Big Data

Anatomy of the Data Scientist

20/12/18

What is a Data Scientist?

The most common definition of a Data Scientist is that of an expert in solving complex problems in different disciplines (finance, marketing, industry, etc.) making use of data analysis with statistical and computer tools. Much has been written about what is now considered to be the sexiest professional career of the 21st century, and in order to become a Data Scientist, a long path of assimilation of new technological and mathematical knowledge, as well as a set of personal skills, awaits.

 

Nowadays, this path can be easily initiated due to the wide course offer and the vast online community. This path seems to have no end, because being a Data Scientist is much more to acquiring this set of capabilities in a short period of time. It requires a maturity and learning process that can only be achieved through experience, adaptation to change and the effort to continuously learn new things.

Aptitudes and attitudes

To illustrate all of the above, you can consider up to six skill groups that shape this complex profile:

There are many attempts to outline the capabilities of the perfect Data Scientist, as well as the path that those who want to develop their career in this field must follow.

 

This infographic from Swami Chandrasekaran is a good example of the path to follow in order to become a good Data Scientist:

Original image: https://datafloq.com/read/long-road-big-data-scientist-infographic/423

 

Another interesting example would be to start studying and analysing this Periodic Table:

Regarding the technological tools used to help the work of the Data Scientist, the quantity is constantly increasing. Here we can see a summary of the main technological standards in the data industry, many of them used in Santander Global Tech:

If we want to have a more complete vision of the set of technologies, we can also check theBig Data Landscape 2018
In the next article, we will analyze the different profiles and solutions that companies are finding to cover the needs that the new data industry is generating.

Author: Jesús López

 

Santander Global Tech

 

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