Exploring Uncommonness of a Common Data Scientist

I have been asked multiple times and have seen people making their designations or portraying themselves as Data Scientist.

Here my very very clear & concise opinion: If you contain these all then you are one step behind to being called Data Scientist and if you have any one then count the number of points add 1 to it that it you get your position from being Data Scientist:

1. 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.

2. 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.

3. 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.

4. 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.

5. 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.

6. 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.

7. 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.

If you have conquered all these then the only thing that is between you and being Data Scientist is:

DOMAIN KNOWLEDGE!!

I foresee Data Science which is currently portrayed as a field will become skill of tomorrow just like Excel. It came like a specialized skill and now is an obvious skill for everyone :)

I recently visualized and analyzed survey of Data Science on Kaggle and Here are the summary results which you will be amazed to see.

THE DATA SCIENCE DISCOVERY DASHBOARD : LINK TO VIZUALIZATION

THE DATA SCIENCE DISCOVERY INSIGHTS : LINK TO VIZUALIZATION

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