Mastering R Is The First Step For A Top-Class Data Science Career

In a world where 2.5 quintillion bytes of data are produced every day, a professional who can organize this humongous data to provide business solutions is indeed the hero! The mix of personality traits, experience and analytics skills required for the data scientist role is considered difficult to find, and, thus, the demand for qualified data scientists has exceeded supply in recent years.

Data Scientist’ is the sexiest job title of the 21st century. Data scientist topped the list of 50 Best Jobs in America by Glassdoor.com in 2016 and again in 2017, based on metrics such as job satisfaction, number of job openings and median base salary and the simple way to become one lies in R programming.

R, the language and environment for statistical computing and graphics, is the fastest growing open source competitor to commercial software packages like SAS, STATA and SPSS. R has earned the mettle of being a very powerful language used widely for data analysis and statistical computing. R has earned the mettle of being a very powerful language used widely for data analysis and statistical computing. Since its birth in the early 90s, R’s user interface has continually become more enhanced and interactive.

A few features of R which contributed to its leadership position are:

• Statistical analysis environment: R provides a complete environment for statistical analysis. It is easy to implement statistical methods in R. It includes tools for conventional and modern statistical models including Regression, ANOVA, GLM and Tree, in its object-oriented framework, which makes it easier to extract as well as merge the needed information rather than copying it. Most of the new research in statistical analysis and modelling is done using R. So, the new techniques are first available only in R.

• Open source: R is open source technology, so it is very easy to integrate with other applications. The coding of R is simple and easy to pick up, even for someone who wishes to learn R just as a standalone programming language. Even professionals from non-technology backgrounds — such as Sales, Marketing, Economics, Research, Science, Operations, among others — can learn R programming easily.

• Huge Community support: R has the community support of leading statisticians, data scientists from different parts of the world and is growing rapidly.

• Availability of packages: R comes with an exhaustive library of more than 10000 packages customized for different computation tasks. By leveraging these packages, one can gain high-performance computing experience.

• Benefits of Charting: R has some great tools to aid data visualization to create graphs, bar charts, multi-panel lattice charts, scatter plots and new custom-designed graphics. Unparallel charting and graphics offered by R language is highly influenced by data visualization experts. Graphics based on R programming can be seen in blogs like The New York Times, The Economist, and Flowing Data.

The R vs. Python battle

There is a very close battle when it comes to choosing between R and Python. For a flourishing data science career, you have to master at least one of these two languages. One tends to favour R a little more since it is better suited for conducting complex exploratory data analysis. R has some unique features that are important for data science applications. Some of these features are explained below:

• R being a vector language can perform many operations at once. Adding functions and avoiding a loop is a feature of R makes it more powerful and faster than the other languages.
• R doesn’t need any compilers as it’s an interpreted language. Unlike other languages like Python, Java or C, R directly interprets the code into a full-fledged program making the development easier.
• There is no better option for statistical analysis and graphs than R, with capabilities around matrix multiplication available straight out of the box. As the power of R is being realised, it is finding use in a variety of other places, starting from financial studies to genetics and biology and medicine. This is because R is a Turing-complete language, which means that any task can be programmed in R.
• R provides support functions for data science applications. Some of them are charts, graphs, data interface, statistical functions, etc. All these functions are specifically used for data science applications and statistical analysis.
• The ability of R to translate math to code seamlessly makes it an ideal choice for someone with minimal programming knowledge but wants to become a data scientist. On the other hand, Python may not have as many packages and libraries as R, but it does have tools like Pandas, Numpy, Scipy, Seaborn etc. that perform the same duties. For sheer ease of learning, R might be a slightly better option.

The Big guns have committed to R

Every industry, from automobile to social networking to banking, has committed to using R programming for effective data analysis. Ford Motors built features in its Fiesta car after analyzing social chatter around dream functionalities that users wish for in a car. Facebook uses R programming to categorize status messages into 68 categories and deriving a metrics around the nature of status messages get posted at different times of the day. Google not only uses R but wrote standards for the language that is overall widely accepted. Microsoft purchase Revolution Analytics, which is kind of the commercial version of R and developed servers and services on top of it. Uber could successfully generate insights around reduction in drunken driving cases across the US ever since it increased its fleet size.

These are just a few of the many interesting use-cases of R adoption. Every day, somebody new commits to R and begins a new and innovative data science journey.

Edureka has a specially curated Data Science Course Online that helps you gain expertise in Machine Learning Algorithms like K-Means Clustering, Decision Trees, Random Forest, Naive Bayes. You’ll learn the concepts of Statistics, Time Series, Text Mining, and an introduction to Deep Learning as well. New batches for this course are starting soon!!