If you’re new to the world of data analysis, you’ll soon see that proficiency in statistical programming languages is among the most crucial skills you could have. Data analysis professionals use Structured Query Language (SQL) when they communicate with databases. However, to clean, manipulate, analyse and visualise data, Python or R are your best options.

What is Python?

Python is one of the most popular and high-level programming languages in the world today. This general-purpose language is recognised for mimicking the English language with its intuitive syntax. There are several applications for Python today, but its leading applications are:

  • Web app development
  • Automation and scripting
  • Data analysis and data science

What is R?

R is a statistical and software environment programming language created for data visualisation and statistical computing. R has multifold capabilities that can be clubbed under three categories:

  • Data visualisation
  • Data manipulation
  • Statistical analysis

Python vs R - How Do I Choose?

Off the bat, you can never go wrong with either of these programming languages. Both R and Python are highly in-demand and help you tackle nearly every data analytics job that comes your way. The right one to choose will depend on your interests, career objectives and industry background.

While there’s no wrong choice, choosing the right programming language for your career path can be tough. Here are a few factors you should consider:

The learning curve:

Both R and Python are comparatively easy languages to learn. Originally, Python was created to aid with software development. If you have a knowledge background in C++ or Java, Python will come more naturally to you than R. But if you have a knowledge background in statistics, R might seem easier to learn. Looking at it objectively, Python’s syntax is much easier to read which means the learning curve is not too steep. R has a steep learning curve at the start but gets much easier once you get the hang of it.

Work environment:

By nature, R is a tool for statistical analysis that engineers, academics and data scientists without programming knowledge use. Python is a high-level production-ready language that is used for several industrial, engineering and research applications.
Generally speaking, it’s easier and more sensible to learn a language that everyone around you already uses. The same is true for programming languages too. If you are just entering the world of programming languages, it might be hard to figure out which language every company uses while applying for jobs. Before you start learning, look up job listings for the roles you are interested in and see their job requirements. Are more companies looking for Python professionals or R? This can give you a fair idea of which language will give you better opportunities.

Strengths and weaknesses:

Both R and Python can achieve the same goals using different methods. Each language has its strengths and weaknesses. If there are specific tasks that you will spend more time on, go for the language that can help you achieve those goals.
Python works better if you handle large volumes of data, build models for deep learning and perform non-statistical operations like saving data in databases, running workflows and web scraping. R works better if you create data visualisations and graphics and build statistical models.

Career path:

R is a better fit when it comes to statistical learning, with its data experimentation and exploration libraries par excellence. On the other hand, Python works better when you deal with large-scale apps, machine learning and data analysis carried out in web apps.
Consider how each programming language fits within your long-term professional goals. If you are interested in domains like statistical calculations or data visualisation, R is the better choice of the two. If you are passionate about data science and wish to work with technologies like deep learning, AI and big data, Python will be more interesting.
This is also true for your interests that go beyond just data. Python is a general-purpose programming language that has wider applications in computer science, development and programming than just data science. 

Also Read: How To Run Your Python Scripts

How to Learn Python or R - Earn a Professional Certification:

Both R and Python are great data programming languages. They are also easy to learn for beginners who don’t have prior coding experience. Additionally, regardless of which language you choose to learn, there is an abundance of learning resources, study material and job opportunities out there for you.

Earning a data analytics certification from a recognised vendor like IBM or Google provides a learning framework for programming languages in the data analysis context. If you want a career in data analytics or data science, these certifications are a good way to create a solid foundation. With a combination of video resources, interactive labs and simulation projects, preparing with a Koenig certification course will give you a holistic learning experience. You can sharpen your technical skills and knowledge and complete these courses in under six months. 

Python vs R - The Key Differences:

The easiest way to distinguish between R and Python is the way they approach data science. Both of them are open-source languages and have the support of large communities that are always expanding their functions and libraries. R is used chiefly to analyse statistical data, while Python offers a more generalised data-wrangling approach. 

Like Java and C++, Python is one of the leading multi-purpose programming languages today. Its readable and intuitive syntax makes learning the language much easier. Programmers and developers use Python for machine learning within production environments and data analysis. For instance, some developers could use Python for face recognition app building or developing Machine Learning applications. 

In contrast, R was created by statisticians to help with specialised analytics and statistical models. A data scientist uses R for deep analysis that needs only limited coding and aesthetically pleasing data visualisations. 

Other Differences Between R and Python:

Data Collection:

Python allows every data format type, like CSV (comma-separated value) files, JSON files etc. SQL tables can also be imported directly within Python code. When it comes to web development, Python has a requests library that allows you to fetch data by accessing the web to build datasets. On the other hand, R is made for data analysts and allows Excel, text and CSV files. SPSS and Minitab format files can also be converted to data frames in R. Python provides greater versatility to fetch web data, while Rvest and other modern R packaging are designed for fundamental web scraping.

Data Exploration:

You can easily integrate Python apps within engineering environments. On the other hand, R apps are perfect to visualise your data through graphs and plots. How you interact with data in your workplace will help you decide which of the two languages you should choose.
Python has a library for data analysis known as Pandas, that you can use for data exploration. You can sort, display and filter data within seconds using this library. R is better optimised for large dataset statistical analysis. It provides several options for data exploration. R helps in building probability distributions, applying various statistical tests and applying standard techniques for data mining and machine learning.

Data Modelling:

Python is made with standard data modelling libraries. This includes Numpy (numerical modelling analysis library), SciPy (calculations and scientific computing library), SciKit-Learn (Machine Learning algorithm library) and others. For specific R modelling analysis, you might have to depend on packages beyond the core functionality of R. However, the specific package sets call the Tidyverse simplify the process of data importing, manipulating, visualising and reporting.

Additional Read: Top 20 Python Interview Questions And Answers (2021 Updated)

Data Visualisation:

Data visualisation is not a key competency that Python offers. However, you can use its Matplotlib library to generate basic charts and graphs. Additionally, Python’s Seaborn library lets you create more informative and attractive statistical graphics for data visualisation. R, however, was designed to display statistical analyses results. Its base graphics module enables you to create basic plots and charts. You can also create advanced plots like scatter plots using ggplot2.

Python vs R - Which is the Right One?

There are several tools out there like Microsoft Machine Learning Server supporting both Python and R. Many organisations use both these languages for different applications. This would mean there is no point of a Python vs R conversation then. So to sum up, both these languages are promising for your career and generally used within the same business environment. So keep your best interests in mind and give your career the boost it deserves by signing up for a Python or R preparation course today.

Armin Vans
Sheyans is a Android Certified Developer and certified professional with rich experience in Application Development, Programming, and Corporate training. He delivered training on CEH, CPENT, ECSA, Security+, CNDV2, CISSP, CISM, Android, iOS, Java, Objective C, Swift, and Xamarin Mobile Framework.



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