Clean Technology Meets Data Science: News and Articles

  We've been getting a lot of questions recently from folks who are already working in clean tech fields - water, energy, agriculture, environmental consulting, climate change - about data science and what you need to get started. Some of the questions that we get asked a lot: Is all this AI and data science stuff hype? Is it really useful? How is data science different from the traditional statistical methods that we've used? I'm confused about what a data scientist does? Is that different from a data analyst? I can see the potential, but there I don't know where to start and which options are most useful to me at this stage. If this sounds like something you've been thinking, join us for a free webinar this Friday, August 16th at 11.30 am Pacific Time!

  As we saw in the last post, a data scientist's role requires the ability to  capture, process, analyze and visualize the data.  While there are some off the shelf software tools, most applications in the clean tech and data science space require knowledge of a programming language in order to perform many of the tasks effectively.  The popular choices for a clean tech data scientist are 1.   Python : Python is probably the single most critical element in the data scientist’s toolkit. It’s a flexible, easily learnt computer language that is powerful because of the large stack of libraries that have been developed. Do you need to figure out how to get data from a website – or train a machine learning algorithm? The chances are that there is an existing library in Python that can be plugged into your code.     The main libraries that are necessary for any of the data science use cases are   scipy, numpy, statsmodel and pandas . These can be used for building a predictive model using

  We've all got pictures in our mind of what certain occupations entail - and this may be very different from what actually happens! When I started my career as an environmental engineer working in a consulting firm, I had this vision of working outdoors all the time, helping to save the planet, interacting with community members who were anxious to solve the problem...... but the actual work turned out to be quite different. I spent at least half of my time in the office filing out forms and paperwork that were needed to meet regulations, analyzing data and my interactions with community members usually meant chatting with them about the samples we were collecting, if we were going to be in their way, and the weather that day. So what's a day in the life of a clean tech data scientist like? Sadly, it's not just running around the office shouting "Eureka" because you found something amazing that will help the planet - though those days do come by! Most of the time

In the last few posts, we’ve talked about the type of careers and skills that are needed in order to become a clean tech data scientist. The field is expanding rapidly right now, with openings in almost every clean tech sector and across a wide range of organizations. Just check out our   jobs portal   to see the range of positions available right now - and these are just the ones we’ve highlighted!   But how do you become a clean tech data scientist?   Since this is a relatively new field, there really haven’t been degrees or courses so far that said “Masters in Environmental Data Science” or “PhD in Data Science and Clean Technology”! Of course, this is changing with a few universities beginning to offer programs in earth systems and data science or clean technology and data science, but these are still only a few in number and started only in the last couple of years.     If you’re starting your career and are interested in this field, the universities that offer specific coursework

  Last time, we looked at the skills that someone from a clean technology background would need to acquire to become a full - fledged clean tech data scientist. Today, we’ll take a look at the other side of the balance - if you have a data science background, then how can your skills be applied in the clean technology sector?   Someone who is already a data scientist or is in the software field and looking to break into a clean technology industry or company has a very different problem from the clean tech specialist. To start with, your skills are most likely in algorithms, statistics and data engineering. That means that you can code, build prototype algorithms, build hardware/software interfaces if you’re in sensors or robotics and you know how to store and access data. You probably already have a PhD in physics or maths or another engineering domain or maybe a Masters in computer science.     So, the data science and engineering skills that the clean tech specialist would need to p

  We had a great time hosting our webinar on careers last month- a big thank you to all of you who signed up and attended and asked all those interesting questions! If you’re interested in getting the slides, they can now be downloaded for free   here .   And now, to continue with our series of posts on this topic.. What kind of careers can be built at the intersection of clean technology and data science? The figure above shows the sectors in both clean technology and data science.  The kind of career you want to build will really depend on which end of the balance you’re sitting on. Are you someone who comes from a clean technology background and wants to pick up data science skills? Or are you someone who has a strong data science/software background and are interested in applying those skills to problems in clean technology? This will also govern the skills you will need to pick up or expand on as you build your career. If you’re coming from a clean tech background, you’re probably

  Since our last post discussing the market demand for clean tech data scientists and the potentially high growth in the years ahead, we've been getting a lot of questions from our readers about jobs, careers, salaries and how to get hired. Since most of the answers are going to be longer than a single blog post, we've decided to host a webinar discussing Careers in Clean Technology and Data Science.  The webinar will be held on April 26th, so please register   here   to reserve your spot. And if you do miss it or can't make it, don't worry! We'll host another one later in the year and in the meantime, we'll have the slides from the presentation available for download.

  We’ll be doing a series of posts discussing careers at the intersection of data science and clean technology - what kind of jobs are there, what career paths do they lead to, where can you work and what are typical salaries in this field.   The market for clean technology and data science is still in its infancy, but growing rapidly.   The market is divided between several sectors (energy, agriculture, water, climate change) to name a few  and each of these sectors has a market size ranging from multi-million to several trillion dollars.   As data science, including the use of sensors, machine learning, imagery and statistics, penetrates each sector , the market for clean technology and data science becomes correspondingly large. In fact, it’s been   estimated that we could be looking at a market size   between   100   billion to 6 trillion dollars   worldwide by 2025 . To put that in perspective, the software industry in the US has a market size of close to $2 trillion dollars today

  Several interesting research studies have been written highlighting how data science is being embedded in clean tech. What I find fascinating is that these stories showcase research at very different scales – at the micro level and at the scale of the Earth System. The first study that came out this week was by teams from the University of California, Irvine and NASA’s Jet Propulsion Laboratory evaluating the melting of Antarctica’s glaciers. The teams used satellite data to monitor the location and movement over the years of the glaciers “grounding line” – the point at which the glacier begins to float while still attached to the land. The reason that’s important is because it helps determine how much ice is melting into the oceans – with all the associated implications for understanding sea level rise in a changing climate. The recent data (2014-2016) came from the Sentinel-1 mission launched by the European Space Agency and the previous years data (1992,1996 and 2011) that was use

  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