A data scientist takes enormous masses of messy data (unstructured and structured) and uses their mathematical and programming skills to clean, massage, and organize them. They then apply their analytical skills and industrial background to uncover hidden solutions to various business challenges.
A data scientist is an engineer with a strong mathematical background and solid programming fundamentals, a skill set that provides the ability to better define market expectations. With the help of statistical tools, a data scientist is able to analyze the actual customer needs and predict their future preferences, which are created automatically by anomaly detection systems and performance tracking solutions.
It is safe to say that data scientists are expected to master three main aspects. First, they must be able to know what the problem is that needs to be solved in the company. For example, while working for a car manufacturer, they must know if they are trying to reduce the cost of production or if they want to predict customer preference trends. Second, they must be able to understand how to formulate this problem in a mathematical way. Finally, they must get the necessary datasets and combine them to solve the problem.
In our example, data scientists must look at the cost of parts to understand how to reduce production expenses or go on social networks to explore the latest trends. In the final stage, they implement the algorithm to translate it into a computer programming language.