Optimize Workflows and Improve Business Decisions with Data
To be effective and keep up to speed in today’s workforce, engineers need to understand how to efficiently manage, process, and respond to an ever-expanding stream of data from industrial sensors, robotics and advanced instrumentation. Data Science for Engineers participants learn to apply data science and machine learning methods to transform raw data and uncover valuable, actionable insights within their organizations.
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Who Should Enroll
This course series is designed for working chemical and process engineers interested in optimizing and automating plant operations or developing data-driven strategies for their business. Each course can be taken individually or taken together as a linked series. Participants in individual courses will focus on specific topics and earn 2 Continuing Education Units (CEUs) per course. When taking the series, topics build and participants earn 6 CEUs. If you would like to learn more about credit options, please contact us directly.
Enroll in the series if you intend to apply data science tools in your work on a regular basis and would like to build a robust working model with ongoing faculty consultation using your own dataset or one provided.
What You Will Learn
Participants in this series will learn how to apply fundamental programming skills and modern data science methods with real data sets and models. Participants will understand the opportunities and constraints of working with large data sets to improve automation, optimization, and strategic decision making that benefit operations and the business, overall.
- Exploratory data analysis
- Supervised and unsupervised machine learning models
- Neural networks and introduction to AI
- Effective data dashboarding
Case studies introduce concepts and a complex simulation of a factory, or surrogate model, provide a through-line for all three courses in the series, which will help you to apply concepts and explore ways to optimize both a wide range of business processes and operations.
At the conclusion of the series, participants will be able to confidently manage complex sets of data and use them to predict and model processes, automate tasks, reduce downtime, improve margin velocity, enable line and product level consolidation, reduce changeover, and other means of optimization that have a direct impact on your company’s bottom line.
This series is equivalent to University of Washington’s Chemical Engineering courses UW CHEM E 545 & 546 — courses developed as part of a $3 million investment in creating unique graduate level coursework at the intersection of data science and chemical engineering.
Meet the Instructor
Dr. Beckner is a GIX faculty member, an instructor for the UW MS in Technology Innovation, a Data Science Advisor for the Pfaendtner Research Group, and partner at MFG Analytic, where he works with manufacturing clients to optimize their production processes using cloud-based tools. He received his Ph.D. in Chemical Engineering Data Science from the University of Washington and his B.S. in Chemical Engineering from the University of Texas at Austin. His consulting work inspired him to help organizations streamline their workflows and increase profit margins by training in-house employees to better understand and use data. In this course series, he distills his knowledge as a chemical engineering data scientist to the most salient, practical information applicable to the practicing engineer. Read his full biography here.
Course Details & Registration
Each online course in the Data Science For Engineers series is 4 days long from 8:00 a.m. - 1:00 p.m. (PST) with a break for lunch. Instruction is virtual.
April 12 - 15 or April 26 - 29
May 10 - 13 or May 24 - 27
June 7 - 10 or June 21 - 24
Each course in the series is $2500. Participants that register for the full series will receive a 15% discount.
Sign up to attend a short information session, meet the instructor and have your questions answered.Sign Up Here
David Hurt, Battery Technology consulting engineer
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