Clean Technology Meets Data Science: News and Articles

  We had a great time hosting our   "Getting started with Data Science for clean technology professionals" webinar  - a big thank you to everyone who registered and asked all those interesting questions!  If you're interested in getting the recording, it's now available for download on our website. And now, back to our regular posts on how data science is used in different clean tech fields! There's been a lot of news about the wildfires in the Amazon and the consequences for the planet, so let's talk about wildfire detection and how it's done. Wildfires, and in particular the Amazon fires, are detected using data from a wide range of satellites - NASA's array, the EU's Copernicus, Brazil's Terra satellites, Japan's Himawari-8, and CubeSats among others. But what exactly do these satellites see and how can you identify a wildfire from the data? Typically, satellites carry multi-spectral cameras or sensors on board. As the satellite passes o

  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!

  "If you can't measure it, can you fix it?"   One of the greatest challenges faced by almost everyone working in a clean technology field - water, agriculture, energy, climate, forestry, wildlife, soils, corporate sustainability, smart cities - is the challenge of monitoring. At its essence,this is the challenge of   what needs to be measured, how often and how accurately can it be done . Traditional methods of monitoring have involved sensors (of different levels of accuracy) placed in specific locations and the data removed and processed off-site by engineers and field analysts at specific time intervals. This is a time-consuming process, with data that isn't as frequent or as spatially dense or with as many parameters as decision makers and scientists would like - but, until recently that's been the best that we've had. The advent of smartphones, high-frequency and high resolution satellite data and the whole Internet of Things (IoT) is changing this parad

I was at the   Open Water for California   conference in Sacramento last week - a conference dedicated to data science for water, with a focus on California. The conference itself was well attended with people from many different sectors - academia, government, non-profits and community members. What was particularly interesting is that it had an entire track devoted to hackathons and building new tools to solve some of the pressing problems being faced in water today - as well as the more traditional sessions with talks on the latest research and tools for the water sector.   The hackathon were focused on issues related to understanding trash movement into water sources (especially plastic), building consumer confidence reports on water safety and tools to better understand drinking water sources. There was a lot of interest in water safety understandably, with Flint still fresh in our minds and the recent focus on water quality issues in California communities affected by the wildfir

  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

Last time   we looked at the kind of applications in clean technology where using virtual reality or augmented reality systems are making a significant difference.     But, how do these systems work?   Most AR and VR systems can be broadly classified as follows. These systems can be divided into 1) the hardware required to get the data, process and display it 2) the software needed to develop simulations of the systems being studied and create virtual objects and 3) the server where the data are stored and processed and where machine learning algorithms can be deployed to improve outcomes.   Key Hardware systems : The hardware systems can be categorized into the input systems and the display systems.     Most AR and VR systems use GPS (to determine location), cameras (to obtain the live images of where the user is located and/or looking), gyroscopes and accelerometers (to determine speed and direction of the user’s movement) and other sensors that are specific to the problem being solv