July 19th, 11am Pacific Time: Getting Started with Machine Learning in Clean Technology - Live Workshop

 It’s hard to believe that July is already here - what a year it’s been so far! When the pandemic started we were just beginning to launch our series of online courses and workshops to a small community of professionals who were interested in applying data science to solving the problems facing our planet. Our community has been growing every month that we’ve been doing these courses and workshops - and this month, we're doing a workshop on a topic that many of our members have asked us about - Machine Learning in Clean Technology.

If you’re interested in learning about machine learning in clean technology - the problems and use cases in clean technology, what algorithms are deployed most often, how machine learning in clean tech differs from other common applications in high tech, and how to get started - join us on Sunday, July 19th at 11am -12.30 pm Pacific Time.  You can sign up for the workshop here or on our courses page. 

So, how do our workshops and online courses work? The workshops are virtual, live sessions where we introduce the material and the concepts in the topic and then work through a real-world example that illustrates what we’ve been talking about. We’ve heard from our attendees that working with us on a real problem clarifies the concepts, gets them started quickly using datasets and software that is used in the field and helps build their confidence. We then convert the workshop into an online course so that every one who’s signed up gets access to the material and can work through it at their own pace and time - even if they weren’t able to make it to the live session. Of course, the real-world problem, together with the datasets are included! 

We started the workshops as one-off sessions, but based on the requests we’re getting from the community, we’re now going to expand these to an entire series of live workshops, online courses and reports. Starting August, we’ll be doing 2 live workshops every month, with more workshops and reports to follow. So far, we’ve done introductory courses on spatial data analysis, remote sensing and machine learning - we’ll be doing deeper dives into these and more topics on applying data science in clean technology.

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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