This interdisciplinary certification is designed to equip engineering students with practical skills in digital platforms, artificial intelligence (AI), and big data analytics, specifically tailored to the evolving needs of the energy sector. The program aims to prepare graduates to thrive in a multidisciplinary environment where data-driven decision-making, real-time resource allocation, and digital innovation are reshaping traditional supply chain models.
Digital platforms have revolutionized the energy supply chain by facilitating seamless exchanges between suppliers and consumers, reducing transaction costs, and enabling more efficient resource management. While these technologies enhance operational efficiency and broaden access to services, they also present new societal challenges in workforce dynamics, environmental impact, privacy, and equity. This certificate program addresses these complexities by integrating technical training with a critical understanding of the broader implications of digital transformation in the energy industry.
Who Should Take This Certificate?
The target audience for the Graduate Certificate in Big Data and Energy Supply Chain includes a broad range of individuals seeking to enhance their expertise at the intersection of digital technology and the energy sector. This includes current graduate students in engineering disciplines such as industrial, mechanical, petroleum, electrical, and chemical engineering who wish to specialize in data-driven energy systems and supply chain optimization. The program is also well-suited for working professionals in the energy, utilities, and chemical industries who seek to upskill in digital platforms, AI, and big data analytics to remain competitive and advance into leadership roles. Additionally, it targets technical professionals and career switchers from fields such as computer science and IT who are looking to transition into energy-focused roles. International students and global professionals from energy-intensive regions will benefit from the program’s relevance to U.S. energy infrastructure and digital innovation. Finally, the program appeals to public sector employees involved in energy planning and regulation, as well as academic researchers and educators interested in applying big data to energy systems.
Admission Requirements
Educational Background
Applicants must hold a bachelor’s degree in engineering, computer science, information systems, applied mathematics, or a related STEM field from an accredited institution.
Minimum GPA
A minimum cumulative GPA of 3.0 (on a 4.0 scale) in the last 60 hours of undergraduate coursework is typically required. Applicants with a lower GPA may be considered based on professional experience or supplemental materials.
Prerequisites (can be waived based on experience or prior coursework)
- Basic knowledge of statistics or probability
- Introductory programming skills (e.g., Python, R, or Matlab)
- Familiarity with linear algebra or optimization is preferred but not required.
Work Experience (optional but recommended)
Professional experience in the energy sector, data analytics, supply chain management, or digital platforms is encouraged, especially for applicants not currently enrolled in a graduate program.
Curriculum
Core Courses
These are required courses:
- INDE 6334 — Predictive Data Analytics
A predictive analytics course focuses on principles, techniques and applications of predictive data analytics, designed mainly for graduate students who have basic knowledge of statistics. It primarily focuses on the ideas, algorithm implementations and applications of predictive data analytics methods and how to leverage them for analytic decision-making in practice. - EDS 6342 — Introduction to Machine Learning
Concept learning, hypothesis spaces, decision trees, neural networks, Bayesian learning, computational learning theory, instance-based learning, genetic algorithms, rule-based learning, analytical learning, and reinforcement learning.
Core Elective 1
Choose one of the following courses:
- SCLT 6314 — Measurement and Evaluation of Supply Chain Operations
Assessment techniques, performance analysis, cost/trade-off evaluations and other methods to optimize Supply Chain Operations. - SCLT 6316 — Global Supply Chain Logistics
Optimize supply chain activities at a global level. Planning methods, implementation techniques, process factors, outcome interpretation, and other activities necessary to achieve optimum results. - INDE 6361 — Prod Planning & Invent Control
Principles and techniques of production planning & inventory control; demand management, forecasting, sales, and operations planning, master production scheduling, material requirements planning, capacity planning and management, production activity control, capacity management, just-in-time, distribution requirements planning, order point inventory control methods and enterprise resource planning. - INDE 7390 — Supply Chain Management
This course provides an overview of the supply chain networks that bring energy from sources to customers, including operations and information management requirements. Students will gain a basic understanding of the various aspects of the Energy Supply Chain, including crude oil, natural gas, and electric power supply chains.
Core Elective 2
Choose one of the following courses:
- LAW 5355 — Oil and Gas Law
Covers the basic property, contract, and regulatory framework for oil and gas production in Texas. Explores common law property concepts; the provisions of an oil and gas lease negotiated between a mineral interest owner and an oil company as lessee; and also examines Railroad Commission regulation of drilling, production, pooling, and unitization for the efficient and fair development of oil and gas. - SCLT 6318 — Supply Chain Strategies
Understanding the role of strategic planning to optimize supply chain activities. Planning methods, implementation techniques, process factors, outcome interpretation, and other activities necessary to achieve optimal results. - PETR 6351 — Introduction to Petroleum Engineering
Petroleum origin and migration, major oil and gas fields, drilling and production methods, petroleum composition and phase behavior, reservoir engineering methods of oil resource estimation and optimization. - ECON 6345 — Energy Economics
Energy economics with applications: Markets and market structures including the effects of regulations; sources; substitutes; externalities; data analysis and policy. - INDE 6360 — Engineering Analytics
Method and application of analytics techniques in statistical learning, machine learning and predictive modeling. The course covers a variety of analytic techniques including regression, classification, tree-based methods, model assessment and selection, prediction enhancement, system modeling and identification, and high-dimensional statistical modeling. - FINA 7371 — Energy Value Chain
The nature of energy assets, operations and products produced, and the economics of each component of the EVC.
Additional Information
For more information, please contact:
Dr. Gino Lim
Professor & Chair
Email: ginolim [at] uh.edu (ginolim[at]uh[dot]edu)