Mastering R Is The First Step For A Top-Class Data Science Career
Recommended by 37 users
Data Science is the sexiest job of the 21st century. With data science rapidly growing as the most desirable tech job title across the globe, it is essential to understand what it takes to ride the data science wave. The simple answer 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. Since its birth in the early 90s, R’s user interface has continually become more enhanced and interactive. But the biggest reason for the sudden hype around R lies in its ability to implement machine learning algorithms in a fast and simple manner and easy integration with other widely used platforms like Hadoop. R is today the language of choice for data science.
The journey of R programming began with the purpose to develop a language that focused on delivering a better and more user-friendly way to perform data analysis, statistics and graphical models. For the first few years, R was primarily used in academics and research, but lately the enterprise world is discovering the hidden potentials of R. Today, R is the fastest growing statistical language in the corporate world. A few features of R have contributed to its leadership position are:
Huge community backing
One of the main strengths of R programming is its huge community that provides support through mailing lists, user-generated content and a very active Stack Overflow Group. This is further amplified by CRAN – a huge repository of R packages to which users can contribute.
Ease of coding
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.
Availability of packages
R comes with an exhaustive library of around 7800 packages customized for different computation tasks. By leveraging these packages, one can gain high performance computing experience.
The R vs. Python battle
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 also integrates well with other computer languages like C++, Java, and C. For statistical analysis and graphs, there is no better option than R, with capabilities around matrix multiplication available straight out of the box. 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, Seabornetc 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. 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 which 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!!