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Data Science Programming: Python vs R

With every industry generating massive amounts of data – the need to crunch data requires more powerful and sophisticated programming tools like Python and R language.

Data Science with R Language

Millions of data scientists and statisticians use R programming to get away with challenging problems related to statistical computing and quantitative marketing. R language has become an essential tool for finance and business analytics-driven organizations like LinkedIn, Twitter, Bank of America, Facebook and Google.

R is an open source programming language and environment for statistical computing and graphics available on Linux, Windows and Mac. R language has an innovative package system that allows developers to extend the functionality to new heights by providing cross-platform distribution and testing of data and code. With more than 5K publicly released packages available for download, it is just a great programming language for exploratory data analysis language can easily be integrated with other object oriented programming languages like C, C++ and Java. R language has array-oriented syntax making it easier for programmers to translate math to code,in particular for professionals with minimal programming background.

Why use R programming for data science?

  1. R language is one of the best tools for data scientists in the world of data visualization. It virtually has everything that a data scientist needs- statistical models, data manipulation and visualization charts.
  2. Data scientists can create unique and beautiful data visualizations with R language that go far beyond the out-dated line plots and bar charts. With R programming, data scientists can draw meaningful insights from data in multiple dimensions using 3D surfaces and multi-panel charts. The Economist and The New York Times exploit the custom charting capabilities of R programming to create stunning infographics.
  3. One great feature of R programming is its reproducible research-the code and data can be given to an interested third party which can trace it back to reproduce the same results. Thus, data scientists need to write code that will extract the data, analyse it and generate a HTML, PDF or a PPT for reporting. When any other third party is interested, the original author can share the code and data with the third party for reproducing similar results.
  4. R language is designed particularly for data analysis with a flexibility to mix and match various statistical and predictive models for best possible outcomes. R programming scripts can further be automated with ease to promote production deployments and reproducible research.
  5. R language has rich community of approximately 2 million users and close to 1000’s of developers that draws talents of data scientists spread across the world. The community has packages widespread across actuarial analysis, finance, machine learning, web technologies,pharmaceuticals that can be of great help to predict component failure times, analyse genomic sequences, and optimize portfolios. All these resources created by experts in various domains can be accessed easily for free, online.

Applications of R Language

  • Ford uses open source tools like R programming and Hadoop for data driven decision support and statistical data analysis.
  • The popular insurance giant Lloyd’s uses R language to create motion charts that provide analysis reports to investors.
  • Google uses R programming to analyse the effectiveness of online advertising campaigns, predict economic activities and measure the ROI of advertising campaigns.
  • Facebook uses R language to analyse the status updates and create the social network graph.
  • Zillow makes use of R programming to promote the housing prices.

Limitations of R Language

  • R programming has a steep learning curve for professionals who do not come from a programming background (professionals hailing from a GUI world like that of Microsoft Excel).
  • Working with R language can at times be slow if the code is written poorly, however, there are solutions to this like FastR package, pqR and Penjin.

Data Science with Python or R Programming- What to learn first?

There are certain strategies that will help professionals decide their call of action on whether to begin learning data science with Python language or with R language –

  • If professionals are aware of the fact on what kind of project they will be working on then they can make a decision on which language to learn first. If the projects requires working with jumbled or scrape data from files, websites or any other sources of data then professionals must first start their learning with Python language. On the other hand, if the project requires working with clean data then professionals must first learn to focus on the data analysis part which requires learning R programming first.
  • It is always better to be on-par with the teams so find out what data science  programming language are they using R or Python. Collaboration and learning becomes much easier if you and your team mates are on the same language paradigm.
  • Trends in increasing data scientist jobs will help make a better decision on which what to learn first R language or Python language.
  • Last but not the least, do consider your personal preferences as to what interests you more and which is easier for you to grasp.

Having understood briefly about Python language and R language, the bottom line here is that it is difficult to choose learning any one language first -Python or R to crack data scientist jobs in top big data companies. Each one has its own advantages and disadvantages based on the different scenarios and tasks to be performed. Thus, the best solution is to make a smart move based on the above listed strategies and decide which language you should learn first that will fetch you a job with big data scientist salary and later add onto your skill set by learning the other language.


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