Introduction:DATA SCIENCE WITH –R!

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Instructed by Abrar Hussain Development / Programming Languages


R- R is the lingua franca of statistics. It is a free and open source programming language used to perform advanced data analysis tasks.

If you are looking to start a career in data science or to gain the skills to be able to transition to this field in the future. Then you are probably doing some research on which of these three programming languages you should learn first to maximize your chances of landing your dream job. you should focus on mastering R .

Take a look at these some factors as a starting point to help you decide:

The Industries where tool is used

Burtch Works,HR company, over 1000 quantitative professionals which language they preferred R . Here are some survey results:

R and Python, on the other hand, are used by Startups and mid-sized firms. Tech and Telecom companies require huge volumes of unstructured data to be analyzed, and hence data scientists use machine learning techniques for which R and Python are more suitable.

R is free software that can be downloaded by anyone any time .

To analyze data in R, you will use data mining libraries . you won’t code in native R language when analyzing data. The code you write in these libraries looks somewhat similar to the code you write in R. Hence, it is easier to learn R .

Data Science capabilities

R is known for In-memory analytics and is mainly used when the data analysis tasks require a standalone server. R is an excellent tool for exploring data. Currently, R has more than 5000 community contributed packages  in CRAN. The wide range of packages and modules available for statistics and data analysis makes it the most popular and powerful language in data science.  Statistical models can be written in a few lines of code.

You can draw complicated graphs beautifully in R using packages like Ggplot2, lattice, rCharts, etc.

Community Support

R has 125 active user groups worldwide, and the number of user group meetings has increased by a significant amount in the last year.  Python has 1,657 user groups, its communities strictly focused on data is much less when compared to R.

R have huge online community support- mailing lists, user-contributed code and documentation.


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