Course description
The course presents and discusses data science techniques and their applications in the exploration, mining and sustainable use of raw materials and energy resources in the XXI century and beyond.
The course focuses on the relation of data science and the exponentially growing volume and diversity of (multi-source, multi-sensor) data, on one side, and the trends of the disruptive technologies and forces shaping the exploration, mining and use of raw materials and energy resources, on the other – including political risk, market volatility, climate change, circular economy, the companies’ social responsibility and social license to operate, the world’s population demographic and available income growth and the growing complexity and increased access difficulty to mineral and energy resources.
The course aims to present a 360º perspective of the challenges the society will face in the next century in the supply and use of raw materials and energy and the technologies used to overcome them.
The course is based on a heavy use of practical cases – analyzed and discussed by the students as a means to explore ways to solve real problems, question the methods’ underpinnings and consolidate the grasp of their theoretical and methodological basis. Students are encouraged to bring their own data and problems to discuss during the course.
Course content
The course is presented in six modules (three days). Each day's activities are divided in two parts: one dedicated to the analysis and discussions of state of the art and trends in XXI century society, raw and energy resources and data science technologies; the other dicated to the analysis of specific data science methods and application cases.
Day 1
Morning: Classical and unconventional raw materials and energy deposits. Resources, reserves, risks and markets - methodologies and international standards and guidelines.
Afternoon: Data Science - General model. Unknow resources estimation
Day 2
Morning: Energy transition, climate changes, raw materials and circular economy. The society and mineral and energy resources: transparency, sustainability, social responsibility and waste.
Afternoon: Production control. Pricing.
Day 3
Morning: Digitalization, IoT, Autonomous Vehicles, 5G, Blockchain. Deeper, further away: deeper into the crust, the ocean bottom, Moon, Mars and the asteroids
Afternoon: Clustering. Time series. Text mining.
Type of training
The course is combining classroom lectures, computer-based work, group work and self study.
Objectives and outcome
Participants in the course will be able to inform and design long-term strategies:
- On raw materials and energy resources acquisition for the XXI century and beyond.
- To implement data science and other big data-based technologies in raw material companies.
Language
English