Graduate Certificate | Online & In-Class Participation
Whether you are ready to advance your career, start your own business, or become a leader within a corporation, Shippensburg University can prepare you for achieving your personal and professional goals. Shippensburg University's John L. Grove College of Business offers you AACSB-accredited programs to help you to succeed in a rapidly changing global economy.
The 12-credit graduate certificate program is blended with online and in-class participation. Courses are offered once each calendar year, so you can easily complete the certificate in one year.
Please note: the Business Analytics courses can be applied to the MBA program as a concentration. For more information, visit ShipMBA Program.
Skills for Success
Learn the core concepts and gain hands-on experience:
- Search large data sets
- Build predictive models
- Develop interactive data-visualization tools using charts and heatmaps
- Maximize the use of Excel add-ins and XLMiner software for data mining
- Make data-driven marketing and business strategies
Students completing this certificate will develop skills in data mining, data visualization, and will be able to search through large data sets and develop predictive models. They will be able to use tools such as spreadsheets, statistical analysis software, and databases to manage and analyze large data sets. They will also develop skills to enable interaction with other organizational personnel specializing in analytics.
Course Information & Registration
For more information on course scheduling and registration, please see www.ship.edu/PCDE/Registration_Information/
- MBA 506 Data Mining for Predictive Analytics I
- MBA 507 Data Mining for Predictive Analytics II
- MBA 511 Marketing Analytics
- ISS 550 Database Design (Available at Ship & Dixon University Center)
MBA 506 Data Mining for Predictive Analytics
3 credits, Online | Professor / Instructor: Dr. Robert O. Neidigh
This course covers the basic concepts of data mining and introduces students to the data mining process. The students will learn data reduction, exploration, and visualization. The primary emphasis of the course will be using the data for predictive modeling. Time series forecasting methods are introduced. Students will use large data sets to build models in XLMiner.
After course completion, you will be able to:
- Construct charts and heatmaps to visualize data
- Manipulate categorical variables for dimension reduction
- Select variables to include in multiple linear regression
- Use ensembles to boost predictive power
- Construct time series forecasts including trends and smoothing methods
- Use XLMiner to search through large data sets and develop predictive models
MBA 507 Data Mining for Predictive Analytics II
3 credits, Online | Professor / Instructor: David Hwang, Ph.D., MBA, MS
This course is a second level course in managerial data analysis and data mining. The emphasis is on understanding the application of a wide range of modern techniques to specific decision-making situations, rather than on mastering the theoretical underpinnings of the techniques. Upon successful completion of the course, you should possess valuable practical analytical skills that will equip you with a competitive edge in almost any contemporary workplace. The course covers methods that are aimed at prediction evaluation, classification, association rules, and clustering. It also introduces cutting edge interactive data-visualization tools, as well as data reduction techniques. Students will use large data sets to build models in XLMiner.
- Understand second level statistical and data mining techniques
- Understand the uses and limitations of various techniques
- Implement major techniques using Excel add-ins and improve spreadsheet skills
- Become smart/skeptical consumers of statistical and data mining techniques
- Enable interaction with personnel specializing in analytics
MBA 511 Marketing Analytics
3 credits, Online | Professor / Instructor: Dr. Michael K. Coolsen
This course is designed to expose MBA students to the use of analytics in marketing strategy decision-making. Understanding the marketplace has now become an intense data-driven process, as many global companies have increasingly shifted their priorities in measuring strategic effectiveness to combine traditional marketing research efforts (e.g., descriptive survey analysis and focus groups) with advanced data science practices and marketing dashboard analytics.
After course completion, you will be able to:
- Expose and facilitate understanding of how companies can use data to create analytics that drive marketing strategy decision
- Understand how linear regression, logistic regression, and cluster analysis are conducted to create marketing dashboard analytic
- Use real-life cases and their respective data to learn how to create three aspects of marketing analytics: statistical analysis, experiments, and managerial intuition
ISS550 Database Design
3 credits, Online & face-to-face class component | Professor / Instructor: Azim Danesh, Ph.D.
This course is designed for students to learn the fundamentals of a database environment and address data and information management issues in a global multi-user business environment. Students will develop an understanding of the various roles within the data administration function of an organization.
To be eligible for admission to the program, applicants must:
- Have earned a bachelor’s degree from a regionally accredited institution
- Complete the graduate certificate application
- Submit official undergraduate and graduate (if applicable) transcripts documenting the earned degree
Tuition & Payment
Program Support & Contacts
For assistance with enrolling. course registration, or course schedule, contact:
Office of Professional, Continuing, and Distance Education (PCDE)
For more information about the MBA program and courses:
Dr. Irma Hunt, Ph.D., MBA Director
John L. Grove College of Business
Cancellation Policy- Shippensburg University reserves the right to cancel any courses due to insufficient enrollment or other unforeseen circumstances.