Launch Announcement: Our hands-on, virtual workshop series begins Sunday

 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.

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.

What our community are reading

Moonshots, Models, IoT and Machine Learning in Agriculture

  What do Google, Climate Corporation, early stage startups in farm robotics, and researchers trying to figure out how to feed the world sustainably have in common? They’re all grappling with one of the toughest challenges of working with natural systems - how do you work with data that is sparse, unevenly distributed and with systems that have so many connections and interactions with other systems?     Before the advent of cheap sensors that are connected to phones, easily accessible satellite data and drones that can fly over fields quickly and inexpensively -     scientists in companies and academia worked on developing plant and crop models that incorporated as many aspects of the farm and as much data as was available so that they could understand and predict what was likely to happen on the field. Understandably, the forecasts took some time to produce and as the models grew more complex, issues about how to estimate model parameters and the uncertainty associated with the resul
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Our online community space is now live!

 Our online community space is now open to anyone who has signed up for a free or paid course on our website! In addition to everyone who signed up for our cohort-based courses, we're now expanding it to all the members of our community. If you've already signed up for any of our courses, check your email for the invitation for the space. It's where we'll get together to talk about all things data science and clean technology related, discuss the latest research, network and make connections with other professionals in the sector. It's an invitation only , no bots and no trolls allowed space - so come on over! Here's where you can check out our courses and join our community !
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How much water should an email consume? Data centers and water use

  A mid-sized data center consumes around 300,000 gallons of water a day, or about as much as 1,000 U.S. households; About 20% of data centers in the United States already rely on watersheds that are under moderate to high stress from drought and other factors; Operating a data center often requires a tradeoff between water use and energy use; And in a survey of 122 data centers in the United States, only 16% or 20 utilities reported plans for managing water-related risks. As professionals working in the field, what can we do to solve this issue? One aspect is developing and using water models that can identify water risks at different scales - so that we can predict the risk to water supplies under a changing climate. A second is using machine learning to identify and optimize water use between all the stakeholders in the watershed - data centers, farmers, cities, other industries - so that biases and needs are brought out into the open and the key issues identified. A third, of cours
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