Data scientists thrive on data. They are professionals who are responsible for collecting, analysing and interpreting huge amounts of data, to gather solutions to all business problems and challenges. The data could be structured or unstructured. The data scientist collates the entire data and comes up with action plans for organisations. They are responsible for observing the latest data trends and utilising them to create feasible solutions. To be a data scientist is no joke. At any given point in time, a data scientist is a mix of an analyst, a mathematician, a statistician and a technical programmer. To be a professional in data science means to a very diverse and sound technical skill set along with business acumen and high intellectual curiosity. They need to be particularly great at communication, teamwork and need to have a formidable attitude as they are crucial thinkers of problem-solving solutions.

The true professionals in data science actually work on intuition. They need to play around with data and understand the A-Z of it and whether it makes sense to them. They should be able to reflect on the outcomes of the analysis beforehand and should be able to work around with all difficulties like missing values, unstructured data etc. Their natural curiosity should help them visualise the possibilities. They also need to be meticulous as they are handling huge volumes of data and incredibly patient.

Data Scientists v/s Data Analysts

This confusion stems from the fact that companies have different names for people handling data. Moreover, there’s no black and white to this demarcation. While data scientists have a lot to do with the whole data analysis process, data analysts are restricted to organising the data and analysing it using standard tools and techniques. They also present the data in readable formats for the laymen to read and digest information. Whereas, data scientists read and understand data from a business perspective. A data analyst works on guided paths and pursues answers to questions that are given. Data scientists actually think and bring about questions to the table. A data scientist is many times forced to think about the statistical models he will use to make sense of the data, or advanced programming for the same purpose, whereas a data analyst doesn’t have to deal with the models or programming for that matter. Analysts are also not expected to lay out action plans for the business.