Analytics Training and Professional Development

The Center offers an extensive selection of analytics and data management in-person courses designed to provide analytics professionals with the skills to manage data, use popular analytics tools, and develop and communicate insights that improve business performance. Courses are
taught by leading academics and industry experts and include a mix of lecture and working sessions.


Introduction to MS PowerBI (no prior experience required):  July 27 - Aug 5, 2020
4 online sessions  Details and Registration

Advanced MS Power BI   September 14-23, 2020
4 online sessions Details and Registration

SQL for Business Analysts (no prior SQL experience required):   September 15-24, 2020 
4 online sessions  Details and Registration

Additional courses will be added soon.

In order for attendees to get the most benefit, support and interaction, these classes will be delivered on-line in four, 3-hour sessions over two weeks through Webex training with a live instructor and Business Analytics Masters students as technical support for attendees.  All course materials will be supplied prior to the course start date.  Software for these classes is available as a free download or an activation key will be provided, if applicable.

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, analytics and data science effectiveness and are taught by data scientists and professionals 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 or 513-556-7146.

Training Classes

PYTHON FOR DATA SCIENCE Introduction - Intermediate - Advanced
Introduction to Python emphasizes the core concepts and uses of Python, specifically data types, data structures, functions, and classes The Intermediate course provides more detailed information on how to program more efficient data science applications in Python, and modeling with scikit learn package.
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R FOR DATA SCIENCE Introduction - Intermediate - Applied - Text Mining - Machine Learning - Deep Learning with Keras and Tensorflow
In the Introduction course, we will help both new and existing R users master the basics of R and exploratory data analysis, compute key descriptive statistics, and perform basic data wrangling activities.  The Intermediate course will cover the application of R for the entire data science workflow – data acquisition, wrangling, visualization, analytic modeling, and communication.  The Applied course will cover the application of several descriptive, predictive and prescriptive analytic techniques with an emphasis on application in R rather than on the theoretical nature.
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TABLEAU Data Visualization - Introduction - Advanced
Learn basic data visualization skills, best practices, and how to present analytical output as meaningful information in the data visaulization training.  The Introduction to Tableau is designed for beginner users and anyone who works with data with or without technical or analytical backgrounds. Advanced Tableau covers advanced chart types, business dashboards, and other advanced methods and features.
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MS POWER BI Introduction-Advanced
The Introduction course is designed to be a primer to Microsoft PowerBI and its use as a data analytics and reporting tool. The Advanced course is a continuation of the "Introduction to Power BI" class and takes a deep dive into advanced level Power BI skills.  There will be an even mix of lecturing and working sessions.
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MACHINE LEARNING Fundamentals - Machine Learning with R - Deep Learning with Keras and Tensorflow in R
The fundamentals workshop is designed for beginners with little experience in Machine Learning (ML). Most commonly used tools and methods in ML will be discussed during the workshop with an applied focus. The Machine Learning with R 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.
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DATA MANAGEMENT - SQL for Business Analysts
The Data Management course introduces attendees to best practices in database design and how to interact between analytical tools and a database. The SQL course continues the Data Management training and is a hands-on introduction to using SQL.
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This one-day session focuses on providing a fundamental understanding of analytics, examples of successful applications in financial and other industries, how to get started, what resources, skill sets, organization and cultural elements need to be in place for long-term success.
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Jeffrey Shaffer, UC Center for Business Analytics Instructor, talks about data visualization.

Video link:

Other Classes


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.

Data 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.

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.

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.

For more information about these classes, or for custom training classes, please contact
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Larry Porter

Training , Marketing and Sponsorship Manager