R Programming for Data Science | A Complete Courses to Learn

Learn R Programming for Data Science OnlineWhat is R programming?

R is a powerful language and environment that is used for statistical computing and graphics. Very similar to the S language and environment that was developed at Bell Laboratories by John Chambers and colleagues, while the R programme was a part of the GNU project. R can be considered as a different implementation of S and there are some important differences while so much of their code is similar for both S and R.

R provides a lot of variety for statistical including linear and non linear modeling, classical statical tests among others as well as for graphical technique which is quite extensible. The S language is, therefore, a great choice for research in statistical method and R provides an Open source to match the requirements.

One of the biggest strengths of R is that it is easy to integrate it with publication quality plots including symbols and formulas related to mathematical equations. So while great care has been taken to ensure default choices in minor design changes, the user is more often than not in full control of the software. In addition, R is available as a free software under the terms and conditions of Free Software Foundation’s GNU General Public License. It is capable of being compiled and run on a number of platforms including UNIX platforms, MacOS and Windows.

An integrated suite for software facilities that are used for data manipulation, calculation, and graphical display, it has the following features:

1. A good data handling and storage capability
2. A suite of operators that can calculate a variety of matrices and arrays
3. A large, coherent and integrated collection of tools that can effectively help companies to analyse data
4. Graphical facilities for data analysis and displays which can be availed either on the screen or through hard copies
5. A well developed simple and effective programming language that includes loops, conditionals, and user-defined recursive functions.

In short, R is a programming language and software environment that is used for statistical computing and graphics. The R language is further mostly used by statisticians and data miners so that they can develop new and innovative statistical software and data analysis methods. Polls, surveys of data miner and studies have effectively proved that the popularity of R software has increased substantially in the recent years.

What are some of the advantages of R software?

Some of the benefits of R programming include the following:

1. It allows data analysts to access every technique at their fingertips: R software includes almost all types of data manipulation, statical models, and charts that a modern scientist would ever need or require. Through this software, they can easily find, download and use cutting-edge community reviewed methods in the field and predictive modeling to help them find effective solutions to their need, free of cost.
2. It allows to create beautiful and unique Data visualisations in an effective fashion: Representing complex data with charts and graphs is an integral part of data analysis process and R goes far beyond the traditional methods. Heavily influenced by important influencers in the field of Data visualisations like Bill Cleveland and Edward Tufte, R makes it easy for scientists to draw conclusions from multidimensional data like 3D surfaces and multi-panel charts. Many stunning infographics that appear on big magazine brands like New York Times, The Economist and the FlowingData blog have made use of the charting capabilities of R software to create their designs.
3. It allows users to gain results in a faster manner: Instead of using inflexible black-box procedures or point and click menus, R enables users to use a programming language that is exclusively used for data analysis and interpretation. Intermediate level R programmers create data analysis that is much faster than users who make use of legacy statistical software as R users have the capability to mix and match to ensure faster results. Besides R scripts are automated in an easy fashion, making them the ideal choice for both reproducible research and production deployments.
4. It ensures that talents collaborate on a global level: As R programme is a thriving open source project, it is used by a community of more than 2 million users and thousands of developers on a global scale. So whether scientist uses it to optimise their portfolios or analyse genomic sequences or predict component failure time, they are used by experts across all categories, making it possible for other people to access the concerned resources, codes and application, free of cost online..

What is data science?

Many of us have heard it that statistics is one the next sexy jobs that are coming up in the career opportunities (this fact is vouched by even Hal Varian). Almost five years Tim O’Reilly said that data is the next big thing to happen in the world. But what exactly is data and why is it so important? And why is there so much importance being given to statistics and data in the world today?

The web is full of apps that are driven by data. All the e-commerce apps and websites are based on data in the complete sense. There is a database behind a web front end and middleware that talks to a number of other databases and data services. But the mere use of data is not what comprises of data science. A data application gets its value from data and in the process creates value for itself. This means that data science enables the creation of products that are based on data.

One of the earliest data products that were available on the internet was the CDDB database which was when the developers of this product realised that CD had a unique signature for all the tracks that were recorded on the CD. This is what inspired the Gracenote to build a database of track lengths and with it added the metadata of the album. A good example of this is when you make use of iTunes to rip a CD, then this is an example of the kind of the database. So before iTunes does anything, it reads the length of every tack and then sends to the CDDB software. After this, it gets back to the track titles. In case you have a CD that is not created by you then you can make an entry for an unknown album. While this might sound relatively simple, it was a revolutionary step in the field of data management. This is because the CDDB software was viewing music as data and not as an audio source and thereby creating massive value for it. So while the CDDB software had nothing to do with music, it was revolutionising the industry, just by viewing the audio issue within the industry as an issue that deals with data.

