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Industrial Data Analytics: Models, Forecasts, and AI Tools

This advanced data analytics course empowers engineers and technical professionals to transform complex data into actionable insights. Participants will gain hands-on experience with Python, applying techniques such as classification, clustering, frequent pattern mining, anomaly detection, failure event modeling, deep learning and forecasting to predict system performance and optimize processes. Emphasizing real-world industrial applications, the course bridges analytics with practical outcomes in reliability, quality, and data-driven decision-making, enabling participants to deliver measurable impact in their organizations.

Learning Outcomes:

At the completion of this course, participants will be able to:

Apply core and advanced analytics methods in Python, including data manipulation, visualization, classification, clustering, and pattern mining to extract insights from data and support decisionmaking.

Develop and implement predictive models in Python for failure event modeling, anomaly detection, deep learning, and time series forecasting to address engineering and industrial challenges.
Integrate data-driven and AI-based approaches, such as statistical process control, supervised and unsupervised learning, and deep learning to enhance reliability, quality control, and operational efficiency in real-world applications.

Who Should Attend:

Manufacturing, process, and quality engineers aiming to apply analytics to improve performance and reliability.
Operations and continuous improvement leaders seeking to integrate predictive and diagnostic models into decision-making.
Data analysts and technical managers interested in expanding from basic analytics to applied machine learning and forecasting using R and Python.
This course will use Python. Students are not required to know Python in advance, though prior familiarity is preferred. However, some programming experience is required.

Course Details: RA00118-E107

Course Outline

DAY 1

Course Introduction
Module 1: Basics of Python, Getting Data into Python, Data Manipulation in Python
Module 2: Data Visualization, From Data to Insights
In-class Exercise and Q&A

DAY 2

Day 1 Recap
Module 3: Classification and Supervised Models in Python
Module 4: Clustering and Unsupervised Learning in Python
Module 5: Frequent pattern mining in Python
In-class Exercise and Q&A

DAY 3

Day 2 Recap
Module 6: Introduction to Python for Data Analytics
Module 7: Failure Event Modeling Approaches in Python
Module 8: Anomaly Detection with SPC and Multivariate Monitoring
In-class Exercise and Q&A

DAY 4

Day 3 Recap
Module 9: Deep Learning for Data Analytics
Module 10: Time Series Forecasting in Python
Break
In-class Exercise and Final Q&A

Course Schedule

Instructors and Program Director

Instructors
Kaibo Liu
Dr. Kaibo Liu (Grainger STAR Professor, Industrial and Systems Engineering)

Dr. Kaibo Liu is currently the Grainger STAR Professor in the Department of Industrial and Systems Engineering at the University of Wisconsin-Madison and serves as the Associate Director of the UW-Madison IoT Systems Research Center. He earned his B.S. degree in Industrial Engineering and Engineering Management from the Hong Kong University of Science and Technology, an M.S. degree in Statistics, and a Ph.D. degree in Industrial Engineering from the Georgia Institute of Technology. Dr. Kaibo Liu’s research is in system informatics and big data analytics, with an emphasis on the data fusion approach for system modeling, monitoring, diagnosis, prognostics, and decision making. His research has been successfully funded by NSF, ONR, AFOSR, ERDC, DOE, NIH, and Industry.

He is the recipient of three prestigious early career awards, including the 2019 Outstanding Young Manufacturing Engineer Award by SME, the 2019 Feigenbaum Medal Award by ASQ, and the 2019 Dr. Hamed K. Eldin Outstanding Early Career IE in Academia Award by IISE. He is also the winner of the Innovations in Education Award from IISE in 2020 and the Award for Technical Innovation in Industrial Engineering from IISE in 2021. He received the honor of the Georgia Tech Alumni Association’s 40 Under 40 and Ragnar E. Onstad Service to Society Award from UW-Madison.

In 2024, he received the prestigious Hromi Medal Award from ASQ. Dr. Kaibo Liu is currently serving as a senior editor of IEEE Transactions on Automation Science and Engineering and as the focus issue editor of IISE Transactions on Data Science, Quality, and Reliability
Xin Xia
Program Director
James Rink

Additional Information

Total Credits:

CEU 1.4,

PDH 14,

Course Administration

Fee Information

Fee covers Live instruction and course materials.

Discounts

Receive 10% off the registration fee per person when 3-7 individuals from the same organization enroll. Team affiliation will be confirmed as part of the registration process.

Receive 15% off the registration fee per person when 8 or more individuals from the same organization enroll. Team affiliation will be confirmed as part of the registration process.

Payment Options

If you are planning to attend an Interdisciplinary Professional Programs course, payment is required at the time of registration. Below are the payment options:

Pay by Credit Card

Enroll online and pay by credit card.

Search for the course on the website and then click on the Enroll Now button from the course webpage.
Enter all necessary course attendee information and payment information on the course enrollment page.
You will receive an email to confirm successful enrollment and payment.

Enroll over the phone and pay by credit card.

Call CERC Registrations at 608-262-2451.
Provide the registrations representative with:
- the course name, dates, and/or course number.
- the necessary course attendee information and payment information.
You will receive either a mailed document or an email to confirm successful enrollment payment.

Pay by Check

Mail in a completed registration form and check payable to UW Madison.

Fill out a registration form (found either in the back of the course brochure you received in the mail or here).
Prepare a check, made payable to UW Madison.
Mail the registration form and check to: CERC Registrations 21 N Park St, Ste 7101 Madison, WI 53715
You will receive either a mailed document or an email to confirm successful enrollment and payment.

Pay by Purchase Order

Request Purchase Order information from registration@cerc.wisc.edu
- If a fee quote is needed, contact custserv@interpro.wisc.edu first
You will receive an invoice from UW–Madison Business Services for payment, usually within 3–5 business days

Military

If using SF-182 form, please call our registration number at 608-262-2451 or email registration@cerc.wisc.edu for details and instructions.

Event Cancellation

We reserve the right to cancel a course due to insufficient enrollment or unforeseen events. If we cancel a course, participants will be notified via email or phone and will be given the option for a full refund or to transfer their registration and any fees paid to another course. We are not responsible for non-refundable plane tickets, hotel reservations, and other travel related expenses. For enrollee Course Cancellation, refer to notes on course page.

Cancellation Policy

If you cannot attend, please notify us no later than one week before your course begins, and we will refund your fee. Cancellations received after this date and no-shows are subject to a $150 administrative fee. You may enroll a substitute at any time before the course starts.