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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What’s the impact of your smartphone? Mapping the distribution and environmental impact of mines around the world

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How many different materials go into making your smartphone? According to the American Chemical Society, at least 64 different types of chemicals constitute your smartphone . Many of these materials, particularly the heavy earth metals, are extracted from the Earth through mining operations. Mining has been a part of human society for several thousand years now - from the shallow mines of earlier centuries to the more extractive ones created in the last hundred years. The challenge of mining and other extractive industries is that they are essential to many of our needs today - coal for energy, silicon for solar panels and heavy earths for electronics being among the well known ones. At the same time, they cause significant damage to local environments through the disturbance to the land caused by establishing the mine itself, to environmental impacts on water, land and ecosystems from mining operations.   So far, it’s been difficult to map the spatial extent and environmental impact o
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