Opinion: Please stop calling yourself a data scientist

Photo credit: TheAWPMaster.

Many of my friends, colleagues, and contacts have started calling themselves data scientists. A number of resumes have crossed my desk indicating that we’re minting data scientists faster than expected.

I’ve seen this movie before. The IT biz has historically rebranded job titles based on what’s trending—today’s software architects were once known as designers or systems engineers. Nothing is trending faster and louder than predictive analytics, machine learning, deep learning, and AI. So, it’s our turn to rebrand data geeks as data scientists.

Now don’t get me wrong, some of these folks are legitimate data scientists. However, the majority is not. I guess I’m a purist. Calling yourself a scientist indicates that you practice science following a scientific method. You create hypotheses, test these with experimental results, and, after proving or disproving the conjecture, move on or iterate.

Data science is an applied science. So as an applied scientist, you create things—models, methods, and algorithms that provide practical utility. These things are valuable because they predict future outcomes from relatively few data inputs. In some cases, your models are black box enigmas (you might not understand how the prediction is derived, you’ve only shown that the models are accurate).

So in the spirit of maintaining an unadulterated definition of data science, I make the following assertions that might indicate that you’re not a data scientist:

• Expertise with the business intelligence stack doesn’t make you a data scientist. You’ve spent much of your time predicting the past by performing time series analysis of historical data. It’s not data science because you rarely perform experiments and your predictive power is illusory.
• Programming experience with Hadoop, R, Python, Octave, Matlib, and Mathematica are data science tools. Skills in handling these tools alone don’t give you data science cred.
• An advanced degree in mathematics, statistics, or econometrics doesn’t mean you’ve earned the right to call yourself a data scientist. Hopefully, you’ve developed the skills to apply descriptive and predictive techniques while maintaining a strong grasp of the underlying theory. But data science is an applied discipline focusing on specific subject area data. It’s most likely that you didn’t receive sufficient real-world experience pursuing your college degree.
• Evangelizing that big data, little data, or any data is the future of the predictive enterprise may look relevant on your resume—it may even get you a few conference speaking gigs and entertain your friends at cocktail parties—but you’re not a data scientist if you do. You’re just a big data groupie.
• The eight-week course you took on Coursera or the data science boot camp you attended makes you no more a data scientist than my recent golf lessons make me a golf pro. I believe in lifelong learning, and I’m all for self-improvement. But this is just self-delusion.
• You’re a subject matter expert and an Excel wizard capable of creating incredible charts, graphs, and pivot tables. Those skills, while valuable, don’t make you a data scientist.
• You’ve recently acquired a data science platform from SAS, IBM, or Microsoft. After reading the manual, watching the 10 introduction videos, or taking the five-day training course, you believe that you can create predictive/explanatory models of subject matter data by dragging and dropping algorithmic widgets onto a canvas and pressing the “learn” button. This doesn’t make you a data scientist. In fact, you’re dangerous.

I know this article is snarky, and I apologize if I’ve offended anyone. But I think it’s time we clearly define what a data scientist is and is not. I know that I’ve omitted other data science sub-disciplines like experiment design, sampling, and others. Maybe these are points to consider for next time.

This article was first published on LinkedIn.

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