Individual Badge Price: $900 each | Belt Bundles: $2,500
Exploratory Data Badge Starts: TBD |Dynamic Models Badge Ends: TBD
TUES & THURS | 6:00pm - 8:30pm
We have all heard terms like data analytics, AI, machine learning, deep learning, or similar. What do they all mean? How can an industry professional master them? Data Analytics for the Process Industries is here to demystify the field for industry professionals and students.
Designed and presented by leaders from industry and accomplished faculty from the University of Houston, it answers the question: “What does data analytics mean for the process industries?” It builds practical skills, bridging fundamentals with applications. This new program is structured as a series of micro-credentials or “digital badges” that you can immediately add to your online resume. Each badge represents an important data sciences skillset, together with practical examples, designed to equip you to handle current and emerging challenges using advanced data-based decision-making tools and techniques.
Each badge is a 15-hour module, delivered over a 3-week period, and the badges are stackable. The first three badges, which together form the Bronze Belt in Data Analytics for the Process Industries, will be launched in August 2021. Additional belts (silver and gold) are planned.
Industry is undergoing significant changes, including repositioning operations to be cost-competitive in a world of low oil prices, while meeting the growing demands of energy, competitiveness and productivity in a sustainable way. This requires working in smarter and more efficient ways. Innovation has always been at the core of the process industries. Many oil refining and chemical companies are already finding ways to implement data sciences solutions and are realizing tangible benefits, including competitive advantages.
As data sciences are playing an increasingly important role in addressing these industry challenges, industry leaders have recognized that there is a workforce shortage in skilled data scientists who understand the process industries. For this reason UH Energy, at the University of Houston, in collaboration with the William A. Brookshire Deptartment of Chemical & Biomolecular Engineering and industry experts, has developed the Data Analytics for the Process Industries Program. Our goal is to equip current and aspiring process industry professionals in data analytics concepts, and to provide hands-on experience with real world examples.
Who should attend?
Data Analytics for the Process Industries has been designed with two distinct groups in mind:
- Professionals already in the process industries and facing daily challenges handling data and making or supporting operational and business decisions. Data Analytics for the Process Industries will enhance your capabilities, and enable deeper insights from the data that you handle every day, creating opportunities for improved efficiencies and more productive and competitive operations.
- Young professionals preparing for a career in the process industries. Data Analytics for the Process Industries provides a unique perspective and a ready to apply practical skillset, giving you a competitive advantage as you enter the professional marketplace.
The industry focus is oil refining, chemicals, petrochemicals & polymers, and other process industries.
After successfully completing the Bronze Belt, participants will understand how to extract knowledge from raw data and be skilled at:
- Exploratory data analytics
- Data visualization
- Data processing
- From spreadsheets to toolkits for data analysis
- Programming essentials
- Regression and correlation
- Dynamic models
- Design of experiments
INDIVIDUAL BADGE PRICE:
BRONZE OR SILVER BELT BUNDLE (All 3 Badges purchased together):
The course is offered in 15-hour modules, each over a 3-week period. Digital badges are awarded for each module. For completing each group of 3 Badges, learners earn a Belt. Currently, only the Bronze Belt is available. Belts and Badges are a permanent addition to your skillset and resume.
The Bronze Belt starts, in the Exploratory Data Analysis & Data Visualization Badge, by introducing data analytics in the context of engineering, and it presents a range of tools and techniques for data analytics in a very familiar format – spreadsheets. Topics covered include data visualization and presentation, preprocessing data, simple estimation tasks, regressions and correlations; and understanding the limitations of spreadsheets. In the Programming Basics and Solving Practical Problems Badge, we progress to more advanced tools, and introduce arrays and structures, looping and conditionals, functions and scripts, debugging and error handling, and additional ways of visualizing and plotting data. And finally, the Dynamic Models, Design of Experiments, and Classification Badge focuses on dynamic models and design of experiments, and also introduces classification tools and multivariate methods. Some of the process industry applications covered in the Bronze Belt are data visualization and reduction, linear and nonlinear regression for static or dynamic models, and process monitoring and fault detection.
