Individual Badge Price: $400 | Belt Bundles: $1,000
Dates: Data Processing: June 5 | Alt Machine Learning Ends: August 16
MON & WED | 6:00pm - 8:30pm
Energy professionals – current and future – need deeper insights into operational data to make tougher decisions. UH Energy and the Subsea Systems Institute are delighted to present No Code Analytics and Machine Learning in Energy to address this need for the energy industry.
Designed and presented by leaders from industry and accomplished faculty from the University of Houston, the program provides a structured series of micro-credentials or “badges” that will provide the necessary data sciences skillset to facilitate developing solutions to current and emerging challenges using advanced data-based decision making.
Each badge is a 15-hour module, delivered over a 3-week period, and the badges are stackable. The three badges together form the Bronze Belt in No Code Analytics and Machine Learning in Energy.
The energy industry is undergoing significant changes, including repositioning operations to be cost-competitive in a world of low oil prices, and meeting the growing demands of energy in a sustainable way.
This requires working in smarter and more efficient ways. Innovation has always been at the core of the energy industry. Many energy companies are already finding ways to implement data sciences solutions and are realizing tangible benefits, including enhanced sustainability and competitive advantage.
Why This Program?
Data sciences are already playing an important role in addressing several industry challenges. Industry leaders have validated these claims and have recognized that there is a shortage energy industry professionals with a practical knowledge of data sciences.
For this reason, UH Energy and the Subsea Systems Institute have developed the No Code Analytics and Machine Learning in Energy. This program is designed to equip current and aspiring professionals in the energy industry with data analytics concepts and hands on experience on applications with real world examples, using readily accessible tools that do not require extensive programming skills.
This program is designed, developed and delivered jointly by subject matter experts from the University of Houston System and from industry.
Who should attend?
No Code Analytics and Machine Learning in Energy has been designed with two distinct groups in mind:
- Those of you who are already in the energy industry and facing challenges daily in making operations more efficient while continuing to grow the energy business sustainably. No Code Analytics and Machine Learning in Energy will enhance your capabilities to get deeper insights from the data that you deal with, facilitating finding solutions to these challenges.
- If you are readying yourself for an energy career, you need to prepare yourself for a dynamic, exciting and challenging professional life. The No Code Analytics and Machine Learning in Energy program provides a unique perspective and a ready to apply practical skillset, giving you a competitively advantaged competence as you enter the energy marketplace.
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. Upon completing the initial 3 Badges, learners will earn the Bronze Belt in No Code Analytics and Machine Learning in Energy.
There are three belts in the program. The Introductory Belt (Bronze) is followed by an Intermediate Belt (Silver) and finally by an Advanced (Gold) Belt. The Belts and Badges will be a permanent addition to your skillset and resume.
The Bronze Belt provides the participants the tools and techniques to build and evaluate data-driven models via the machine learning approach. It covers the data analytics techniques to extract knowledge from raw data by building data-driven models. All aspects of data-mining – data exploration, data preprocessing, machine learning modeling, and model evaluation – are covered. The sessions combine theoretical knowledge with hands-on training of the data analytics techniques using real energy industry datasets.
Python is an easy to learn, powerful programming language. It has efficient high-level data structures that make it suitable for rapid application development. Topics covered in this session will include data types, conditional & loop statements, functions, input/output, modules and regular expressions. Upon completion of this course, participants should be able to understand existing scientific python codes as well as write their own python applications.
The Gold Belt will introduce participants to tools and techniques for building and interpreting valid models for time series data using examples in the energy industry. Participants will also be introduced to fundamentals of Deep Learning, as well as Convolutional Neural Networks and Recurrent Neural Networks. Hands on sessions will focus on building CNN and RNN using the Keras library in Python.
Professor of Computer Science, University of Houston – Downtown
Associate Research Scientist, Department of Energy and Petroleum Engineering, University of Wyoming. Previously 27 years with Schlumberger, in a variety of technical, managerial and training positions.
After successfully completing the Introductory (Bronze) Belt, participants will understand how to extract knowledge from raw data and be skilled at:
- Data Exploration
- Data Preprocessing
- Machine Learning Modeling
- Evaluating Model Performance
- Improving Model Performance
Credentialing Program Prerequisites
- Rising senior in a bachelor’s degree program in engineering, technology or business with an understanding of energy industry operations such as seismic, drilling and production
- Industry Professional
Frequently Asked Questions
- An Industry Professional - Priority is given to applicants from within the energy sector
- A Student - Either in a graduate program, or a rising senior in a bachelor’s degree program in engineering, technology or business with an understanding of the energy sector - e.g., seismic, drilling and production or offshore operations
After we received your application, it will be reviewed by the admissions committee. They will notify you of their decision within two business days. If we are able to offer you a seat in the program, we will send you instructions for online payment. Your application will be accepted upon receipt of your payment.
Currently, we only accept credit card payments.
The course is delivered online as a live synchronous delivery. Some of the lectures are prerecorded in segments of roughly 20 minutes each. The recorded segments are followed by live interaction with instructors in real time. The live interaction will be used to expand on some of the topics in the recorded lectures, and to resolve any misunderstandings and/or questions that that learners may have. Numerical examples will be solved using Orange software to illustrate the principles taught and students will have an opportunity practice solving problems using Orange software.
Yes. These include weekly multiple choice quizzes and more comprehensive finals.
Yes. The classes are recorded, 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.
No. However, the later badges build on the content of the earlier ones, so it is much preferred to take the badges in the specified sequence.
If you miss a badge, you may be able to register for the badge for delivery through the recordings, and you will have to complete and pass all the homework and exams to earn the badge. 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. IBM needed to train personnel to support rapidly changing product needs. Rather than using extended training programs, they developed training for minimum skillsets, and deployed this through online media. They awarded micro-credentials to successful candidates and issued digital badges to certify the awards. 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 the Bronze 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.
Baskar Velusamy, Architect | Developer
"Always good to refresh your AI/ML skills...this time specific to oil&gas..best course for AI/ML who is in energy industry!"
Ellya Saudale, Senior Geoscientist | Geomodeller| Seismic Interpreter | Asset Evaluations | Data Analytics
"I am grateful for the assistance from great lecturers; so I am thanking for Dr. Shastri, Kalyan for the excellent lectures and course exercises."