04-782   Energy Data Analytics

Course description

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.

Learning objectives

• Critically evaluate the landscape of energy data sources relevant to sub-Saharan Africa, including utility data, energy resource data, geospatial datasets, household surveys, and economic data
• Apply Python-based data analytics workflows to real energy datasets, from raw ingestion through to cleaned, analysis-ready formats
• Use statistical and machine learning methods to model energy demand, generation, and consumption patterns across on-grid and off-grid contexts
• Conduct geospatial analysis to support electrification planning, resource mapping, and infrastructure siting decisions
• Analyze the economics of energy technologies and productive use applications
• Communicate data-driven findings effectively to technical and non-technical stakeholders, including regulators, funders, and community organizations
• Situate analytical outputs within the institutional, regulatory, and financing frameworks that govern Africa's energy sector

Outcomes

Technical skills

By the end of this course, students will:

• Gain proficiency in various python libraries used for data analytics including pandas, numpy and matplotlib.
• Be able to clean and structure real world datasets
• Be able to work with various types of datasets that are common in the energy sector including time series data, survey data, and spatial data
• Master competency in geospatial data analysis — handling raster and vector datasets and producing maps for electrification planning

Domain knowledge

• By the end of this course, students will:
• Understand the structure, challenges, and data ecosystems of African energy systems
• Be familiar with characteristics of electrical load and energy demand
• Have knowledge of energy access and clean cooking metrics, fuel stacking behavior, and the data methods used to evaluate energy transitions
• Understand renewable energy resource assessment and the role of data in project development
• Understand the spatial aspects of energy systems including network topology, spatial energy planning considerations, and distribution of energy resources
• Have knowledge of the financial and economic underpinnings of energy systems on the African continent

Professional skills

By the end of this course, students will:

• Be able to scope, execute, and present an independent data analytics project on an African energy topic
• Be able to translate analytical findings into policy briefs, donor reports, and stakeholder presentations

Content details

• Demand analysis and load profiling
• Renewable energy resources assessment
• Geospatial data for planning of energy systems
• Survey data analysis
• Power systems and grid data analysis
• Energy economic and financial data

Prerequisite

Knowledge of the Python programming language. Experience with common libraries like pandas, umpy, and matplotlib is valuable. No background in energy systems is assumed.