Saurabh Sahu
Sep 1, 2018 · 5 min read

The skill set of a successful data scientist will comprise both technical and non-technical skills. While technical skills like programming and quantitative analysis are highlighted, it is easy to undervalue the impact of the non-technical skills. An effective communication is 20% what you know and 80% how you feel about what you know.

“What makes a good scientist great is creativity with data, skepticism and good communication skills.”

List of 5 non-technical skills required by every data scientist

1. Communication — One of the most important skills to have is effective business communication. Whether it is understanding the business requirements or the problem at hand, probing stakeholders for more data, or communicating insights, a data scientist needs to be persuasive. “Storytelling,” as the data scientists call it means that analytical solutions are communicated in a clear, concise, and to-the-point manner so that both technical and non-technical people can benefit from it. Data visualization and presentation tools are widely employed by data scientists for their graphic appeal and easy absorption by all teams in the organization. Often underestimated, this is one of the most important skills for the simple reason that all statistical computation is useless if the teams can’t act upon it.

“You need to be able to take a dataset and discover and communicate what’s interesting about it for your users.”

2. Data-Driven Decision Making — A data scientist will not conclude, judge, or decide without adequate data. Scientists need to decide their approach to a business problem in addition to deciding several other things like where to look, what tools and techniques to use, and how to visualize and communicate it in the most effective possible way. The most important thing for them is to ask relevant questions, even if they seem far-fetched. Think of it as a child exploring all his surroundings to draw conclusions. A data scientist is pretty much the same.

“Critical thinking skills…really [set] apart the hackers from the true scientists”

3. Mathematical and Statistical Acumen — A data scientist will never thrive if he/she doesn’t understand what test to run when and how to interpret their findings. They need a solid understanding of algebra and calculus. A statistical sensibility provides a solid foundation for several analysis tools and techniques, which are used by a data scientist to build their models and analytic routines.

“Without grounding in statistics, a Data Scientist is a Data Lab Assistant.”

4. Teamwork-On one hand, data scientist will have to collaborate with the teams to understand their requirements, gather feedback to reach benefiting solutions, on the other hands they will have to work with fellow data scientists, data architects, and data engineers to perform their tasks well. The culture in a data-driven organization will never be that of the data science team working in isolation.

“Scientist dream about the great thing, engineer do them along with support of good team”

5. Intellectual Curiosity and Passion –Data scientists are passionate about their work and have an inconsolable itch to use data to find patterns and provide solutions to business problems. They often have to work with unstructured data and rarely know the exact steps they need to take to find valuable insights that lead to business growth. Sometimes, they don’t even have a clear problem to work with, just signs that there is something wrong. That’s where their intellectual curiosity guides them to look in areas no one else has looked in.

“You don’t need to read “How to think like Sherlock” just ask a data scientist!”

Communication skills for Data scientist:

Data Scientists must be able to fit into the corporate culture, interacting in business meetings, and contributing ideas that can help the company move forward. Without effective communication skills, Data Scientists could be mistakenly perceived as not being team players, damaging the careers of even the most talented analysts. Here are a few tips that can help Data Scientists avoid committing career faux pas without realizing it.

Practice Active Listening:

If you’ve ever been told you’re a great listener, this is one area where that skill could come in handy. In business, as in everyday life, listening means truly hearing and assimilating what the other person says, rather than thinking about what you will say next.

Understand the Business:

An important part of productively communicating with your co-workers is fully understanding their own priorities as they fit into the overall mission statement of your employer. If your company doesn’t provide information about its goals and objectives, ask questions. Communicate with members of various teams to determine what their own ongoing issues are and remember those issues when you work together.

Hone Your Presentation Skills:

Even if you’re never called upon to demonstrate your work to a crowd of thousands, you’ll eventually find yourself tasked with showing your work to someone. Perhaps it’s a boss, perhaps it’s the CEO of the company, but if you’re prepared to demonstrate the fruits of your labor with confidence, you’ll make a lasting impression on others.

Practice Business Writing:

The widespread use of text messaging has led many workers to take shortcuts in their daily correspondence. But slang has no place in the office. Keep e-mails professional, well-worded, and properly punctuated to avoid being seen in a negative light. If your written communication skills are lacking, consider taking a refresher grammar course or asking a colleague to proofread your e-mails before they go out.

Stop to Think:

Don’t feel as though you always have to deliver an immediate response to verbal and written communications. A hasty response can create a situation you can’t take back. This is especially true if you’ve received a communication that elicits an immediate negative reaction. Remember, cooler heads prevail. Feel free to ask the other person if you can have a little extra time to think about the issue at hand and carefully rehearse your own response before officially sending it.

Conclusion:

Effective communication skills are important for everyone in an organization, from the CEO to the summer interns. As a data professional, speaking to those who work in other departments can help open up opportunities that will ensure your career longevity within an organization. By opening up to others, you can share the basics about the work you do, potentially starting a line of communication about using Big Data to improve operations in human resources, accounting, sales, and other areas of the organization.

!!!!Thanks for Reading !!!!

Regards ,

Saurab sahu

GreyAtom

GreyAtom is committed to building an educational ecosystem for learners to upskill & help them make a career in data science.

Saurabh Sahu

Written by

GreyAtom

GreyAtom

GreyAtom is committed to building an educational ecosystem for learners to upskill & help them make a career in data science.

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