The Eclectic Past and Diverse Future of Data Science

The Eclectic Past and Diverse Future of Data Science

A decade ago, the Harvard Business Review declared data scientist the “sexiest job in of the 21st century,” highlighting massive shifts in how organizations collect and use data. The demand for data science skills rose accordingly, leading many universities to quickly build data science curriculums. Simultaneously, data science is now a growing source for leadership roles at established companies and startups alike.

While the seasoned data science veterans examine the influences and challenges in the field, educators and students point toward goals and hopes. As business and culture march onward in the information era, skilled data experts will be vital architects of the systems and products that govern our lives. Together, they portray data science’s past, present, and future.

Diverse backgrounds, diverse data

At the university level, data science didn’t have the same relevance as mathematics or computer science until 2005[1], when the National Science Board advocated for data science as a career path. The need for data scientists was already apparent, leading many graduates of the 90s and 2000s to enter the field despite having no formal training in data science. 

As a result, many contemporary data scientists have diverse academic and technical backgrounds.

“I studied mathematics at university,” said Amy Sharif[2] , head of data science operations at Peak, an AI software company based in Manchester, United Kingdom. “When I was finishing up university, data science wasn’t really a common career path, at least in the UK, so I started working in a marketing agency on the data analytics side.” 

Stuart Davie, VP of data science at Peak echoed Sharif’s experience. “I got my Ph.D. in computational physics before data science was really a field,” he said, “so I found a post-doc opportunity working on machine learning.” Davie, who now leads teams at Peak, sees this diversity of backgrounds as a strength. “We have a very diverse set of people working in our team. We have 30 to 50% PhDs, from physics to evolutionary biology. It’s a real blend of fields,” he said. 

When asked how this strength plays out in their work, he highlighted how his team thinks through problems.

“We’ve found in some companies; roles are very tightly bound. One side is saying “What”, the other saying “How”. But data scientists are very inquisitive, they want to ask ‘Why?'” Regardless of background, many data scientists feel drawn to the field for reasons they can’t quite articulate.

“My path to data science wasn’t intentional, but, in a way, it was always a role I was looking for,” said Sharif, “I just didn’t know it existed.” 

“My path to data science wasn’t intentional, but, in a way, it was always a role I was looking for. I just didn’t know it existed.” 

Present-day foundations 

While pioneers of the field fell into their passion for data science, the next generation is afforded earlier exposure in their careers. As of 2021, over 900 programs at 500 universities are focused on data science, an awe-inspiring number for such a young discipline.

UC San Diego’s Halıcıoğlu Data Science Institute, founded in 2018, is one such institute focused on broadening data science education to meet the rise in demand for skilled workers. Rajesh Gupta, distinguished professor and founding director of HDSI, spoke about the mission and goals of the department in an interview.

“The roots of HDSI are based on my experiences in computer science when I started to notice that data was having a larger impact than engineering,” said Gupta. “Data impacts economics, the hard sciences, everything. So, we were wondering if there was a better way to study this phenomenon.”

HDSI now offers an undergraduate major and minor alongside three graduate programs. Curricula are designed to teach students vital skills needed in a workforce experiencing a critical shortage of data science professionals.

“Our focus on data came almost entirely from the industry, the needs of business, and the students seeking employment,” Gupta said. “Accordingly, our goal is to teach students concepts that will have longevity: programming, machine learning, and similar.”

While this focus on the next generation of data scientists is foundational to Gupta’s efforts, he also recognizes the importance of basic data science competencies for all students.

“I do see data science making significant inroads into general curriculums, from HS to college undergrads,” he said, “Core data literacy is vital no matter what field you’re in.”

A data-driven tomorrow

From industry to academia, one thing is constant for data scientists: optimism for the future. The explosive growth of opportunities and newfound prominence of the field has catapulted data science to being, well, a very attractive line of work. So it makes sense that the field abounds with positivity.

“What’s exciting about this field is how fast it’s moving,” said Stuart Davie. “[It’s] booming. I expect that to continue.”

Beyond new jobs and opportunities remains an important truth about this field: as it grows, so does our understanding and knowledge. These scientists mine datasets for insight and hidden meaning and turn those into actionable solutions that make the world a better place. Improving revenue by five percent could change an industry and discovering earlier symptoms to diagnose cancer could save lives. For Amy Sharif, this element of discovery anchors her work.

“What makes it really important to me is that it helps us understand the world around us,” she said, “because, as humans, we are always looking to do things better, and data is a vital tool in that pursuit.”

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