A great data scientist not only know how to test and optimize the data. When it comes to the results, he explains his processes for the non-technical team. That's why great communication skills are a component of Data Scientists tools belt.
Communication skills will permit to explain the technical results for the non-technical skills that will lead to a better understanding of the data. It's a must-have skill when dealing with decision makers, marketing and sales departments.
Because decision makers need actionable solutions driven by quantitative insights.
By following a storyline, people are more inclined to catch data insights. It is also useful to know how it can impact a business and less focus on long analyses. Visualization skills help better communication with non-technical people. Tableau is a tool for data visualization and you see an emerged trend from there. Finally, check the ethical side of your propositions and be sure that it is acceptable.
Analysis skills are the most demanded skills with Machine Learning according to Glassdoor.
Data scientists need to be critical thinkers and give objective analysis. It permits to formulate pertinent opinions and judgments.
Large amounts of Data need to have great patterns and trends in the data. They also have to explain these analyses to their team and know tools such as:
Hypothesis skills are a great arrow to add to a Data Scientist arch. It is useful for companies looking for data-driven problem-solver. Good hypothesis use the most important points. This sounds a little bit creepy but it is easy to focus onto a lot of tiny details.
Large companies use Machine Learning when they work with a lot of Data. Netflix, Google Maps or Uber use Machine Learning models. Machine Learning models can be implemented using R or Python libraries. Because to become an expert, really understand different techniques.
Machine Learning provides the ability to do certain tasks, such as recognition, diagnosis, planning, robot control, prediction, without being explicitly programmed.
Statistical skills are important even if companies do not ask for it everytime when they hire.
Yet, good statistical skills give a better use and understanding of underlying projects.
Being able to understand significant data variations helps to deliver accurate data-driven conclusions
Finally, statistical skills will help using programming languages better.
What means to be a great problem solver?
More than solving problems, great problem solvers define problems by themselves.
Then, being adaptable and ready to act fast and with a plan.
Problem solvers look at the situation from different perspectives because it will improve understanding.
Problem solvers are able to explain results with simplicity is another characteristic of problem solvers.
Problem solvers have an intuitive understanding and intuitive data insights.
Finally, problem solvers looks for simple and pragmatic answers.
Related subjects from the guide: Data Scientist Job: Role, Skills, Education, Tools and Salary