Big Data Analytics - Data Scientist

The role of a data scientist is normally associated with tasks such as predictive modepng, developing segmentation algorithms, recommender systems, A/B testing frameworks and often working with raw unstructured data.

The nature of their work demands a deep understanding of mathematics, appped statistics and programming. There are a few skills common between a data analyst and a data scientist, for example, the abipty to query databases. Both analyze data, but the decision of a data scientist can have a greater impact in an organization.

Here is a set of skills a data scientist normally need to have −

Programming in a statistical package such as: R, Python, SAS, SPSS, or Jupa

Able to clean, extract, and explore data from different sources

Research, design, and implementation of statistical models

Deep statistical, mathematical, and computer science knowledge

In big data analytics, people normally confuse the role of a data scientist with that of a data architect. In reapty, the difference is quite simple. A data architect defines the tools and the architecture the data would be stored at, whereas a data scientist uses this architecture. Of course, a data scientist should be able to set up new tools if needed for ad-hoc projects, but the infrastructure definition and design should not be a part of his task.