus groups/interviews, and online), analyzing research data, and generating actionable results for business decision-making. The emphasis is on practical applications for the marketing professional. Prerequisite: DSCI 701 or DSCI 810. Enrollment restricted. LEC.

The class is not offered for the Fall 2017 semester.

FIN 722 Prediction Markets
Prediction markets are a type of derivative market which can be used to make forecasts about upcoming events. Research has shown that they leverage the incentives of financial markets and the wisdom of crowds to effectively pool large amounts of information and make accurate, real-time forecasts. The class will explore the theoretical underpinnings of prediction markets and how they have been applied to topics ranging from politics to athletics. The main focus will be on applications within firms. We will evaluate the strengths and weaknesses of prediction markets, using as the basis of our discussion cases from real-world applications. Prerequisite: FIN 711. Enrollment restricted. LEC.

The class is not offered for the Fall 2017 semester.

Faculty coordinator

Steve Hillmer

Application requirements

  • Online application
  • $30 application fee, payable online
  • Official transcript (current MBA students excluded)
  • Current resume

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