Learn The Difference Between R and Python in Data Science today.
The Difference Between R and Python in Data Science

Data science is a multi-disciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from structured and unstructured data. Data science is the same concept as data mining and big data: "use the most powerful hardware, the most powerful programming systems, and the most efficient algorithms to solve problems".

1- R

R is one of the most popular languages in the world for data science. Built specifically for working with data, R provides an intuitive interface to the most advanced statistical methods available today. Here are a few highlights of the language:

Built specifically for data analysis and visualization
One of the most popular languages for data science
Preferred by statisticians and academic researchers
Language of choice for cutting edge statistics
Vast collection of community-contributed packages
Rapid prototyping of data-driven apps and dashboards

A Data Scientist combines statistical and machine learning techniques with R programming to analyze and interpret complex data. Join

DataCamp's Introduction to R

In this introduction to R, you will master the basics of this beautiful open source language, including factors, lists and data frames. With the knowledge gained in this course, you will be ready to undertake your first very own data analysis. With over 2 million users worldwide R is rapidly becoming the leading programming language in statistics and data science. Every year, the number of R users grows by 40% and an increasing number of organizations are using it in their day-to-day activities. Leverage the power of R by completing this R online course today!

2- Python 

Python is one of the world’s most popular programming languages. Whether you want to build a website or a machine learning model, Python can get you there. Here are a few highlights of the language:

General purpose programming language
Easy to read and write (and learn!)
One of the most popular languages for data science
Preferred by computer scientists and programmers
Language of choice for cutting-edge machine learning and AI applications
Commonly used for putting models into production

A Data Scientist combines statistical and machine learning techniques with Python programming to analyze and interpret complex data. Join

Learn Data Science Online for free on DataCamp

The skills people and businesses need to succeed are changing. No matter where you are in your career or what field you work in, you will need to understand the language of data. With DataCamp, you learn data science today and apply it tomorrow.

DataCamp’s lessons are bite-sized so you can learn in a way that fits your schedule, on any device. Tracks conveniently order the courses so you can find what fits your needs at a glance.

Earn certificates as you complete courses covering the entire data science workflow. Whether you’re just starting out or a seasoned pro, you’ll improve your skills in data manipulation, data visualization, statistics, machine learning, and more.

DataCamp's Introduction to Python

Python is a general-purpose programming language that is becoming ever more popular for data science. Companies worldwide are using Python to harvest insights from their data and gain a competitive edge. Unlike other Python tutorials, this course focuses on Python specifically for data science. In DataCamp's Introduction to Python course, you’ll learn about powerful ways to store and manipulate data, and helpful data science tools to begin conducting your own analyses. Start DataCamp’s online Python curriculum now.

Bonus:

Data science is a "concept to unify statistics, data analysis, machine learning and their related methods" in order to "understand and analyze actual phenomena" with data. It employs techniques and theories drawn from many fields within the context of mathematics, statistics, computer science, and information science. 

Turing award winner Jim Gray imagined data science as a "fourth paradigm" of science (empirical, theoretical, computational and now data-driven) and asserted that "everything about science is changing because of the impact of information technology" and the data deluge. In 2015, the American Statistical Association identified database management, statistics and machine learning, and distributed and parallel systems as the three emerging foundational professional communities.