Vivian Zhang On Data Mining: The Hot New Career In the Science Industry

Vivian Zhang on Data Mining:

the Hot New Career in the Science Industry

There are more than 54,000 newly minted PhDs in the US annually whose futures largely depend on snagging competitive tenure-track professorships. But there aren’t enough of these academic positions to go around. Thus, these recent graduates are pursuing postdoc after postdoc, resulting in the waste of enormous talent. Burdened with student debt and overqualified for other jobs, what are these new doctorates to do?

Vivian Zhang says she has one answer: the booming data science industry. She is the CTO and Founder of The NYC Data Science Academy, Adjunct Professor at Stony Brook University, and has recently been named by Forbes as one of nine women leading data analytics. In order to help academics, researchers, and people in business build a strong foundation in data analysis, applicants to the Academy go through a 12-week bootcamp. The aim is to produce professionals who can competently carry out methods of data science to give them an edge in the job market.

“My husband was a postdoc of math for 9 years. He went to Stanford, got his Ph.D in math, went to Harvard and did 5 years postdoc. He just couldn’t find any jobs.”

For Zhang, her journey towards developing an expertise in data science has been deliberate. In 1997, she had asked her cousin, ‘what is the future for computer science?’ His answer was prophetic in its accuracy. He believed soon people will be able to data mine on their own laptops, and so she immediately began to learn data analytics and how to code.

Becoming a teacher or mentor wasn’t something Zhang had ever anticipated. But after starting up the NYC Open Meetup, the interest people had in learning how to apply data science in their everyday lives and professions was ravenous. In the six months after the Meetup was launched, it racked up 45 sponsors and 200-300 attendees per event. The intensity with which people expressed interest in what she had to say led to the founding of the NYC Data Science Academy, which was entirely funded by Zhang herself.

Like everything else, methods of research have gone through fads – oscillating between emphasizing the qualitative (the personal and ethnographic) and the quantitative (numbers and statistics). But as technology has advanced, the tides have turned to highlight what we can learn from numbers and data analysis.

As the world becomes increasingly organized around what we can learn from metadata for business, policy, and academic research, we have to ask the question: what exactly is data science?

“Right now, all the major businesses are running on the big data infrastructure,” Zhang reveals. 

Data science is an umbrella term that encompasses creating software platforms, developing tools to gather and parse data, and designing visualizations to convey the relevance of trends within data. It is used in media (Netflix) and health (FitBit), and even travel (Uber/Lyft). With virtually every industry relying on building and improving products with data science, jobs in the industry are plentiful and the pay is far above what academia can accommodate. As companies see that their competitors are increasingly relying on data science to fine-tune their advertising and recruiting, the resulting pressure raises the stakes and expands the market for data scientist positions. Glassdoor reports that the average salary for a data scientist is $118,709, ranking at the apex of their ‘top 50 jobs’ for the last two years.  

“I think that data science is going to be ‘hot’ at least for the next ten years as the most amazing and best paying job on the market.” 

Vivian Zhang Courtesy of NYCDataScience

But can anybody be a data scientist? What does it take to be one? Vivian says bluntly, “it’s hard.” Like, really hard. At least to achieve a fluency to do it at the professional level. She warns that the requirements are rigorous, and people must be adept at at least one of three disciplines: coding, mathematics, or statistics. And it’s quite rare for someone to excel at all three of these skills. People with a background in physics, computer science, or math are most likely to pick up on the expertise necessary to become a data scientist.  Zhang stresses the importance of visualization in data science as ‘something you have to do’ when companies and individuals are applying it for marketing purposes. As it is with most things in life, joining creative vision with ‘hard science’ is required in order to utilize the full potential of what data science can bring to businesses.

The market for data scientists is on everyone’s radar, and the opportunity to improve your salary, career options, and the overall quality of your professional life is alluring. Not to mention its exponential use in business and marketing. In many ways, all modern professionals need to be adept at recognizing data trends, and even more important is knowing what to do with them. Vivian recognized this potential 20 years ago, and with that foresight created an invaluable expertise that grows with each day, lifting fellow professionals and scientists up, and guiding corporate clients in applying data science.

Elena Teare

Elena is a contributor to SWAAY

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