Upstream Energy Data Analytics Program - University of Houston
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  • Pricing

    Individual Badge Price: $900 each | Badge 4 & 5 Package: $1,700

  • Schedule

    Dates: Introduction to Python: August 22 | September 12

    MON & WED | 6:00pm - 8:30pm

Executive Summary

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 workforce shortage in skilled data sciences with an understanding of the energy industry.

For this reason UH Energy, at the University of Houston, has developed the Upstream Energy Data Analytics Program, to equip current and aspiring professionals in the upstream oil and gas industry on data analytics concepts and hands on experience on applications with real world examples.

UH Energy delivers the program in collaboration with UH Departments of Earth & Atmospheric Sciences and Petroleum Engineering, and the HPE Data Sciences Institute at UH, and with subject matter experts from industry.

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PROGRAM OVERVIEW

The oil and gas industry is undergoing significant changes, including repositioning operations to be cost-competitive in a world of rapidly changing 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 oil and gas industry. Many oil and gas companies are already finding ways to implement data sciences solutions and are realizing tangible benefits, including 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 workforce shortage in skilled data sciences with an understanding of the energy industry.

For this reason UH Energy, at the University of Houston, has developed the Upstream Energy Data Analytics Program, to equip current and aspiring professionals in the upstream oil and gas industry on data analytics concepts and hands on experience on applications with real world examples.

UH Energy delivers the program in collaboration with UH Departments of Earth & Atmospheric Sciences and Petroleum Engineering, and the HPE Data Sciences Institute at UH, and with subject matter experts from industry.

Who should attend?

The Upstream Energy Data Analytics Program has been designed with two distinct groups in mind:

  • Those of you who are already in the upstream industry and facing challenges daily in making operations more efficient while continuing to grow the business. The Upstream Energy Data Analytics program will enhance your capabilities to get deeper insights from the data that you deal with, facilitating finding solutions to above challenges.
  • If you are readying yourself for an upstream career, you need to prepare yourself for a dynamic, exciting and challenging professional life. The Upstream Energy Data Analytics program provides a unique perspective and a ready to apply practical skillset, giving you a competitively advantaged competence as you enter the upstream oil and gas marketplace.

Learning Objectives

After successfully completing the Introduction to Python and Python Data Analysis badges, participants will understand and be skilled at:

  • Python Programming
    • Data Import/Export
    • Data Types
    • Control Statements
    • Functions
  • Reusable Data Analysis Models
  • Writing programs to facilitate discoveries from Data
  • Basic data processing
  • Data Visualization

Pricing

INDIVIDUAL BADGE PRICE:

900

EACH

BADGE 4 & 5 PACKAGE:

1,700

Credentialing Overview

The course is offered in 15-hour modules, each over a 3-week period. Digital badges are awarded for each module. Badges will be a permanent addition to your skillset and resume.

Introduction to Python

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
  • Regular expressions
Introduction to Python

Python Data Analysis

Participants will learn how to train and evaluate machine learning models using Python and Jupyter Notebook as the Integrated Development Environment. Hands-on sessions will be based on public upstream oil and gas datasets. Topics covered in this session include using:

  • NumPy
  • Pandas
  • Concepts of Data Wrangling
  • Plotting and Visualization
  • Data Aggregation
  • Modeling libraries
    • Patsy
    • Statsmodels
    • Scikit-learn
Python Data Analysis

Instructors

  • mark-darby.jpg

    Dr. Mark Darby

    Independent Consultant

  • michael-nikolaou-web.jpg

    Dr. Michael Nikolaou

    Professor, Chemical and Biomolecular Engineering, University of Houston

  • guan-qin-web.jpg

    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

  • radha.jpg

    Dr. Suryanarayanan Radhakrishnan

    Clinical Assistant Professor in Decision and Information Sciences, Bauer College of Business, University of Houston

  • dvijesh-shastri-web.jpg

    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

  • kalyan-venugopal-web.jpg

    Dr. Kalyan Venugopal

    Associate Research Scientist, Department of Energy and Petroleum Engineering, University of Wyoming

Credentialing Program Prerequisites

  • Basic knowledge in computer devices.
  • Background in machine learning is encouraged but not required.

Accelerating Credentials of Purpose and Value (ACPV)

Did you know?

UH Energy is working with UH’s Department of Petroleum Engineering, UH’s Department of Earth and Atmospheric Sciences, UH Clear Lake and UH Downtown to accelerate the integration of the Upstream Energy Data Analytics Program with the formal degree offerings at each of these institutions. To learn more about this please visit Accelerating Credentials of Purpose and Value

Frequently Asked Questions

The first step is to complete the online application
UH Energy will review your application to ensure that you satisfy the minimum requirements to register. For initial Badges, this means that you should either be:

  • An Industry Professional - Priority is given to applicants from within the energy sector, with experience in upstream oil and gas operations
  • 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 upstream oil and gas operations such as seismic, drilling and production

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. Each Badge is covered over two weeks. At the end of the first week, students will have an online multiple choice test for homework, and will have to achieve a passing grade to proceed to the next week. If one does not pass on the first attempt, one can review the course material and re-take the test as many times as one wishes. At the end of the second week, there will be a final exam for the Badge. One needs to get a passing grade to move on to the next Badge. The same rules for passing apply for the final as for the test held at the end of the first week.

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.

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 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. 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.

If you cannot find the information you need on the webpage or in the other FAQs, please contact: