This course is focused on providing attendees with the methods to drive more effective decisions and actions through data analytics in an industrial setting.  The course will emphasize the use of visual methods to achieve this.  Course discussion will cover applied use of descriptive, causal, predictive, and prescriptive analytics.

Approach - The course will present the use of data analytics to tackle five major challenges common in industrial settings.  These topics include how to:

• Describe the performance of a product or business process
• Make risk-based decisions in the presence of uncertainty in our data
• Identify the input factors that drive outcome performance
• Optimize the performance of our products or business processes
• Communicate analytical results that will drive business decisions and drive action

Classes will be comprised of a lecture and discussion format.  Example data sets are used in class to illustrate the methods and hands-on team assignments are completed by student teams outside of class to reinforce learning.

The two key outcomes of the course for each student are to demonstrate the ability to:

• Obtain meaning from data
• Effectively communicate this meaning to others to drive decisions and actions

• Engineers
• Engineering managers
• Technical professionals transitioning to supervisory roles

Attendees are able to earn a digital badge as evidence of the knowledge they obtained during the course. Digital badges are micro-credentials that can be earned by successfully completing application exercises woven throughout the course.

Click here for information on digital badges.

Earn 1.4 CEUs, 14 PDHs with this course.

This course is part of the Technical Leadership Certificate. Course may be taken individually as well.

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 2 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

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.