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There is a growing gap between the growth of data and the growth of data knowledge and skills. The former increases at a healthy pace while the latter struggles to keep up. One team hoping to fill the gap in data education is the Data Society. The company offers bespoke training and education sessions for businesses and organizations (as opposed to individual users) and aims to provide users with a basic foundation of core competencies in data management, data analysis and data science. The Washington, D.C. company employs 50 full-time educators who present a data science curriculum created by a team of professional content creators and data scientists. The company also has a list of hundreds of part-time teachers that you can access specifically for specific industries.
The company started with the business-to-consumer (B2C) model, but changed course a few years ago to the business-to-business (B2B) model in order to be able to respond more flexibly to customer needs, according to Founder and Director, Data Society Executive, Merav Yuravlivker.
“We completely flipped the switch and said, no more B2C, we’re focusing on our industry clients,” says Yuravlivker, a former teacher who founded Data Society in 2014. “We’re one of the only players in the space.”
Over the years the Data Society has trained more than 10,000 people for more than 100 customers, many of whom are employees of financial services companies, healthcare companies, and federal agencies. The company’s customer list includes names like NASA, CDC, Department of Treasury, Discovery Financial Services, OptumHealth, and IQVIA, among others. Students fall into two general groups: professionals who need the technical skills to work with data and the techniques to use , and executives who need to know what data they are capable of and how to build a team.
“Beyond the technical piece, we found a big need to train executives, mangers, and general staff in the understanding of how to use data science in general, how to think about that strategy, how to staff up,” Yuravlivker says.
The ability to tailor the data society’s curriculum to a customer’s specific needs gives the company an edge over education providers with more stringent curricula. In some cases, this customization takes the form of industry-specific end projects, while in other cases real projects begin, what consulting jobs look like. (All trainings are now carried out virtually due to COVID19). For example, the Data Society recently assisted the National Park Service on a project to determine which parking lots to install electric vehicle chargers in.
“They actually created a whole new revenues stream for their agency,” Yuravlivker says. “It was really fun to see that.”
Another customer, a Fortune 50 company, engaged Data Society with a plan to teach students how to develop text mining systems. “So we built a course that goes from entry-level programming through text-mining techniques,” she says. “That is just an example of how we’ve used their own data to pull out insights.”
Yuravlivker is seeing a change in the way companies think about data, especially since COVID. They are more interested in getting results from their data than they are in investing in big data and data science themselves. But they’re still not sure how. To get there, they need a helping hand to guide them. For the data society, this means teaching the basics anew, even if your customers have different ideas.
“We have seen more of the introductory-level programs,” Yuravlivker says. “Everybody loves to know that we teach deep learning, but very few organizations are ready for it. So we’re working with a lot of foundational skills to build up that continuous culture of learning, building up that community of practice, and that common understanding of what it is.”
Yuravlivker is surprised at the profound misconceptions that many of her clients and prospects have about the nature of data and data science.
“Honestly, there’s a lot of misconceptions about what data science can and cannot do,” Yuravlivker says. “There’s a lot of managers who just want to throw data science at everything without understanding the reason behind it.”
Many managers love the idea of applying machine learning and deep learning techniques to their specific business challenges, says Yuravlivker, but often their challenges do not justify using these technologies.
“A lot of times, we’ve heard ‘Well, we need deep learning to solve this challenge,’ when maybe it’s just a classification problem, which is a bit of a different level,” she says. “Maybe it’s just, how can we have the appropriate data governance and standardization of data collection to help facilitate the process of data collection?”
The first step in data science is getting the data in order, and that usually takes up most of the data scientist’s time. Ensuring that the data is consistent, clean, and of high quality is not as attractive as building a learning model, for example. But it is a critical step that cannot be skipped. These basic data management techniques are also a foundational part of the Data Society curriculum.
“As you know, garbage in, garbage out (GIGO),” Yuravlivker says. “If we collect bad data, if we put it in an algorithm, it doesn’t matter how good it is–the results are going to be wrong.”
Rather than turning his students into deep learning experts or math geniuses, Yuravlivker would rather have a solid understanding of the fundamental problems that shape corporate data ambitions in the real world.
“The biggest lift, that biggest transformational value, is from that 0 to 1,” she says. “It doesn’t mean somebody needs to learn about neural networks. I would argue most people don’t need to know that. But what they do need to under and is how to use data, what it can do. They need to understand that data collection is 70% to 80% of any data science project.”
For companies and agencies that accept the hype surrounding big data and data science, the data society approach is a dose of solid medicine, but if they follow the company’s educational approach, they will find themselves on a more solid foundation for building their own data products.