What Does a Data Scientist do? Who is Data Scientist? He/she is responsible for collecting, analyzing and interpreting the results, through a large amount of data. This process is used to take an important decision for the business, which can affect the growth and help to face compititon in the market. Before looking at what Data Science Skills you will need to know what exactly a data scientist do? So, let’s find out what are the roles and responsibilities of data scientist. A data scientist analyzes data to extract actionable insight from it. More specifically, a data scientist: Determines correct datasets and variables. Identifies the most challenging data-analytics problems. Collects large sets of data- structured and unstructured, from different sources. Cleans and validates data ensuring accuracy, completeness, and uniformity. Builds and applies models and algorithms to mine stores of big data. Analyzes data to recognize patterns and trends. Interprets data to find solutions. Communicates findings to stakeholders using tools like visualization. Tech and Non-Tech Skills for Data Scientist A domain of Important Skills for Data Scientists We can divide the required set of Data Science skills into 3 domains Analytics Programming Domain Knowledge

How many calories do agencies spend building reports for clients every month? Based on the results of a recent survey we ran–a lot. 58% of the agencies with spoke with say it takes an average of 4 hours per client to prep for a client reporting meeting. Another 23% said it takes one full day to prepare. This clarifies a point I think most people already know–client reporting meetings are a big investment of time. With that in mind, we talked with 31 marketing agencies in an effort to learn the insights and experiences that lead them to run productive meetings that lead to action.

Nearly every business leader (99% according to an HBR study) recognizes the need to build a data driven organization. However, so many struggle to use data for a variety of reasons, a big one of them being that data exists in all sorts of places. This is especially the case with startups, who use a number of out of the box tools that don’t connect to each other, and that only offer so much granularity and flexibility. The solution? A data warehouse.