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 ourjobs portalto 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 in this field at this time are
But what if these specific university options are not available to you?
Let’s take a look at what most people who have a background in this field did. They probably did a Masters or PhD in a traditional engineering or earth sciences curriculum at universities and as part of that, did research in a problem that required data science. Most universities around the world have faculty doing research in problems that need this expertise- either building a machine learning model, getting data from satellite systems, designing experiments in the field and creating statistical models to understand the results or building sensors/robots to collect data. Going this route means that in addition to your knowledge of the earth system, you learn how to code (probably in R, Python and Fortran), get the basics of how machine learning works and know how to present the results of your research in a way that people who are not always familiar with the details of your specific problem can still understand it.
The missing pieces in becoming a full fledged clean tech data scientist are the ones that are typically not taught in universities and are common among all entry level data scientists in all fields. These include both the technical aspects and the business/management aspects such as:
How good are your data? What do you need to make sure that high quality data is fed into your model or if that isn’t possible, how can you modify your model and assumptions to account for it?
How do you make sure your machine learning model or statistical model will function correctly as the problem scales?
How do you create the data storage system and pipeline to store and access data so that it can be robust and efficient? If you’re not the creator, do you know how to access the data?
How can you make your code modular, efficient and scalable? What tests do you need to write into your code to ensure this?
Can you present your results and your code to other teams (software, finance, operations, legal) so that your work can be used across the organization? What do you need to understand about the organization and the teams you are working with?
If additional technical skills are needed, then many people also take courses in these specific technologies to augment their skill set. Courses in coding in Python or SQL, algorithms for efficient coding, introductory machine learning courses are all widely available in platforms like Coursera, Udemy, EdX and in bootcamps. People may also do challenges on Kaggle to apply their new skills to relatively large datasets. All these provide great tools for people to get workingon real data and real problems and develop a deeper understanding of how to use their new skills.
The challenge though is that these courses and datasets are typically focused on problems in other sectors, like social media or finance, where the data and problems are different from that faced by people working in the clean tech sector. Figuring out which courses are immediately useful, which ones provide skills that will be useful in the future and how to make them work for your clean tech sector becomes a full time job in itself!
We’ll be talking more about the tools and skills needed for the clean tech data scientist in our blog here this month, but in the meantime check out our free planif you want to get started!
We're in the processes of building a couple of fantastic new offerings that many folks in our community have asked for - so blog posts will be limited for a few months. Our jobs portal will still be updated regularly to make sure that all our members can keep up with what's happening in the sector. We can't wait to share what's happening at our end!
The last couple of months have been interesting from a climate viewpoint - we’ve seen a record number of climate related disasters around the globe - drought, floods, fires, heat waves…..and it looks like this is probably going to be what our planet will look like in the near future. Add to that the COP26 conference that is scheduled for October 31st - and climate, sustainability and technology are front page news! So, let’s talk about one of the technologies in the news - artificial intelligence (AI) and its impact on climate, water, agriculture, energy, forestry, ecosystems and other sectors in clean technology . AI and its subset of tools - machine learning (ML), data science and statistics - are being touted as one of the key technologies in solving the problems facing the planet today. And while these technologies are certainly powerful - applying them effectively to solve problems in clean tech is another issue altogether. AI has been used by scientists in different clean tech se
Will AI transform water, energy, agriculture, climate and all the other clean tech sectors? Can AI transform these sectors? Some version of these questions always gets asked at any meeting or conference in clean technology. Of course, part of that is because there’s been so much hype around AI and the whole “software is eating the world” interviews that came out a couple of years ago. But part of it is also because these tools are so powerful that professionals working in these sectors can see the potential - but just aren’t sure if it’s applicable to their sector yet. So, let’s start by asking a couple of fundamental questions. Why do we need AI at all? Or any models for that matter? Models are used to understand the world - to estimate the impacts of changes in systems and to try and predict what will happen in the future. Typically, the approaches used in building models can be classified into three broad categories - physical or mechanistic approaches, statistical approaches and