PrintAll sectors of the energy industry and related fields continuously use data to inform decisions. The underlying datasets are becoming increasingly large and complex, as well as commonplace. This course aims to equip students with the data management, manipulation, and interpretation skills to be successful in their future careers. By taking this course, students will learn to:
- identify different types of data and organize them into conventional structures;
- draw statistical inference from data and report conclusions based on this inference;
- conduct statistical simulations in the context of inference and uncertainty quantification (risk analysis);
- make data-driven predictions with regression modeling and machine learning;
- make data-driven classification with Bayes rule and machine learning;
- present their results both graphically and in writing, so as to honor the underlying data and limitations of the analysis; and
- execute all of the above in a modern computing language (e.g., Python).
Additionally, students will develop a conceptual understanding of confidence intervals, hypothesis testing, basic experimental design, regression modeling, and some select machine learning topics. Instruction and assignments in the course utilize real datasets from various energy-related fields. No prior coding experience is necessary, and no purchase of software is required.
Students who register for this Penn State course gain access to assignments and instructor feedback, and earn academic credit.