School: Digital Transformation and Data Science in the Energy and Industrial Sector

24-28 Aug
Activity
2026

"Digital Transformation and Data Science in the Energy and Industrial Sector" is a dynamic, hands-on course designed to equip students and young professionals with the critical skills to lead in an era of AI, data-driven decision-making, and technological innovation. Led by Mariana Kobayashi, this 15-hour program blends real-world industrial case studies with practical training in Python, Power BI, and machine learning.

 

 

The school will take place at the East African Institute for Fundamental Research, Einstein Building (fifth floor), Science and Technology Campus, University of Rwanda. This school is sponsored by the TotalEnergies Professeurs Associes (TPA).

 

To register: please send an email to admin-assist@eaifr.org.

Registration is free.

Dates: 24-28 August, 2026 

Time: 9:30-4:00 pm

Venue: EAIFR Conference Room, Fifth Floor of the Einstein Building, UR CST Campus, Kigali.

 

Course Outline:

  • Introduction to Digital Transformation
  • Overview of digital technologies and their impact on efficiency and productivity
  • Main Technologies
  • Robots, drones, 3D printing, IoT, 5G, virtual reality, augmented reality, Big Data, Machine Learning
  • Real-Life Examples: Case studies and practical applications of these technologies in the energy and industrial sectors
  • Data Science Applied to Industry
  • Understanding Data Analysis: Exploration of data collection, treatment, organization, storage, and analytics
  • Understanding Machine Learning & Artificial Intelligence: Introduction to ML models, clustering/classification, predictive analysis, regression, and qualitative analysis
  • Study of ML & AI Use Cases in the Industry: Exploration of machine learning and artificial intelligence applications in industrial processes
  • Process Modeling and Optimization: Understanding different types of process models and their applications in design, operation, and optimization
  • Digital Twin Concepts
  • Hands-On with Data Analysis Tools
  • Exercises in Python for Data Analysis and Machine Learning: Hands-on exercises to apply data analysis and machine learning techniques using Python
  • Discover Business Intelligence with Microsoft Power BI: Introduction to no-code tools and Power BI for data visualization and business intelligence
  • Innovation process in the industry and project management
  • What is innovation?
  • Emphasis on fostering an innovative culture, open innovation, and the role of various actors in the innovation ecosystem
  • Innovation Challenge Hands-On Project: Practical projects to apply data science and innovation concepts to real-world problems
  • Agile Software Development: Principles of agile methodology, including MVP (Minimum Viable Product) and typical agile sprint processes

Duration: The course spans around 15 hours and can include practical exercises using Python, Power BI, Orange Data Mining, Innovation challenge (Ideation and solution definition). The agenda can be flexible according to the university and students’ needs.

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