Stetson University

Center for Business Intelligence and Analytics

Education

This site is a web archive of Stetson University's Center for Business Intelligence and Analytics (developed during 2013-2015). The site content is no longer updated.

Business Intelligence and Analytics Courses

Introduction to Business Statistics

This course focuses on descriptive statistics, including graphical methods, measures of central tendency and variation, probability and probability distributions, sampling distributions, and introduction correlation and regression. Students are introduced to various statistical computer applications.

Business Statistics

This course provides a survey of statistical topics useful in support of managerial decision-making. The course covers sampling and sampling distributions; foundational statistical inference, including one- and two-sample tests for means and proportions; confidence interval estimation and hypothesis testing; chi-square test; regression analysis; and computer applications. The course has a heavily applied emphasis.

Business Analysis and Systems Design

Students will gain knowledge essential to planning, analysis, design, and implementation. Upon completion of the course, students will have the knowledge to perform effectively as an entry level business analyst.

Business Intelligence

This course introduces the concept of Business Intelligence (BI). Students will learn how BI is used by organizations to make better business decisions, use fewer resources, and improve the bottom line. This course provides an overview of business intelligence topics as well as hands-on experiences. Topics include business analytics, data visualization, data mining, data warehousing and business performance management.

Predictive Analytics

This course provides an introduction to predictive analytics techniques used in business applications and social science research. Using enterprise-class analytics software and real-world data, students will learn how to build predictive models using techniques such as logistic regression, neural networks, cluster analysis, and decision trees.

Social Media Analytics

This course introduces the technologies and managerial issues related to social media analytics (SMA). Students will learn the importance of social media in influencing the reputation of contemporary businesses, examine text mining, sentiment analysis, and social network analysis, and apply the concepts, techniques, and tools to analyzing social media data. Real-world data such as online reviews, microblog postings, human interaction networks, and business networks will be studied. Hands-on training will be provided using a variety of software tools.

Health Informatics

This course is designed to provide an overview in major sections of healthcare information systems, healthcare informatics, and healthcare business intelligence. Informatics combines healthcare and business intelligence for decision making aimed at reducing costs and improving quality through use of information systems and technology.

Managerial Decision Analysis

An analysis of the quantitative decision making process in management. Emphasis on decision theory, probabilities, statistical reasoning, data mining, forecasting. Contemporary information technologies are used to support decision analysis.

Big Data Mining and Analytics

This course is a survey of the means of acquiring, storing, accessing and analyzing large data sets. Topics include using common data sources and APIs for acquiring data, including medicine and health, finance, economics, and marketing, storing and accessing data, and statistical and machine learning algorithms for mining and analyzing data.

Technology for Business Transformation

Using case analysis, class discussion, and problem solving exercises, this course explores the critical factors affecting business success through the use of information technology. Business strategy issues, uses of business intelligence, streamlining business operations, creating an environment that builds innovation, and organizational transformation are discussed in detail.

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