Data, Models and Machine Learning in the Energy Sector
Get started with energy analytics and using data science in the energy sector
What is Energy Analytics? How is data science used in the energy sector, what machine learning and statistical algorithms are the most useful, how can they be applied, and where can you find the data to build your models?
If you've been curious about a sector that's expected to reach $170 billion in five years and is growing exponentially, or if you want to take your skills to the next level and get started using data science in the energy sector, this is the course for you!
We'll walk you through the areas and concepts in how data science is used in energy, identify useful data sources and step through an example problem where we'll explore a dataset from buildings that will allow us to identify opportunities for improved energy accessibility and conservation.
1. Access to a computer with QGIS and Python version 3, the Anaconda installation. If you need help installing Python or QGIS, we walk you through the process in our free course "Install Essential Data Science Tools for Clean Technology".
Gayathri is an award-winning scientist who has been pioneering the integration of clean technology and data science since 2003. She worked in large organizations, including Facebook, Argonne National Laboratory, several Silicon Valley startups and is the recipient of numerous national and international awards. As a scientist, her work has been recognized by the US National Academies of Science and Engineering for innovative environmental research and she was invited by the White House in 2014 to participate in the conference on innovation in clean technology and big data. As an engineer and data scientist, she built the data infrastructure, including data pipelines, dashboards and algorithms, for several startups. She received her PhD in Environmental Engineering from the University of Illinois at Urbana-Champaign and her Bachelors in Civil Engineering from BITS, Pilani, India.