The Silver Belt provides a deeper dive into data analytics based on multi-variate statistics. This includes principal component analysis, Partial Least Squares (PLS), Total Least Squares (TLS), Support Vector Machines (SVM), Neural Networks (NN), Discriminant Analysis (DA), and simple resampling methods. It also covers applications of these techniques to relevant problems in the process industries.
The Gold Belt will introduce participants to tools and techniques for Kernel PCA (Principal Component Analysis), Nonlinear PLS (Partial Least Squares), PLS DA (Partial Least Squares Discriminant Analysis) – Resampling Methods (e.g., Bootstrapping). Examples involving both static and dynamic models will be covered.
Dr. Mark Darby
Dr. Michael Nikolaou
Professor, Chemical and Biomolecular Engineering, University of Houston
Dr. Guan Qin
Associate Professor and Gulf Coast Section of the Society of Petroleum Engineers Endowed College Professor in Petroleum Engineering, Cullen College of Engineering, University of Houston
Dr. Suryanarayanan Radhakrishnan
Clinical Assistant Professor in Decision and Information Sciences, Bauer College of Business, University of Houston
Dr. Dvijesh Shastri
Associate Professor of Computer Science, University of Houston
Dr. Robert Stewart
Director, Allied Geophysical Labs, Hugh Roy and Lillie Cranz Cullen Distinguished University Chair in Exploration Geophysics, Professor of Geophysics, University of Houston
Dr. Kalyan Venugopal
Associate Research Scientist, Department of Energy and Petroleum Engineering, University of Wyoming
Credentialing Program Prerequisites
- Senior or graduate student in engineering, technology or business with a general understanding of process operations such as conveying, pumping, compressing, mixing, separating, and reacting materials
- Process Industry Professional
Frequently Asked Questions
The registration process is currently closed. Please check back soon for information regarding our next DAPI program session or click here to sign up for updates on this and all of our UH Energy Micro-credentialing Programs:SUBSCRIBE
Currently we only accept credit card payments.
The program is run online as a live (synchronous) course, providing interaction with instructors in real time; and the sessions will be recorded. Numerical examples will be solved during the sessions, to illustrate the principles and applications that are taught.
Yes. The instructors will provide homework to reinforce concepts from the class. There will also be tests, and you need a passing grade for certification.
If you miss a class, or a portion of a badge, the material will be available as recorded material, and you can view the recordings at your convenience. However, we strongly recommend that you attend the course synchronously, as the live interaction with instructors is an important instructional component.
The later badges build on the content of the earlier ones, so in general the badges must be taken in the specified sequence. However, this requirement may be waived with the consent of the instructor.
If you miss a badge, you may be able to register for the badge for delivery through asynchronous recordings, and you will have to complete and pass all of the homework and exams to earn the badge. The asynchronous (i.e., recorded) material will be available, and you can qualify by viewing the recordings and achieving passing grades on the tests. However, we strongly recommend that you attend the course synchronously, as the live interaction with instructors is an important instructional component. The same registration process and fees apply whether you participate in the course in real time, or if you only participate asynchronously.
Micro-credentials are certifications for mastery of specific topic areas or skillsets. To earn a micro-credential, you typically have to complete a certain number of activities, assessments, or projects related to the topic.
Digital badges (or ebadges) are a validated indicator of accomplishment, skill, quality or interest that can be earned in various learning environments. Digital badges are now commonly used as “digital transcripts,” and they can be incorporated in LinkedIn profiles. Many badge earners also display their badges through social media.
Use and Acceptance: Micro-credentials and digital badges are now widely accepted by employers as evidence of competency in specific skills. They have been in use for roughly ten years, notably in the IT arena, where IBM has been a leader. Many other companies, large and small, are now using digital credentials, as are many academic institutions, including the University of Houston, Harvard, Cambridge, and a large number of other universities.
Students who have registered can withdraw from the course at any time. However, participants need to withdraw two days before start of classes to receive a refund. If you have paid for a Belt Bundle (all three badges) and would like to withdraw after completing a badge, you may do so two days before the start of classes for the next badge to receive a refund for the remaining badges.