AiThority Interview with Martijn Theuwissen, Co-founder at DataCamp

Tell us about your journey into Data Science? What led you to start DataCamp?

We started DataCamp in 2014 at a point in society where Data Science was just starting to make its way into a variety of industries — from Finance to Marketing to Manufacturing. Data Science was shifting from being a domain for company “geniuses” to one that was now open to all, and in urgent demand across business. Even roles that in the past only required traditional skill sets were starting to call for some level of data fluency, or at least the ability to understand data and communicate insights from that data, to make better business decisions faster.

But while the demand was there, the talent supply was not. Case in point, IDC predicts that by 2022, the skills gap will exceed the supply of workers, creating a shortage of 900,000 highly compensated jobs. This is why we started DataCamp: to make learning Data Science and Analytics skills as simple as possible, to narrow the skills gap and to enable data fluency more broadly.

Fast forward to today: we’re the leading platform for learning Data Science that combines interactive technology with expert instructors. We offer cutting-edge assessment features powered by Machine Learning to ensure learners make continuous improvements.

We’ve empowered 5 million learners globally across 1,600 businesses, including major corporations like 3M, Credit Suisse, Ikea, Intel, Uber and more. And we’re just getting started.

How has Data Science evolved in the enterprise in the last few years, and what challenges are companies facing?

Data is everything in business today. Yet, just about every sector of American business — from Finance companies and Healthcare firms to Management consultancies and the Government — relegate Data Science knowledge to a small group within the company. This is a problem because data scientists need to be able to communicate with colleagues in other functions within a company. This lack of communication results in a lag in productivity and results.

There needs to be a culture shift in general. Acquiring data skills is becoming as commonplace as learning basic computer skills like Microsoft Word and Excel, and we need to make sure we do not leave people behind.

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Data Science has impacted virtually every industry. How is it specifically impacting/influencing the realm of AI/ML?

Good quality data is becoming essential to deploying real ML. Before ML can happen and be impactful, companies need solid data foundations and tools for extracting, loading, and transforming data (ETL), as well as tools for cleaning and aggregating data from disparate sources.

Machine learning (ML) is no longer just a disruptive technology. We see it as foundational across a wide range of established vertical industries including Technology, Healthcare, Finance, Retail, and more, while burgeoning industries such as LegalTech and AgTech are growing rapidly as they adopt ML. We’re also seeing efficiency gains in the development of ML algorithms that are vertical-independent.

The bottom line here is that ML is only as good as the data you feed it. If your data is biased, your model will be, too.

Data Science is one of those areas that most people feel is reserved for “trained experts.” How has that changed?

Many people still believe that data scientists need to be experts in statistics, linear algebra, calculus, programming, databases, machine learning, and more. Some are even convinced that a Ph.D. is a requirement. Today, that’s simply not true. The fact is that anyone can learn how to be an effective data scientist. All it takes is a learning plan that includes quantifiable objectives and understanding the basics of widely used Data Science languages like SQL, Python, and R.

Over time, I’ve seen data scientists on Marketing, Commercial, and Product teams redefine their own roles as their “non-technical” teammates have learned some SQL and Data Visualization in Python or R. Now they can do work and create value that was previously inconceivable. And these are just some of the skills that DataCamp teaches to build data fluency throughout the world.

How does DataCamp help enterprises train their workforces?

Our solution provides an on-demand, interactive learning option that was specifically built for flexibility, and represents a fundamental shift in upskilling and reskilling initiatives. It’s part of a transition enterprise L&D functions are undergoing from creating in-person training material, to providing curated and personalized content for employees using online resources like DataCamp.

The most critical element is making Data Science training an ongoing process. We stress to our enterprise clients that learning shouldn’t be a “one and done” process. This is especially important in a dynamic space like Data Science. Enterprises must ensure their Data Science learning programs are repeatable and measurable. Continuous learning is now the norm. The list of tools and skills needed to solve real business problems is growing quickly. Professionals are now at a time when ongoing learning is critical to remaining relevant. That’s true across all enterprise organizations, but especially in the data world.

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What advice would you have for a budding data scientist getting their start today?

Take some courses with DataCamp! Seriously, I’d advise them to learn the basics of programs like Python, R, and SQL. I’d also advise them to choose a company to work for that has many smart people.

What are some related technologies out in the market that you are excited about?

Data engineering, AI, ML, and Cloud Computing are generating lots of excitement. It’s also been exciting to see so many companies making investments in data scientists, ML engineers, and data engineers within the data function.

It’s also worth mentioning that the internet still has immense power to democratize learning by increasing global access to quality education and making it more affordable. Duolingo is doing this for language learning. We’re pushing really hard to do our part for Analytics and Data Science.

Tag the one person in the industry whose answers to these questions you would love to read:

Michelle Keim, Head of Data Science and Machine Learning at Pluralsight.

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Thank you, Martijn! That was fun and hope to see you back on AiThority soon.