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 is currently the Director of R&D Data Analytics at Abbott Laboratories in the Diagnostics business.  He is responsible for delivering the analytic outputs for design verification and validation submissions, product testing specifications, system integration robustness and capability assessments, next generation machine learning algorithms and business intelligence reporting. 

Tony has 35+ years of experience applying data analytics in the areas of product development, manufacturing, post launch investigations and productivity improvement opportunities.  Specific areas of focus over this time include management of the applied statistics, quality engineering, reliability analytics, data engineering, business intelligence and advanced analytics organizations who have supported the development and launch of 100’s of products across multiple platforms worldwide.

Tony has B.S. in Mechanical Engineering and an M.S. in Manufacturing Systems Engineering from the University of Wisconsin - Madison.   He is a certified Lean and Six Sigma Master Black Belt and is a Senior Research Fellow in the Volwiler Society at Abbott.  Over the past 14 years Tony has also served as an adjunct lecturer at Northwestern University where he teaches “Leading with Data Analytics” for the Master of Product Design and Development Management program.