This newly updated course empowers engineers and technical managers to lead more effectively by leveraging modern data analytics in industrial and technical environments. Participants explore the full analytics spectrum, from understanding past performance to optimizing future outcomes, using visual tools, Excel, and structured decision-making frameworks. With a strong emphasis on practical application, the course blends lectures, discussions, and hands-on exercises to reinforce learning and build confidence in data-driven leadership. Participants will leave with a reusable Excel analysis file to support future data-driven decisions.

• Apply descriptive, causal, predictive, and prescriptive analytics to real-world engineering and business problems.
• Communicate data-driven insights clearly to influence decisions and drive organizational action.
• Use optimization and visualization techniques to improve product and process performance.
• Leverage advanced Excel tools to perform analytics for specific applications

• Engineers seeking to enhance their decision-making with data.
• Engineering managers responsible for performance, quality, or innovation.
• Technical professionals transitioning into leadership or supervisory roles.

Classes will be comprised of a lecture and discussion format.  Example data sets are used in class to illustrate the methods.  Excel workbooks are used to allow for easy access to analytical tools and practicing concepts learned.

Day 1

• Course and Individual Introductions 
• Establish a leader’s role in Decision Driven Data Analytics
• Overview of the role of Data Analytics
• Introduction to Descriptive Analytics, Data Management, Dashboards and Capability Analysis

Day 2

• Day 1 Recap
• Introduction to Causal Analytics
• Overview of the most effective types of causal analytics
• Introduction to design of experiments for analytics

Day 3

• Day 2 Recap
• Introduction to Predictive Analytics
• Use of Machine Learning to develop a Predictive Model
• Use of Response Surface Methods to develop a Predictive Model

Day 4

• Day 3 Recap
• Introduction to Prescriptive Analytics
• Optimization with your causal and predictive analytics algorithms
• How to take action based on all of these 4 days
• Bringing It All Back Together

• Great content, instructor was definitely an expert in the subject and has passion for the subject which helps keep the students engaged.  - Chris Gerold
• There was good references in this course to career applications and Tony was very passionate and knowledgeable about the topic. - Jerrad Lopp
• Instructor is very knowledgeable and had a lot of examples and is very passionate about data analytics. - Jason Berry

Anthony Orzechowski

Tony instructs courses in Applied Analytics in several master's programs at the University of Wisconsin – Madison and Northwestern University.  These course focus on providing students with the immediate ability to lead improvement and innovation in their careers through analytics.  

Tony is the recently retired Director of R&D Data Analytics at Abbott Laboratories, where he oversaw a broad spectrum of analytics responsibilities, ranging from statistics, reliability and quality engineering to data management, advanced analytics, and business decision support.

In his role at Abbott, Tony’s organization supported the development of diagnostic products currently contributing over $4 billion in annual worldwide revenue. With nearly 40 years of industry experience, he has served in leadership roles in both the automotive supply and medical diagnostics sectors, where he also championed use of Lean and Six Sigma methods in operations and product development as a Master Black Belt.

In his new entrepreneurial venture, Tony leads Convergent Analytics, an analytics consulting firm. Here, his focus is on helping companies improve through the use data analytics and maximize these capabilities through generative AI. His efforts aim to equip the next generation of engineers, scientists and analysts with the tools and insights needed to solve the complex challenges that industries will face today and tomorrow.