R is Our Mighty Programming Language

R – The name comes from the initials of its developers Ross Ihaka and Robert Gentleman, who created R programming language for statistical analysis, graphics representation, and reporting. With the passage of time, R has diversified and entered innumerable sectors, with many people declaring it “hot” and many adjudging it as “getting even hotter”.If you are gauging the success of R programming, you need to have a look at the list of companies that use it for handling variety of issues which they face on daily basis.  Revolution Analytics creates a list of companies that use R programming as a fundamental tool for data management and data analytics. However, to understand the expanding horizon for R programming, and to know how mighty R Programming has become, have a look at the following data that discusses about various sectors wherein R Programming is valued incessantly.

Banking Sector

According to data collected by Revolution Analytics, Banks and Financial sector depend heavily on R Programming for various functions such as that of Credit Risk Analysis and Reporting. The names that can be associated with R Programming are Bank of America, and ANZ, one of the leading banks in Australia

Non-Profit Organisations:

Non-profit organisations such as Benetech and Human Rights Data Analysis Group (HRDAG) use R programming for answering geopolitical questions and for analysing human rights data respectively (Revolution Analytics).

Real Estate:

Real-estate agencies depend on R programming and Data Science for predictive analysis. They perform data analysis on the collected data in order to predict sales and purchase, and to formulate and finalise prices of the property

Media and Newspapers:

Media and Newspapers rely on R Programming for the tasks it can perform. Many newspapers such as The New York Times depend on R Programming for Data Visualisation. Similarly, newspapers and media import data for weather forecasting from weather forecasting agencies, which in turn are heavily dependent on R programming for predicting weather forecasts wherein R programming is as efficient as generating graphics for flood/drought/or other famine possibilities

Social Networking Sites:

Social Networking sites such as Twitter and Facebook make use of R programming for multiple functions. Data Scientists working in the Twitter Analytics Domain try to extract meaningful data out of millions of tweets and after analysing the emotional and sentimental quotient hidden within tweets, they try to find out some common observations for the benefit of the concerned entities or organisations.

Aerospace and Flight Aviation Industry:

Aviation industry is one such industry where R Programming is considered as one of the essential “must-haves” since it helps in predicting the flight status, delays, scheduled time, and actual in flight time.

Stock Market Exchange: 

R programming is equally reliable in Stock Market Exchange. It has emerged as a brilliant programming language that ensures smart Business Intelligence in terms of prediction, analysis, and the formulation of policies in the process.

While going through the above mentioned sectors and their dependency on R programming, one finds an appropriate answer to the question that asks “What is the future of R Programming?” The answer to this question is “Future is here and now”. Learn R Programming, build a strong foundation for a remarkable career in Data Science, become an efficient Data Scientist – the mightiest, with the hottest job in your pocket!

You may definitely have a remarkable career in Data Science if you are able to get hands-on training in it. ETLhive organises comprehensive lectures on Data Science, during which the highly-qualified industry-experienced training Professionals at ETLhive impart knowledge on varied concepts and skills associated with Data Science. At ETLhive, you will go extensive training with hands on experiences in Data Science and Machine Learning, Data Manipulation using R, Machine Learning Techniques Using R, Supervised Learning Techniques and the implementation of various Algorithms, Unsupervised Machine Learning Techniques – Implementation of different algorithms, Regression Methods for Forecasting Numeric Data, and Deep Learning – Neural Networks and Support Vector Machines. Get trained at ETLhive and get hired for the hottest job of the century – a Data Scientist!