Did you ever want to use data science to solve problems in energy, agriculture, climate, water, forestry, environmental remediation and other clean technology sectors? And wasn’t quite sure where to start or how to adapt existing algorithms for these sectors?
We are launching a series of hands-on, virtual workshops where we use real world problems and datasets to introduce different aspects of data science for clean technology. We’ll cover remote sensing, spatial statistics, building prototypes, effective visualization techniques, and adapting different machine learning algorithms such as clustering, neural networks, deep learning and genetic algorithms among other topics.
At the end of this series, you’ll be able to generate and access different sources of clean technology data, use a wide range of data science tools and machine learning algorithms in clean tech sectors from agriculture and water to energy and smart cities, build prototypes, and visualize and present your results effectively.
We’ll have 1-3 live sessions every month that will also be available as online courses.
You can sign up for individual workshops or for the entire series through our monthly subscription plan here
On Sunday, May 17th, we will be conducting our first live workshop on "Introduction to Spatial Data Analysis".
A lot of data in clean technology and sustainability is spatial in nature. So, if you've ever wondered how spatial data is different from other types of data, what cluster analysis and hot spots are, and how to get started with analyzing it, this workshop is for you.
And since it's Covid time, the hands-on problem is using air pollution datasets from satellites and ground sensors to analyze the impacts of the lockdowns on air pollution levels at different scales around the world.
If you're interested, the link to sign up for this workshop is here.