R vs Python for Data Science?
How to choose which one to learn when starting in Data Science?
As we know the Data Science and AI field is growing very fast. One of the skills that you need in Data Science is a programming language like R or Python.
As Data Science trainers, we always have students confused about whether to learn R or Python when starting with a Data Science course. Hence in this article we explain about these two main languages for Data Science-how they originated , their similarities, specialties and which one should one chose.
What is R
R is a statistical programming language developed by statisticians and scientists . It is designed for Data and Statistical Analysis ,Data Science and Machine learning.
It has one of the richest ecosystems to perform data analysis with almost 12000 packages available in CRAN (R’s open-source repository). Hence it is very easy to find a suitable package for any analysis for any functionality that you may need . The rich variety of packages makes R the first choice for statistical analysis, particularly for specialised analytical work.It also has very powerful packages for communication/reporting and econometrics along with many packages to perform time series analysis, panel data and data mining.
R language is used by more than 2 million statisticians ,researchers and data scientists from diverse disciplines across the world for exploring large datasets, cleaning, analysing and visualising them, statistical modeling for Polls, Surveys, panel data mining and creating novel statistical models.
What is Python
Python is a multi-tasking programming language developed by software engineer Guido van Rossum. Python can be used for various uses like Web development, GUI development, Software development , Gaming, System administration as well as Data Science and Machine Learning.
Python is first and foremost a general-purpose programming language. It was not specifically designed with data science and analytics in mind This growth shows that while Python’s data science libraries may still have a way to go versus “R”, its data science ecosystem is growing quickly.
Python provides cutting-edge API for machine learning or Artificial Intelligence and has best-in-class tools for pure machine learning and deep learning, but it is not entirely mature yet for econometrics and communication and reporting capabilities.
Hence it can be used very well for Machine Learning integration and deployment but not for advanced statistical analytics. The Data Science focus for Python is more on creating automations and deployments of models rather than the Statistical and econometrics concepts behind them. Most of the data science work can be done with five Python libraries: Numpy, Pandas, Scipy, Scikit-learn and Seaborn.
How do you decide?
So if you are planning to learn and use high-performance data science tools, you have the options of learning either R or Python. You can learn both to benefit from their unique advantages. However, when starting out, it may be better to choose one for more effective retention and usage.
Though there are multitude of articles and information on the differences between R vs Python there seems to very little focus on the Suitability Fit of R/Python in terms of learner’s profile and needs.
Suitability Fit : It is important to consider two following points :
- What is your current profile- i.e., where are you coming from?
- Are you a business and management professional using/wanting to use data for Business Decisions?
- Are you a Computer Scientist engaged in software development and deployment?
- What type of Data Science role you wish to go for initially i.e., where do you want to go?
- Do you wish to use Data Science for Management/Business Decisions?
- Do you wish to use Data Science for creating automated programs and devices like self-driving cars, robotics, gaming etc.
As per our experience usually most people interested in learning data science for business are not computer scientists. So, if you looking for Data Science for business usage, R may be a good option to start with as it has the best overall qualities for business use.
R is useful for:
- It is very easy and flexible to learn but provides high business decision related capabilities.
- Business and Finance activities involve interaction and communication in the form of reports, dashboards to enable decision makers to make quick and well-informed decisions. R has strong communication best-in-class tools for visualization, reporting, and Dynamic and interactive graphics .
- R has a wide rich range of topic-specific packages covering whole gamut of business functionalities such as econometrics, finance, time series and can make business decisions more robust with data insights backed by statistics.
- R can be used very easily for in-depth research and analysis instead of statistical packages like SPSS, SAS, and STATA and can do advanced statistical maneuverings like descriptive analysis,regressions, ANOVA/MANOVA ,classifications, Time series ,hierarchical models with just a couple of line of codes.
- It makes model interpretation very easy through quick and detailed model summaries and plots which help in evaluating and improving the models in a statistical and scientific manner.
However, If you are Computer Scientist or Software Engineer/Developer used to creating and deploying programs and are interested in learning Data Science for building automations (like building self-driving cars) then it is better option for you to learn Python.
Python is very useful for:
- Creating data Science and Machine Learning models for deployment and reproducibility.
- It can create models quickly and integrate systems effectively
- It is a high-level programming languages and scales very fast
- It is Easy to create quick pipelines for data cleaning, transformation, modeling and deployment through a set of repeatable, and ideally scalable, steps. for repetitive tasks.
- It can give you quick entry into Machine learning and ability to create & deploy algorithms.
- Python’s ecosystem for Data Science and Machine Learning is growing very fast .
Some of the key differences in R and Python can be summarized as follows:
- R is mainly used for statistical analysis while Python provides a more general approach to data science
- The primary objective of R is Data analysis and Statistics whereas the primary objective of Python is Deployment and Production
- R users mainly consists of Scholars and R&D professionals while Python users are mostly Programmers and Developers
- R provides flexibility to use available libraries whereas Python provides flexibility to construct new models from scratch
- R is more suitable for your work if you need to write a report and create a dashboard.
- Since R is written by statisticians, it is by far more suitable for statistical correctness. Hence it is more suitable for building and prototyping a statistically robust model which can then be handed off to be written in Python for deployment.
The good news is that the statistical gap between R and Python is getting closer. Most of the Data Science work can be done by both languages.
Hence while starting out, it is better for you to choose the language that suits your profile and initial needs better. Once you know one programming language, learning the second one is anyways simpler So the decision which should be your first Data Science language is not really overwhelming and can be can be summarised to the basic questions of what is your current profile and your initial goal of learning Data Science .