04-782 Energy Data Analytics

12 units, Carnegie Mellon University

The energy transition in Africa presents significant challenges but also opportunities for sustainable development on the continent. Data is poised to play an important role in ensuring that this transition is realized in an equitable, efficient, and timely manner. This course equips graduate students with the analytical frameworks and practical Python-based tools to extract actionable insight from energy data in African contexts. The course is designed for two complementary audiences: energy professionals and engineers seeking to build rigorous data analytics capabilities, and data scientists and IT professionals seeking grounding in energy systems, policy, and sector-specific data challenges. Students from both tracks will work on shared applied projects, reflecting the interdisciplinary nature of real-world energy analytics work. Topics span the full analytics pipeline — from data acquisition, cleaning, and exploratory analysis through to time series modeling, geospatial analysis, machine learning applications, and data-driven policy communication. All analytical work is grounded in African energy datasets, institutions, and policy frameworks, with case studies drawn from across Africa.

Prerequisites: none listed

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