python

Eight Steps to become a Data Scientist ! (The Sexiest and the Hot Job of the Decade)

Data Lover data-science, R , , , , , , , , , , , ,

Thinking how to become a Data Scientist? Here we go, the 8 Steps to become a Data Scientist (The Sexiest and the Hot Job of the Decade)

Well, these steps are not so easy but possible if we try. Most of the steps come with no-cost or very low-cost.

https://i0.wp.com/blog.datacamp.com/wp-content/uploads/2014/08/How-to-become-a-data-scientist.jpg

Thanks for DataCamp for the nice infographic. Is this info useful? Then please share this info with your circle.

Clash of the Titans ! (R vs Python)

Data Lover R , , ,

This is to all out there who are wondering which is better language to learn for data analysis and visualization. Whether one should use R or Python when they do their everyday data analysis tasks.

Both Python and R are amongst the most extensively held languages for data analysis, and have their supporters and opponents. While Python is a lot praised for being a general-purpose language with an easy-to-understand syntax, R’s functionality is developed with statisticians in thoughts, thus giving it field-specific advantages such as excessive features for data visualization.

The DataCamp has recently released a new infographic for everyone interested in how these two (statistical) programming languages relate to each other. This superb infographic discovers what the strengths of R over Python and vice versa, and aims to provide a basic comparison between these two programming languages from a data science and statistics perspective.

R vs Python for data science

Note:

Not to ignore the new entrant in war field “Julia” language. It is a high-level dynamic programming language designed to address the requirements of high-performance numerical and scientific computing while also being effective for general purpose programming. Influenced by MATLAB, C, Python, Perl, R, Ruby and others.

Soon we expect Julia to join the clash !