Analytics Training and Professional Development

These courses are open to the public. If you are an individual or business professional looking for analytics training, the Center for Business Analytics offers corporate training on a variety of business and data analytics topics. These short courses address some of the key skills required to increase data management skills and effectiveness and are taught by experts from the University of Cincinnati and from leading business analytics companies.

We also offer these and other customized courses at your location. For more information on any of these on-site course options, contact Glenn Wegryn, Executive Director of the Center for Business Analytics at wegryngn@ucmail.uc.edu or 513-556-7146.

Upcoming classes

August 23-24, 2018.

This course will introduce intermediate-to-advanced tools in Excel for analytics. We will cover data visualization topics that move beyond the basic charting tools in Excel. Descriptive analytics methods for analyzing data and generating meaningful insights will be covered using PivotTables, PivotCharts and other Excel tools. We will use Excel for predictive analytics by utilizing Excel’s regression tools and other forecasting capabilities. What-if analysis and other prescriptive analytics tools in Excel will also be introduced. We will cover special Excel functions such as VLOOKUP, MATCH and Data Tables to help you unleash the power of Excel as an analytics tool. This is the perfect class for the Excel user who is ready to take the next step of improving their analytics capabilities in a familiar software environment. 

October 25-26, 2018.

The course is an introduction to Microsoft PowerBI and its use as a data analytics and reporting tool. There will be an even mix of lecturing and working sessions where the students will be developing their own dashboards with some sample data.

November 29-30, 2018.

Python is one of the most popular programming languages in use today and has become very popular in recent years because of libraries available for numerical computation, scientific computing, data visualization, and machine learning. This course will familiarize students with the use of Python for data science and the common libraries to perform typical data science operations.

December 6-7, 2018.

This two-day workshop on Tableau will cover beginner, intermediate and advanced topics in Tableau. This course will be taught by Jeffrey Shaffer, a Tableau Zen Master.

Special offer for the R for Data Science Series: Register now for Introduction to R and Intermediate R, and receive a code for a 33% discount on Applied Analytics with R.

December 13-14, 2018.

R is one of the fastest growing programming languages and tool of choice for analysts and data scientists. In part, R owes its popularity to its open source distribution and massive user community.  In this course, we will help both new and existing R users master the basics of R. There will be an emphasis on using hands on exercises and real world datasets.

January 10-11, 2019.

This course will allow novice  users and those with some experience, the chance to learn skills, syntax, and techniques to perform analytics and data mining on datasets.

January 31-February 1, 2019.

This is the second course in the R series and will build on the material from Introduction to R.  Attendance at the introductory course is not required for those with significant practical experience in a professional setting. This course will cover the application of R for the entire data science workflow – data acquisition, wrangling, visualization, analytic modeling, and communication. There will be an emphasis on hands-on exercises and real world datasets.

February 28-March 1, 2019. Registration coming soon.

This is the third course in the R series and will build on the material from Intermediate R. Attendance at the introductory or intermediate course is not required for those with significant practical experience in a professional setting but it is strongly recommended that attendees have significant experience with the topics covered in those courses. This course will cover the application of several descriptive, predictive and prescriptive analytic techniques. The emphasis will be on the general purpose of these techniques along with integration and application in R rather than on the theoretical nature. This allows this course to be more accessible to a wider audience looking to inject R for analytic purposes across organizational processes. There will be an emphasis on hands-on exercises and real world datasets.

For questions and further course information please contact:

Larry Porter
porterlc@ucmail.uc.edu
513-556-4742