I recently caught up with Michael O’Connell who is Chief Data Scientist at TIBCO, a global leader in infrastructure and business intelligence software, to talk about the flourishing career outlook, demand and job security that a career in data science and business analytics are bringing to the higher education community. Here is what he had to say.
Daniel – Managing Editor, insideAI News
insideAI News: What is your outlook for careers in data science? Do you see strong demand and long-term job security?
Michael O’Connell: Absolutely—the oft-referenced McKinsey report outlining the impending shortage of data scientists was right on the money. What’s really in short supply are individuals with a combination of business acumen, analytics skills, and the ability to wrangle data from IT systems—this combination is what brings a lot of value to the table for a variety of businesses. Just knowing a subject like statistics is not sufficient—it’s the ability to wrangle and prepare data for analysis that statisticians have typically avoided. But perhaps most important of all is having knowledge of the business in which you’re performing analytics. Combined, you get the “three-legged stool of data science success”—business acumen, knowing how to acquire and prepare data for analysis, and knowing how to actually analyze the data to derive business insights.
insideAI News: There’s a lot of recent interest for getting into the field from those who want to become the next generation data scientists. What resources are out there for total beginners, to new graduates in econ, stats, applied math, etc. in getting a start as a data scientist?
Michael O’Connell: Those fields—economics, statistics, applied mathematics—are still representing only one part of the three-legged stool I mentioned, so while people in those fields are primed nicely for success in data science, it still isn’t the end-all be-all of prerequisites. I highly recommend anyone looking to get into this field to learn how to wrangle data from SQL. The preparation of data before you even do any math can often be more involved than the math itself, so get your hands dirty by learning SQL on a site like Coursera or any other of the widely available free SQL resources. But as I mentioned in my previous answer, developing the knowledge about a particular business is one of the more overlooked skills to bring to the table. It’s one thing to know how to crunch numbers, but another thing to have the wherewithal to know how those conclusions impact the larger business. Of course, the business acumen mainly comes with experience, but if beginners are looking for an edge I would recommend getting into an area they’re interested in from a subject matter perspective, then learn data science along with the business.
insideAI News: What specific skills should upcoming graduates have in order to secure long term employment, and maintain a competitive edge when interviewing?
Michael O’Connell: I mentioned some of these—basic data access skills like SQL, for example. But beyond that, one can gain a competitive edge by learning a variety of coding constructs such as R or Python to wrangle data, get it from the right sources, and then complement those skills by learning an interactive visual environment like Spotfire that will enable you to mash-up data and explore data visually and interactively. We’re finding that people who are knowledgeable in R and Python and already have some experience with an interactive visual environment like Spotfire are bringing really valuable skills to the table in general for today’s enterprises. These are all freely downloadable environments, by the way—Spotfire, R, etc.—it’s fairly easy to grab these tools and jump right in getting started. Even some tools that are available on a trial basis only, if you’ve explored the tool thoroughly for that month it shows strong initiative in getting familiar with those kinds of environments, and that should help with the competitive edge.
insideAI News: Do you see a benefit for obtaining an advanced degree in a closely related field such as computer science, mathematics, or statistics at this point in time? Is it necessary? Will it help?
Michael O’Connell: Not necessarily, but it certainly helps. Data science is a three-legged stool comprising analytics/statistics know-how, the ability to access and wrangle data from IT, and having knowledge on the business at which you operate. Just having some knowledge of the business in which you’re trying to get a career is a start. If you’re coming out of university with a quantitative degree in something like applied mathematics or statistics, you’ve got that one base covered—so getting your hands on data in environments like Spotfire or R is the other piece in addition to the business acumen. It benefits to have degrees in these areas but it’s not the end of the world if not. Some say the data scientist within the organization knows far more than the statisticians, due to the diverse skills encompassed by the three-legged stool analogy. Anyone looking for a long, fruitful career in the field of data science should be well-balanced in all three areas—not fully maximized in just one of them.
Sign up for the free insideAI News newsletter.
Speak Your Mind