skills data scientist great learning
skills data scientist great learning

While you can find plenty of stuff on the internet about the skills required to become a data scientist, chances are you have already realized that getting started in data science is as hard as pulling off a 2-minute plank after being inactive for 2 years. But if you have the perseverance of a polar bear standing still with baited breath for 18 hours at a stretch to hunt or the resolve of Bruce Lee, chances are that you will make it.

Now, the skillset 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. So, before we go on to the technical stuff, here is a list of 5 non-technical skills that you must possess:

  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.
  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.
  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. In good old days, Math was a subject based on common sense and the need to resolve basic problems based on logic. This hasn’t changed much, though the scale has blown up exponentially. 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.
  4. Teamwork – Another feather in the cap that data scientists can’t do without is teamwork. While it may seem they can work in isolation, they are deeply involved in the organization at different levels. On one hand, they 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; rather the team will have to inculcate the same characteristics across the organization for the best utilization of insights they draw for various departments.
  5. Intellectual Curiosity and Passion – This is a tad-bit cliched but true. 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!
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