A Trillion Dollar Market - But Where Are the People?
Last week we talked about how the market in clean technology and data science is already in the multi-billion dollar range and is headed to the multi-trillion dollar space in the next decade or so. However, one of the challenges that analysts highlighted was the lack of professionals who have sufficient expertise in both clean technology and data science. So today, let’s take a look at what’s happening in educating professionals in this exciting, new field as well as the kind of skills that are needed.
Most of the traditional college and university programs haven’t yet caught up with the demand for professionals at this intersection of specialities - although they are getting there! While many universities and colleges have created data science degrees - these usually focus on the problems that are faced by the high-tech and internet sectors. The graduates from these programs usually have a pretty solid understanding of coding, algorithms including machine learning, and statistics - but they have less knowledge of how to adapt these skills to solve problems in the clean technology sector.
Other universities have graduate programs and research in environmental engineering/environment science or earth science where there are courses and specializations targeted at using data science tools in those sectors. However, these are usually single or dual semester courses at most and help students develop basic skills in analysis and coding. Students from these programs tend to have an extremely strong background in and understanding of the clean technology sector (water, air, environment, climate, agriculture etc.) - but lack the kind of coding and algorithmic skills that jobs at the intersection demand.
Many PhD students or students who do a Masters thesis who are working on problems that use data science in cool ways for the environment find that they have to develop the skills to solve their research problem by combining courses from several different departments. These students, especially those with doctorates, are the ones who come out of school able to apply their knowledge of the sector and their coding and algorithmic skills to real-world problems in the sector. Unfortunately for the companies that are looking to hire, there aren’t as many graduates as there are job openings at this point - so a definite gap in skilled professionals at the entry level!
Let’s take a look at some of the job descriptions in our jobs portal to see if we can break down the kind of skills that are needed. If we look at this role for a Data Scientist at either Syngenta or BASF, a couple of the large agricultural firms, you’ll see a need for both a strong background in the agricultural sector and a deep understanding of coding (ideally in Python), statistics, machine learning and how to use data science to build agronomic models. So, if it’s someone working as an individual contributor in a technical role - you need to be able to write code well enough that you can build model prototypes, discuss data and engineering issues with the software team and understand the agricultural sector well enough to be able to make the prototype something meaningful and useful enough to customers in the sector.
That’s the kind of role that someone who’s been working on a research problem in crop modeling using remote sensing data or building a robot to manage weeds and has graduated recently can do. However, they’re likely to need help in navigating the corporate structure and figuring out how to work with software engineers, data scientists, statisticians, business development and so on in order to be most effective. And that’s something that definitely isn’t taught in school!
Now, this kind of coordination is what’s usually expected of managers and other experienced professionals in companies and organizations. If you’re in a manager role and working at the intersection of clean technology and data science - then you need the ability to understand the technology as well as manage the people in your team and figure out how to get them the support they need from other groups in the organization. So, once again - a combination of domain knowledge, data science knowledge and business understanding is needed.
That means that while there’s a lot of pent up demand for cool technology in robotics, machine learning and other data science fields that can be applied to solve the problems in clean tech - there still aren’t enough people who understand the clean tech sectors and the cool technology to build these products and to deploy them at a commercial scale.
And that’s what we’re doing here at Ecoformatics - we fill this gap by empowering clean technology professionals with data science skills through our online education platform where we use real-world problems to build and apply skills.
