What's new in 2020 at Ecoformatics?

 Here's wishing all our readers a very happy new year!  Last year, we started our journey to making data science accessible for people interested in clean technology and solving the problems facing our planet. 


We had the pleasure of conducting several workshops and online webinars on different aspects of data science in clean technology. We covered a wide range of topics in our in-person workshops where we discussed data sources in different clean tech sectors, how to build effective algorithms and models including deep learning, and presented uncertainty analyses and business use cases. We also began conducting online sessions in the latter half of the year where we introduced folks to careers and tools at the intersection of data science and clean tech. 

As part of our expansion plans in 2020, we're creating an online education platform that focuses on applying data science effectively in clean tech sectors. We're in beta this month and are building our content which will consist of live workshops, online courses and reports . Right now, we're offering a bundle of our existing courses and reports for free to all our beta users (a $30 value). We'll be adding more content every month, including access to new webinars and live workshops, so stay tuned.

If you think that having access to our courses and other educational and informational content would be useful, please sign up here as one of our beta users!

Our platform is hosted by Teachable, which we chose primarily for their commitment to user privacy and the glowing reviews from other students on how easy it was to use. We'd love to hear what you think about it, so please let us know!

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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