Defining the Wizard a.k.a. a Data Scientist

Keeping up with the recent theme of “The Wizard of Oz,” in this post I have decided to analyze the “wizard” or a data scientist. Big Data, analytics, metrics, business intelligence are all buzz words we constantly hear.

LinkedIn released their top skillsets for 2017 with statistical analysis and data mining at number two on the list. There is no hiding that data scientists and the skills required are growing in importance – but do we have a full understanding of the skills actually needed to be a data scientist?

Everything You Should Know About Data Science: The Century's Hottest Career written by Laurence Bradford, helps us begin to understand this “wizard,” “man behind the curtain,” or more simply put a data scientist.

According to Bradford, “Gautam Tambay, cofounder and CEO of Springboard, believes that ‘Data is the new oil.’” There are some specific things you should know about these wizards:

1.       Early on most data scientists were only PhD’s our those who completed various higher level education courses. With the amount of data only increasing, there is a short in the supply of these data scientists. Today someone with logical thinking and a passion for analytical insights are beginning to do the job once reserved for those with PhDs.

2.       Being a data scientist isn’t strictly numbers. Niraj Sheth a data scientist at Reddit stated, "Fundamentally, it is as much about people -- the users you're building for and the coworkers you're building it with -- as it is about math and engineering. Having a hybrid background myself has definitely helped me understand which parts of data science to leverage at different times."

3.       Tambay further breaks down being a data scientist into five simple steps:

1.       "First of all, you want to learn to break down problems into its constituents. Every time you think about why something’s happening, create a hypothesis. This can apply day to day. When you’re doing anything with your friends. When you see something happening, [ask] ‘why did that happen?’

2.       "[Second], think about, ‘what data would I need to prove or disprove this hypothesis?’ Think about why this would happen, think about a hypothesis, think about what data you would need to prove or disprove the hypothesis, then go find the data and see if the data confirms your hypothesis.

3.       "[Third], think about how to bridge the gap between this simple hypothesis-driven thinking to actually running large experiments. That’s where you need to learn the statistics, that’s where you need to think about how to clean and wrangle data, because often data is messy.

4.       "[Fourth], you think about how to organize the data into analyzable form, and that’s when you need the tools, whether it’s Python programming or a language like R or some people will just even use SQL and Excel for smaller problems. But that’s when you need the tools to actually analyze and conduct your analysis.

5.       "Finally, you need tools to visualize and present your insights -- data storytelling."

If we’ve learned anything from my past posts and the movie The Wizard Of Oz, it’s that anyone can be “the man behind the curtain.” With the right kind of drive, inquisitive nature, and logical thinking anyone can be your data scientist.

Data will play a significant role in the success of companies over the next decade. This will require us to adapt – especially us as marketers. I look back to Sheth’s comment from above, as marketers we have the people skills or understanding necessary for part one, we need to develop ourselves analytically to achieve part two.

Putting people and analytics at the center of defining a data scientist is a logical fit for a marketer. While analytics and data was not the “attractive” part of marketing that got me interested in this field, I am beginning to become more interested in it as I recognize it as the future of this field.