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

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.

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.

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.

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.

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.

February 28-March 1, 2019.

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.

Classes not currently scheduled

Advanced Data Management

Advanced Data Management is a continuation of the topics discussed in the Introduction Data Management course. This course focuses on dimensional modeling and data warehousing techniques, and teaches participants advanced SQL code. SQL development will focus on learning procedural language SQL (PL/SQL, Transact-SQL, TSQL) to create flexible and useable solutions to solve test business cases. The class will finish with coverage of current database options (cloud services), discussion of NoSQL (big data solutions), and learning how to consume data from a database into analytical tools. This class will build upon an individual's strengths in business, information technology and analytics, and is meant for those wanting to increase their knowledge regarding databases and programming in SQL.

Advanced Data Mining

Find the useful information hidden in your data! This course surveys comput­er-intensive methods for inductive classification and estimation, drawn from statistics, machine learning, and data mining. Participants learn the key in­ner workings of leading algorithms, compare their merits, and briefly demon­strate their relative effectiveness on practical applications.

Analytics in Excel

This course introduces 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.

Analytics for Leaders

This half-day session focuses on providing a fundamental understanding of what analytics is, examples of successful applications in financial and other industries, how to get started, and what resources, skill sets, organization and cultural elements need to be in place for long-term success. Examples in Descriptive, Predictive and Prescriptive analytics will be reviewed, including common tools and methods used to perform analytics and conveying results effectively. This is an interactive, thought-provoking course with lectures and break-out work groups to allow better understanding of the concepts taught.

Big Data and Spark

Making sense of the tools used to analyze big data can seem confusing and overwhelming at times. This session helps you understand how these components function and form the core of big data analytics systems. The emphasis of this course will be on understanding the fundamental principles of big data systems using Hadoop and Spark.

Spark allows the processing of huge volumes of data in real-time, and is a dominant choice for performing analytics at scale. Similarly, the Hadoop Distributed File System (HDFS) forms the backbone of most big data systems. In this course, participants will learn the theory behind how these tools work so they can understand when, and how, to implement them effectively. The relative strengths and weaknesses of various big data systems will be highlighted to explain how Spark has emerged as a popular choice for analyzing dynamic, high-velocity, and high-volume data.

Participants will also get hands-on experience using HDFS and Spark to illustrate the power of big data analytics.

Data Mining

Most organizations don't suffer from a lack of data, but from a lack of actionable insights based on that data. Powerful techniques now exist to take messy data sets and generate surprising insights. Textbooks and websites tout the power of data mining, but few sources actually discuss the difficulties and challenges that must be overcome with real world data. This two-day workshop focuses on the challenges of building predictive models in the real world and techniques experienced practitioners apply when developing their models.

Data Visualization

Data visualization encompasses a set of techniques and principles that can be used to transform data at its most basic form into charts and tables that can be analyzed and presented to generate insights and spur action. This two-day workshop will teach participants how to use data visualization to both analyze complex data sets to generate insights and to present analytical output as meaningful information.

Introduction to Big Data and Hadoop

This two-day workshop explores drivers behind Big Data and uses cases across a wide variety of industries to illustrate the power of new technologies to harness Big Data and generate meaningful insights. Participants will be introduced to Hadoop and key-value data storage, the central components of the Big Data movement. These systems allow the distributed processing of very large data sets for structured and unstructured data.

Introduction to Python Programming

This course is designed to be an introduction to Python and its uses as a data analytics tool. The material will emphasize the core concepts in Python, specifically data types, data structures, functions, and classes and how they can be implemented and used to address data analytics problems. Popular modules used in data analysis will also be covered at a high level to include both NumPy, Matplotlib, and statsmodels.

Machine Learning with R

Learn the fundamentals and application of modern machine learning tasks. This course will cover unsupervised techniques to discover the hidden structure of datasets along with supervised techniques for predicting categorical and numeric responses via classification and regression. This course is intended for academics and data science practitioners who wish to learn about machine learning tasks as well as a guide to applying them. Readers should have knowledge of basic statistical ideas, such as correlation and linear regression analysis, along with intermediate R programming skills.

Text Mining with R

If you work in analytics or data science you are familiar with the fact that data is being generated all the time at ever-faster rates. Analysts are often trained to handle tabular or rectangular data that are mostly numeric, but much of the data proliferating today is unstructured and text-heavy. Many of us who work in analytical fields are not trained in even the simplest approaches to analyzing unstructured text data. This short course serves as an introduction to text mining with the R programming language.

For questions and further course information please contact:

Headshot of Larry Porter

Larry Porter link

513-556-4742