Google has always been an expert at creating data products. Some instances where they have done this include the following:

1. One of the breakthrough activity of Google was its realisation that a search engine could generate inputs on things other than text on a page. The page rank algorithm of Google was among the first to use data outside the page and in particular, helped to link back to the page. Tracking of links made Google very useful and PageRank has been very integral to the success of the company as well.
2. While spell check is not a very big thing, but by suggesting possible corrections to words that have spelled incorrectly and observing the user clicks in response, Google has ensured that it is much more accurate and to the point. They have built a dictionary of common misspellings, their corrections and the contexts that they might incur during the process.
3. Speech recognition has always remained a very difficult and problematic problem. But in the last few years, Google has made massive strides in the field of voice data that they have collected and since then they are trying to integrate voice searches into their core search engine in a more comprehensive and intricate manner.
4. The Swine Flu epidemic of 2009 was an example where Google successfully tracked the progress of the epidemic by following searches on flu-related topics, conducted by people across the globe.

This is not to say that Google is the only company that is making use of data in an effective fashion. Facebook and LinkedIn are other big social media giants that make use of data to suggest patterns of friendship relationships. or people a user might know, with amazing accuracy. In a similar manner, Amazon saves the search, correlates the searched item and comes up with an appropriate recommendation that in turn help to drive the retail business of the brand. They have come to understand that a book is not just a book, a gadget is not just a gadget and a customer is much more than a customer because they leave behind a trail of data that can be mimed and put to use in an effective fashion. The thread that ties most of these applications together is that through these above sources value can be added to the data collected. So whether data is collected in terms of voice searches or product reviews, they all contribute in adding value and this is, in reality, the essence of data science.

R Programming for Data Science Course Description

1. Overview and History of R: Here you will learn about how R as a software evolved and grew in the industry
2. Datatypes and Basic Operations: Here you will learn about data types and how to operate them in an effective fashion
3. Reading Data: This section will help you understand how to read data in an effective fashion
4. Scoping Rules: In this section, you will learn the importance of scoping and its related rules

What are the requirements to learn data science using R programming

A basic training in R programming is a really good idea because it will help you learn the fundamentals of R that are necessary for data analysis and interpretation. Here are some skills that individuals will need to possess before they enroll in this course:

1. If you are interested in being a part of this course, it is important that you have at least a basic understanding of how a computer system operates
2. You must have a basic understanding of basic programming concepts
3. You must have at least some knowledge about statistical reasoning
4. Being a passionate learner will help you learn more about the industry in an effective manner

Who should undertake this R Programming for Data Science Training?

R Programming for Data Science ideal target audience for this course would include individuals who are trying to get into analytics domains. Graduates who are trying to discover a new career field and are passionate about working in an exciting industry is also perfect for this course. In addition, anyone who wants to learn about R programming would be an effective target audience for this course as well.

R Programming for Data Science FAQ’s

1. Is it difficult to become trained in the field of R Programming for Data Science?

Definitely not. In fact, most of the skills needed for students who want to learn about data science is pretty easy especially if you are passionate about the field. Many individuals who have a basic understanding of statistical analysis and data interpretation can successfully complete this course. In fact, after learning about this software, they can effectively enhance their resume as they will have an extra competitive edge over other applicants and people who are in the same field.

1. Where can one apply to learn about R Programming for Data Science?

There are many institutes and organisations that offer this kind of training for individuals who are interested. In fact, learning about data science using R programming is one way in which statistical reasoning students can stand out in the crowd. At EDUCBA, we bring together the best resources and techniques to help our students gain an intricate and detailed understanding of the R software and the manner in which data impacts organisations/companies. After this course, individuals will be able to help brand managers and website owners to use data in a manner that will empower and strengthen their brand image, not just locally but globally as well.

Career Benefits of this R Programming for Data Science Training

Everyone knows that data is what is driving the world today. And without a proper understanding of how to create value from the volumes of data lying around in every office, no brand can maximise its potential and reach. This has effectively created the need for individuals who can not just understand data bit help brands to discover value from them. That is why there are multiple benefits for individuals to invest their time and resources in learning about data in an effective manner. When brand managers can effectively understand data, they can have a direct impact on various aspects of their brand including customers, target audience, goal creation, and management of resources.

Understanding the world of data science can, therefore, help

you to broaden your understanding of the field on one hand and improve your resume value on the other hand. This can make you a valuable addition to the company in an effective fashion.

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