Clean Technology Meets Data Science: News and Articles

  Today, let’s look at some of the ways in which sensors, machine learning and other data science tools are helping solve problems in the water sector Water conservation   is one of the largest components of the water sector. And with the droughts hitting California, New South Wales and South Africa recently, there’s been a lot of focus on education and changing people’s behavior through information.  So how do we make it easy for people to do their best to minimize water use?   Companies and organizations have tried several different approaches – public education programs about water use being one such example. Another being the water comparison report that water companies in California started sending out to their customers with the monthly bill. It’s a short summary that shows how your water usage compares with your neighbors and how much water you have used against your allocation. This is a fairly straightforward use of data science – it uses a nearest neighbor approach with a bui

  In today’s post, we’ll take a look at a few problems in Agriculture and Food that are being solved using machine learning, computer vision, social networks and satellite data among other data science tools. What do social networks, sensors, food and farms have in common? Social media immediately makes us think about Facebook, Snapchat, Instagram, Whatsapp, Google and all the different ways in which we human beings connect with each other today. All these apps use Graph and Network Theory to understand how people may be connected to each other, the links between them and how strong or weak those connections are. Reid Hoffman of LinkedIn famously said that “we’re all six weak connections apart from each other” and in today’s connected world, that number looks like it’s getting smaller and smaller. So what would social media have in common with agriculture? First, there’s always the way in which farmers and workers interact and connect with each other.   Several startups like the Farmer

  Over the next few months, we’re going to take a quick look at examples of problems in clean technology where data science is used to create solutions. This is our “snippets series” for the year and an introduction for folks who are interested in a flavor of what data science and clean tech can do together. This series of posts will focus on several clean tech sectors – starting with the Agriculture and Food sector.

  An interesting place to look at how the ecoinformatics market is evolving is to take a look at the startup scene in this space. What’s really interesting about the clean tech and big data space is that innovation happens at all levels – startups, national and state government institutions, cities, large companies, non-profit organizations, just to name a few. People and organizations are working to solve problems at different scales and that drives innovative solutions in all the different clean tech sectors. For example, the Nature Conservancy is doing some really interesting work in looking at how remote sensing and machine learning can be applied to wildlife and conservation. Similarly, cities in California are experimenting with smart meters and social media to improve water conservation.   However, let’s look at startups in the space as surrogates for estimating the market. And as expected, a picture emerges of a few sectors that are being heavily funded. The   CleanTech Group  

  Here we’re going to take a look at one of the smaller sectors in clean tech and look at how data science is making a huge impact. This is just an example of how the market is likely to evolve for the ecoinformatics sector.   The environment has often been seen as nothing more than an unlimited source of materials that are then turned into useful products that human beings can use. While this viewpoint was challenged in the latter half of the 20 th century, there still remains a sense that the environment is more of an after thought for business rather than something that has value and contributes to a business.   So it’s really interesting, especially in our current climate, to see how investing in conservation and ecosystems has performed. The   Ecosystem Marketplace    put out a report showing that   private investment in conservation has topped $8 billion globally  in 2016 and that   investors are expecting a rate of return of at least 5% and in some cases of 10% and more . Compar

  This month we'll be talking about the market for Ecoinformatics or the intersection between Clean Tech and Data Science! To start with, let's look at some numbers for each sector independently. The clean technology market size has been variously estimated at between   2 and 6 trillion dollars   over the last decade. A recent study published by the World Bank showed that the market size in   2012   was estimated at approximately   $5.5 trillion . The main sectors are shown below (by percentage). In short, energy (solar, wind, energy efficiency, alternative fuels), agriculture and water make up the largest part of the clean tech scene with energy being the dominant player at this time. Each of these sectors is a multi-billion dollar sector in itself, so it isn't hard to see that data science use cases for each of these sectors will also constitute a multi-million dollar market at the least, if not more.   The market size for data science is harder to estimate since it can c

  The Harvard Business Review   called the job of a Data Scientist “the sexiest job of the 21 st century”. It’s a field that been booming in the last five years or so, with applications in multiple sectors – from finance to health to computer science to space and more recently to clean technology. Data Science or Big Data   as it’s sometimes being called is a relatively new term that refers to processes and methods to generate, process and develop insights from data. With more people coming online these days and generating large volumes of data, we’ve had to use methods and techniques from a wide range of disciplines to handle the speed, volume and variety of the data and understand what the data are telling us. That’s where the whole concept of “Big Data” or Data Science has come about – it’s a multi-disciplinary field that uses a basket of methods and tools from different fields, especially statistics and machine learning. Some of the topics I talk about in my list below are now acce