News: Water Tech, AgTech and more about our new, redesigned platform

We've been in "building mode" here at Ecoformatics for the last few months and are delighted to announce our new products!

First, welcome to our new, redesigned website. We've consolidated all our products and services under a single platform with Teachable to make it easier for our members to access all our content. As a reminder, your existing account for courses and webinars will still work on our new website.

Second, our live events will be back starting January!  Our events will continue to be primarily virtual, and we're adding a new networking aspect to all our events. We are looking forward to talking about data science, machine learning and sensors in different clean technology sectors with professionals from around the world!


And finally, we’re excited to announce our new products - the WaterTech track and the AgTech track. These are our first tracks where we focus on data science in a particular clean tech sector - specifically the water and wastewater sector and the agriculture and forestry sector. As many of you know, so far we’ve been looking at data science tools and techniques across a broad range of clean technology sectors, and how these different sectors interact - like the food-energy-water nexus. 


With our new tracks, our goal is to help professionals working in these specific sectors to go from “I’ve never coded, I hate statistics and I’m not sure how useful machine learning and AI are to my problems” to “I can now build useful machine learning models, I understand when and how to apply data science, and I can successfully build and manage a team that can use these new technologies in my company and sector”.


We'll be launching our pilot courses for these tracks in a few weeks, so stay tuned!

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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