Do you want to be a Data Scientist?
Guest post by Michael Koploy of SoftwareAdvice.com
By now, pretty much everyone has heard that “Big Data” will be the next “big thing” to revolutionise how we work, live and communicate.
But who will manage the Walmart database that contains over 2.5 petabytes of data from the retailer’s 1 million customer transactions per hour? Who at YouTube is analyzing the 48 hours of video uploaded to its website every minute?
For every Big Data problem, the solution often rests on the shoulders of a data scientist. The role of the data scientist is similar in responsibility to the Wall Street “quants” of the 80s and 90s – now, these data experienced are tasked with the management of databases previously thought too hard to handle, and too unstructured to derive any value.
The sexiest job of the 21st Century?
Thomas Davenport and D.J. Patil brought the data scientist into the national spotlight in their October 2012 Harvard Business Review article: Data Scientist: The Sexiest Job of the 21st Century. Job trends data from Indeed.com confirms the rise in popularity for the position, showing that the number of job postings for data scientist positions increased by 15,000% between the summers of 2011 and 2012.
When I asked Bruno Aziza last year how he would best describe a data scientist, his answer still sticks with me today. “Think of a data scientist more like the business analyst-plus,” he told me. Part mathematician, part business strategist, these statistical savants are able to apply their background in mathematics to help companies tame their data dragons. But these individuals aren’t just math geeks, per se.
“A data scientist is somebody who is inquisitive, who can stare at data and spot trends. It’s almost like a Renaissance individual who really wants to learn and bring change to an organization.” — Anjul Bhambhri, Vice President of Big Data Products, IBM
Tips for aspiring data scientists
If this sounds like you, the good news is demand for data scientists is far outstripping supply. Nonetheless, with the rising popularity of the data scientist – not to mention the highly-competitive companies that are typically hiring for these positions – candidates will have to be at the top of their fields to get the jobs. Here are some quick tips for career success.
For students about to graduate: Focus on academics before (and after) graduation. Successful data scientists come from a number of different disciplines: biostatistics, econometrics, engineering, computer science, physics, applied mathematics, statistics, machine learning, and other interrelated disciplines. Experience applying the scientific method to many disciplines and areas of research will provide fruitful in the field of data science. And as important as academics are during school, it’s just as important to stay up-to-date with current research trends and discoveries within academia, even after graduation, for instance by subscribing to academic journals.
For career entrants: Focus on business acumen. While programming and statistical expertise is the foundation for any data scientist, a strong background in business and strategy can help jettison a younger scientist’s career to the next level.
Krishna Gopinathan, founder of Global Analytics Holdings, recently recounted to me how he has built some of his most exceptional data scientist teams. The secret, in his opinion, was to build teams around data scientists that ask the most questions about:
- How the business works
- How it collects its data
- How it intends to use this data
- What it hopes to achieve from these analyses.
These questions were important to Gopinathan because data scientists will often unearth information that can “reshape an entire company.” Obtaining a better understanding of the business’ underpinnings not only directs the data scientist’s research, but helps them present the findings and communicate with the less-analytical executives within the organisation.
While it’s important to understand your own business, learning about the successes of other corporations will help a data scientist in their current job–and the next.
