How to break down the barriers to teaching (and learning) data science

Data science is taking over the business world. It’s time to make it more accessible in academia.

“The sexiest job of the 21st century” — that’s how Harvard Business Review described the role of data scientist in 2012.

Ten years later and the data science rocket shows no signs of slowing down. In fact, according to the US Bureau of Labor Statistics, employment of data scientists is predicted to grow by 36% through 2031 — significantly faster than the average for all jobs.

In a world where modern business revolves around collecting and quantifying data, it’s no surprise that data science is a bright career path. But it hasn’t made teaching — or learning about — data science any easier.

Let’s look at what’s standing in the way of data science education — and how the right technology can help teachers and students alike.

Setup is difficult

Getting started in data science is easier said than done without the right tools. Crunching huge data sets requires powerful processing units, but not everybody has the budget for expensive hardware.

And even if they did, using traditional data notebooks isn’t as easy as flipping a switch. Every time teachers or students want to start working on a new project, they have to perform the tedious, time-consuming task of configuring their environment, installing Python packages, and so on.

Accessibility is limited

Data science isn’t a monolith. It attracts a wide range of people with different backgrounds, interests, and skill sets. Some may have training in advanced mathematics, but limited experience in extracting, transforming, and loading data. Others may be experts at developing models, but beginners when it comes to cleaning data before analysis.

This mirrors the data teams at modern companies. They’re not just made up of data scientists — they often include analysts, software engineers, and more. But traditional data notebooks are far from intuitive and user-friendly for newcomers.

Collaboration is nonexistent

The myth of the lone data expert has been thoroughly debunked. Successful data exploration — the kind that uncovers valuable insights and makes an impact — requires teamwork, both among data professionals and their non-technical colleagues.

But true data collaboration is next to impossible in the classroom setting. Simultaneous work isn’t an option, so students are forced to save, download, and send their notebooks to their classmates or instructors for review. And then the “fun” of reproducing work can begin.

Modern data notebooks are the answer

At Deepnote, we believe data science is for everyone. That’s why we’ve built a collaborative notebook that beautifully integrates data science into every workflow and decision — and made it free for students and teachers.

See Deepnote in action in the video below:

While Deepnote wasn’t built expressly for education, it’s been adopted by educational institutions around the world and is now used in many classrooms on a daily basis.

Why? Because collaborative data notebooks break down the biggest barriers to teaching and learning data science, including:

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Setup

Deepnote runs in the cloud — all the heavy lifting is done on remote hardware you don’t have to purchase and host yourself. Our cloud-first approach means Deepnote can be accessed from any computer, whether it’s a personal laptop or library workstation.

"Deepnote is breaking down barriers for entry and shifting the culture of organizations toward a team-centric approach to data,” said Dan St. Paul, founder and software developer at Sports Data Solutions.

Our notebooks also come with basic software and environments already installed and configured. Both students and teachers can jump in and start querying or coding in seconds — all that’s required are a browser and internet connection.

Accessibility

Deepnote is designed to be intuitive and user-friendly — not confusing or overwhelming. It comes complete with prebuilt templates and integrations with popular data science libraries and tools to help learners get started quickly.

"Deepnote is like a Swiss knife every data scientist should have in his pocket,” said Luděk Čižinský, a student at IT University of Copenhagen. “Apart from its main advantages, such as collaborating in real time, it provides you with many more tools, from integrations to auto-completion of your Python code. All put together, you get to focus on the data science problem while Deepnote handles everything else."

You’ll also find tutorials embedded in Deepnote to help users get up to speed quickly on everything from running cells to publishing results. And any burning questions can be answered in our Deepnote Community, where users of all skill levels come to problem-solve and share their work.

Collaboration

Whether in the classroom or as part of a hackathon or student club project, Deepnote is built for collaboration. Our notebooks allow multiple users to share the same execution environment at the same time, as well as comment for real-time and asynchronous collaboration.

"Deepnote allows us, as teachers, to jump right into multiple student notebooks in real time,” said Steven Eno, Head of Instruction at 2Sigma School. “We can assist them while they are coding, leave comments, and monitor their progress. Deepnote provides the collaborative coding interface which allows us to focus on our students and their learning process."

Students can follow along with their instructors during lectures and highlight different areas of the course material they may need help with. Teachers can leave feedback on their students’ work and provide instructions on how to tackle problems. And with built-in version history, it’s easy for both teachers and students to restore older versions of notebooks for different use cases.

The field of data science is always evolving — there’s no standard curriculum or universal approach to teaching and learning it. But the barriers to entry remain the same at learning institutions around the globe.

With the right tools, educators can help today’s learners become tomorrow’s innovators, minus the impediments of traditional technology.