Coming this Sunday, September 20th: Bayesian networks in clean technology live, virtual workshop

 How do you find out why new technologies are being adopted? How do you find the early adopters and figure out why they are using these new technologies?

 

As startups and individuals build new tools and applications in agriculture, water, energy, sustainability, forestry and climate - some of the the biggest questions they face are understanding who is likely to adopt these technologies, the parameters governing these decisions and how they interact with each other. 

 

So, how can this be measured and modeled quantitatively? Welcome to the wonderful world of Bayesian networks! 

 

Bayesian networks are powerful machine learning algorithms that allow us to model how different aspects of a problem are interacting with each other, estimate how likely it is that someone will choose to do something like buy a new technology, account for the uncertainty inherent in problems in clean technology where we don’t know all the parameters and values associated with them - and solve a whole suite of similar problems. 


If you’re curious about these algorithms, how they’re used in clean technology, how we can build them using Python - come and join us this Sunday, September 20th at 11 am-12.30 pm Pacific Time for a live, virtual workshop. It’s a gentle introduction to probability, Bayes’ theorem and how it works in clean tech for everyone who’s interested in these topics, followed by a hands-on problem solving session in Python where we’ll model how new technologies in agriculture like drones, crop and soil maps and smart sensors are being adopted by different users in the United States.

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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