Business Analytics Minor
The Business Analytics Minor helps students develop the skills and gain the knowledge of the technologies, applications, and processes used by organizations to turn massive, diverse, and complex data into actionable insights to drive their decision making across all aspects of business, including finance, marketing, accounting, and operations. The minor utilizes a problem solving approach that combines the management of data with training in data mining, predictive analytics, and data visualization. Students will graduate with hands-on expertise using current software applications that will enable them to contribute to their organizations immediately upon graduation.
The Business Analytics Minor will focus on preparing students in the following ways:
- Students will gain an understanding of data management tools and the practical design and use of databases in a business environment
- Students will develop the abilities to use data mining techniques to derive useful business insights
- Students will be able to author enterprise dashboards to report trends and support “what-if” analysis in real time
- Students will gain proficiency in applying current analytics technology to explore, analyze, and visualize data
- Students will strengthen presentation skills to effectively communicate business-relevant implications of data analysis
- Students will display the abilities to apply data analytics skills in one or more functional areas of business
- Minor in Business Analytics. The minor is currently only available to business students.
Jia Shen, Ph.D.
Associate Professor and Chairperson
Sweigart Hall 369
(15 credits not including prerequisites)
|Information Systems Essentials|
|Statistical Methods I|
|Statistical Methods II|
|Practical Business Analytics with Excel|
|Select two from the following menu||6|
|Accounting Info Systems|
|Seminar in Economic Research|
|Population Healthcare Management|
|Marketing Web Analytics|
|Quantitative Meth Bus Forecast|
The minor is currently only available to business students.
Business students pursuing the Business Analytics Minor are advised to take GSC 385 Management Information Systems for Global Supply Chain Management in lieu of CIS 385 Management Information Systems for their business major requirement.
Only two CIS courses can count towards both the Information Systems Major and the Business Analytics Minor.
CIS 330 Database Management 3 Credits
This course involves the study of computer databases. Major topics include relational databases, use of the structured query language (SQL) to query relational databases, and design and maintenance of relational databases.
Prerequisite(s): CIS 185.
CIS 350 Practical Business Analytics with Excel 3 Credits
CIS 350 – Practical Business Analytics with Excel is a required course for the proposed Business Analytics minor. This course will provide the student with an opportunity to gain proficiency in analyzing and visualizing data using Excel. The learning experience includes not only classic tools, such as pivot tables and VLOOKUP, but also more advanced Excel data tools such as building Excel data models, creating data mash ups, and using the Power Pivot add-in. The course also requires students to complete a data analysis project along with a presentation about the business insights drawn from the data analysis results. The project requires students to understand the business problem, identify and apply the appropriate analytic and visualization tools, and communicate the insight in an intuitive and effective manner.
CIS 360 Data Mining 3 Credits
This course deals with modern technologies for data analysis. Hands-on exercises for data retrieval, data visualization and predictive analytics will be carried out using up-to-date methodologies and software tools. The full data mining life cycle will be covered from recognizing business problems and opportunities amenable to data mining analysis through deploying and monitoring solutions.
Prerequisite(s): CIS 185.
CIS 370 Systems Analysis and Design Project 3 Credits
Topics include modeling techniques and methodologies to address the planning, analysis, design, and implementation of high quality systems, delivered on time and within budget. Using rapid application development tools, students will also construct an operational system within the span of a single semester. Issues and tools related to the management of project teams are also discussed.
Prerequisite(s): CIS 330.
ACC 320 Accounting Info Systems 3 Credits
This course provides an introduction to accounting information systems and enterprise-wide, process-focused information systems. Topics include quality of data for decision usefulness, internal control concepts and documentation tools, and database theory and applications.
Prerequisite(s): ACC 310.
ECO 450 Seminar in Economic Research 3 Credits
Students in the course learn to conduct economic research by engaging in an actual community-based research project. At the beginning of the semester, students are assigned to a community-based organization. As a team, students meet with the client, devise a plan of action, collect and analyze data and other information, and write a report to the client. At the end of the semester, students present their findings to the client. Students are permitted to take EC0 450 up to two times for credit.
Prerequisite(s): Permission of instructor.
FIN 315 Financial Modeling 3 Credits
Provides instruction in computer use beyond what is available in other finance courses. Topics include more sophisticated applications of computers in financial management, investments, and other areas of finance and business. Students work on cases and projects which require more advanced usage of spreadsheets and other software and databases.
HTH 215 Population Health Care Management 3 Credits
In this course, we study how disease is distributed in populations and of the factors that influence or determine this distribution. This course introduces the basic methods and tools epidemiologists use to study the origin and control of non-communicable and communicable diseases so that policies and mechanisms to enhance the health of populations can be developed.
MKT 366 Marketing Research 3 Credits
Topics include specific research procedures in gathering, processing, analyzing, and presenting information relevant to marketing problems: advertising planning and effectiveness; product development; distribution channels; sales techniques; consumer behavior; and forecasting. Student learning about research planning, implementation, and interpretation is facilitated by the use of projects or cases.
MKT 367 Marketing Web Analytics 3 Credits
This course teaches web analytics through practical applications with a focus on deriving actionable insights. It provides a broad overview of key web analytics strategies, concepts, issues, challenges and tools. Topics covered include: • How to choose a web analytics tool • Metrics and key performance indicators • Best ways to analyze effectiveness of blogs, marketing campaigns, SEO, SEM and emails • How to utilize quantitative, qualitative and competitive tools to derive actionable insights • How to optimize web sites by incorporating testing and experimentation • Analytics in social, mobile and video • Best practices and pitfalls in web analytics
Prerequisite(s): MKT 200.
MSD 320 Quantitative Methods for Business Forecasting 3 Credits
A study of the various quantitative techniques applicable to the problems of forecasting that occur in business and industry. Topics may include the regression techniques of causal modeling, as well as the moving average, exponential smoothing, and Box-Jenkins approaches of time series analysis. All methods are illustrated with the use of realistic forecasts.
MSD 325 Regression/Analysis Variance 3 Credits
This course examines the use of applied linear statistical models to adequately describe practical relationships in business and economics. The implementation of a popular statistical computing package to analyze realistic data sets is an important component of the course. Topics include simple and multiple linear regression, model diagnostics and remedial measures, and the analysis of variance.