Q&A with a DataCamp Counselor
One of the great things about data science is there are multiple pathways into the field. Some may segue into the profession from “hard sciences” like physics, while others receive Ph.Ds in the discipline after years of university study. Still others find online academies, such as DataCamp, a handy way to pick up data science skills.
DataCamp, which recently closed a $4 million Series A round of funding, is an up-and-coming provider of data science education. We recently caught up with DataCamp Director of Growth Weston Stearns to learn more about the state of data science education, and the company’s plans to expand its scope.
Datanami: What’s your take on the state of data science education? Do you think enough is being done to meet surging demand?
Weston Stearns: Data science is a quickly evolving field, which means educators have to be nimble to keep up with the speed of the changing landscape. The traditional education system is not flexible or fast enough to keep their programs up to date. Online and self-paced platforms have an advantage because they can focus on rapid iteration and development. As the data science landscape changes, online platforms have financial incentives to adapt quickly. Traditional schools may not have the same incentives or resources to adapt as quickly as their online counterparts.
DN: What educational format — in-person university-level courses, boot-camp style courses, or MOOCS — do you think is winning the race to educate the next generation of data scientists?
WS: Right now, each format has its merits. Universities often have trouble keeping up with the speed of innovation. The price of formal higher education is skyrocketing; most data science students do not have the time and money to invest in a degree or bootcamp.
I believe the next generation of data scientists will have tried a MOOC at some point in their education. The quality of content that is available online is extremely high. However, most online platforms don’t have the technology to create dynamic learning experiences on par with in-person bootcamps or brick-and-mortar university settings. As a result, many MOOCs exhibit low completion rates. Most content on MOOCs is consumed passively: students are expected to watch hours of videos and take occasional summary quizzes.
We believe that the best way to learn is by doing so that skills are acquired more quickly and with greater depth (and the experience is more fun!).
DN: How would you characterize the evolution of data science education over the past three years, and what do you expect out of the next three?
WS: The past three years have been marked by the rise of bootcamps and the increasing quality of online solutions. The next three years will likely be characterized by increased personalization and quality of online learning experiences. As I mentioned earlier, the quality of content online is already pretty good—but in terms of the way the content is consumed, we still have a long way to go. It is currently way too passive, and that will all change in the next three years.
DN: If somebody is interested in becoming a data scientist but has little training, what would you recommend that they do first?
WS: Data science is becoming such a huge field, an important (but often overlooked) first step is figuring out what you don’t know. Many beginners don’t realize all the possibilities that are out there. I would recommend getting up to speed on the basics by taking an introductory course on R or Python online. After that, they’ll have enough understanding to be able to discover new aspects of data science that are still foreign to them and pursue their interests, whether it is through more online courses or in-person training.
DN: What do you see as the biggest impediment to data science education at this point in time?
WS: At the moment, it is creating a high-quality curriculum that can keep up with the pace of innovation in the space. I see this problem in two parts: You need to be able to offer instruction using the most cutting edge tools that are being used in “the real world” but students also need to have a rock solid foundation in the theory behind the tools as well. What is and isn’t cutting edge is constantly changing, but the underlying concepts largely remain the same. Learners should come away knowing not only know how to use the tools, but why they are using them and what is going on behind the scenes. The balance between teaching the theory and teaching the practice is incredibly important. Combined with the pace of how fast tools for data science are evolving, finding this balance is one of the most important challenges in data science education.
DN: We have an abundance of technologies available today, from frameworks like Spark and TensorFlow to hardware like GPU processors and NVMe storage. Do you think the technology is progressing faster than we’re able to take advantage?
WS: In some respects, yes. Some organizations are able to keep up with the pace of innovation and other cannot. Part of the root of the problem is training and hiring: it is difficult to find or train personnel that are able to deploy these technologies to production. In some respect, I think this also goes back to my previous answer. When a technology is brand new, no one has a background working with it. Part of the role of educators in this space is also to create good learners, who are able to adapt and hit the ground running, even with brand new tools.
DN: Tell us about DataCamp. Who are the company’s customers, and what does it provide? How does it differentiate itself in the market?
WS: Datacamp helps professionals and students learn the skills they need to be successful in our increasingly data driven world. About 70% of our users and working professionals with the rest either students or working in academia. What makes DataCamp unique is our interactive interface that allows students to experiment, complete coding challenges, and get tailored feedback on their submissions. Additionally, DataCamp courses are taught by the best instructors in industry and academia so our students are always learning from subject experts.
DN: In light of the company’s recent round of funding, what are the company’s plans for growth? How will this improve data science educational opportunities for your clients and prospective clients?
WS: We are planning to quadruple our curriculum offering over the next year to cover all relevant data science topics and technologies. We will also be expanding our DataCamp for business offering allowing businesses to train their teams more effectively. We are very data-driven in everything we do, so you should be seeing a lot of really great improvements to our platform in the coming months.