Announcement: Our "Introduction to Machine Learning and Data Science in Water" course is now live

 We're delighted to welcome our first ever cohort to our "Introduction to Machine Learning and Data Science in Water" course. If you're in the water sector, are curious about machine learning and data science and not sure how to get started using it in your work - join our course and we'll give you the tools!

This is the first time we're doing a 10-week, cohort-based course and we plan on doing more of them in the future. So, what does our asynchronous, cohort-based course do for you?

One of the challenges with completing courses online is that it's so easy to sign up for one, get busy with life and work, fall behind and then give up on finishing the course that you signed up for. Our cohort-based and asynchronous approach helps you overcome that. The course material is online - so you get to complete it and review it when you get the time. However, we also have weekly office hours that are live - so you get to ask your questions to our experts, have them walk you through the key concepts and get you caught up with the course material if you fell behind that week. What's more, you get the accountability that comes with being part of a cohort of students - you get to work on your project with your peers and we keep you on track with meeting your goals. And once you're done, you'll be part of our online community space - where you can ask questions at any time, have discussions about data science in water and other clean technology sectors and meet other professionals in the space.

Still not convinced? Join us for a 1-hour sneak preview into the course and learn more about how data science and machine learning can be used effectively in the water and wastewater sector. 



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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