Key Data Science Concepts All IT Pros Should Know

Getting the Right Mix: Data Science Takes more than Math and Coding

As noted at the beginning, even though hard skills make up the core of data science, there are also some soft skills involved in bridging the gap between the data and its meaning, the information presented and the actionable insight. This is why a mix of skills, including the technical and creative, are needed to be successful in the field.

A mix of skills for the professional data scientist is what emerges from the list Roger Huang presents in Every Data Science Interview Boiled Down To Five Basic Questions. Those five questions work out to 60% hard skills, 20% soft skills and 20% ability to apply knowledge to the situation. The hard skills make up three of the questions: one on math, one on coding and one on statistics.

Soft skills come into play in providing the answer for what Huang calls “behavioral questions” that assess the applicant’s fitness for the company culture. Then there is what he calls the “scenario question,” the one that challenges applicants to demonstrate their ability to apply what they’ve learned to a particular situation and outline an approach that could work. Mastering scenario questions draws the power of imagination or creativity, as well as communication skills, and business acumen, soft skills that KDnuggets includes in the list of mandatory skills for a data scientist.

What’s Creativity Got to Do with It?

Bill Pardi explained why creativity is essential for data science success in an article on Medium. He clarified as follows: “What I mean by creativity in this context is the process of asking questions and experimenting. Creativity allows us to take the data we have, question our starting assumptions about what the data is telling us, and experiment until we make something useful out of it.”

Pardi offered the analogy of a chef who is the one who has the vision and skill to take the raw food and turn it into a spectacular dish. Without the chef’s cooking skills, the ingredients will not reach their potential. Data itself is a raw ingredient — not the finished product of data science, which is insight.

“For data to support truly creative or innovative outcomes, we must allow it to inform us of the facts so we can ask questions and experiment with the ‘adjacent possible’ to discover the insights and potential that the raw data doesn’t provide” is the gist of its argument.

Using Both Sides of the Brain for Success in Data Science

Pardi’s take on the need for creativity agrees with the insight Olivia Parr-Rud shared in 12 Key Tips for Learning Data Science. She insisted that data scientists need to use “art as much as a science.” She added that it is a mistake to consider “data science as a career that primarily uses the left-brain” when, in fact, “data scientists must use their whole brain.”

Integrating both parts of the brain is what makes it possible to do more than merely observe patterns, she explained:

Most left-brain/linear tasks can be automated or out-sourced. To offer a competitive edge as data scientists, we must be able to recognize patterns and synthesize large quantities of information using both sides of our brain. And we must be innovative thinkers.

Talking Business

It’s not just a matter of thinking creatively but conveying the ideas in a way that makes sense to the intended audience. That means data scientists have to be able to put themselves in the shoes of the decision-makers to see things from their perspective and explain the significance of the analytics in their terms.

As Parr-Rud put it: “Most executives don’t understand what we do or how we do it. So we need to think like leaders and communicate our findings and recommendations in language that our stakeholders understand and trust.”

This is where the data scientist needs to draw on three of the four soft skills identified by KDnuggets: teamwork, communication skills and business acumen. Some substitute domain expertise for business acumen. That refers to understanding what the particular context for the data and the goals of the analytics are.

Without deep domain expertise, Dean Abbott, co-founder and chief data scientist at SmarterHQ observed in an interview, “you don’t know what you’re looking for.” Data scientists have to communicate clearly with the people in the business who know the ins and outs of its operations to learn “which metrics are significant.”

What It’s All About

What about the fourth soft skill KDnuggets included? That’s intellectual curiosity, which underlies all motivation to frame questions and set up the process of finding answers.

This is what brings us to the very essence of science as Einstein described it: “The mere formulation of a problem is far more essential than its solution, which may be merely a matter of mathematical or experimental skills. To raise new questions, new possibilities, to regard old problems from a new angle requires creative imagination and marks real advances in science.”


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Written by Ariella Brown
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As a technology writer, Ariella Brown has covered 3-D printing, analytics, big data, bitcoin, cloud computing, green technology, marketing and social media. She holds a Ph.D. in English and taught college level writing before becoming a full-time writer, editor, and social media consultant. Her best social media outlet of choice is Google+. Links to her portfolio, blogs, favorite quotes, and photos can be found at writewaypro.weebly.com.

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