The two most popular languages used in data science and artificial intelligence are Python
language and R language. They each have some pros and cons, but each is important in their
own way in a rapidly growing industry around the globe. The question is when to use python
and R in projects? It depends on the nature of project because both the languages have strong
tools with some distinct advantages notwithstanding the opinions of some who choose one over
the other. in this blog, we cover the advantages and disadvantages of both the programming
languages.
Python is a very high-level all-purpose programing language first created by Guido van
Rossum, in 1991. Python is famous for professional and seasonal programmers due to
straightforward syntax and more readability. Python’s adaptability and the wide range of
modules tools and frameworks that support everything from web development to machine
learning and data analysis have made it popular across many sectors. Its unique advantages
make it one of the most popular programing language across the industry.
It is also popular language to us for mainly statistical computing and data processing and
created by statisticians Ross Ihaka and Robert Gentleman in 1993.it is popular in academic
researchers, statisticians, and data miners, R has long been the preferred language. Statistical
modelling, data visualization, and exploratory data analysis are good. Furthermore, it is open
source as python and has very efficient ecosystem that makes it very useful for statistical
computing and the data science industry.
It is simple to use and learn.
R and Python learning curves are different. Probably the most important consideration is the
ease of learning and using a programming language.
It is simple for someone with no programming experience to understand because of the
grammar of Python, which is quite like plain English Python is a good choice for novices
because of its readability and simplicity, as is often extolled. It is easy to use, so new users can
quickly learn Python and work with data. Besides, after i, a data scientist can use Python for a
lot of other jobs such as web development and automation.
It is not easy to use R language as like python, it is difficult for people who are unknown to
statistical analysis, statistical tools and techniques makes it less favorable it for programmers .
R is popular in academia and in research. However, because R is so focused on statistics, it’s a
great tool for those who already comprehend data science principles
More tools and libraries and more friendly and easier for beginners to learn python is a better
choice for beginners if they are starting with data science. R is more specialized and, while it’s
harder to learn at the beginning, it’s extremely useful for statisticians and researchers.
R and Python both come with a lot of datasciene related tools and packages, but they also have
their pros and cons.
Pandas: It is a powerful data analysis and manipulation library which gives structures like
DataFrames.
NumPy: Supports a wide variety of mathematical operations, and large multi dimensional
arrays and matrices.
Matplotlib and Seaborn : A collection of data visualization libraries for creating static, animated
and interactive graphs.
Scikit-learn: It works as a strong library for machine learning.
TensorFlow and PyTorch: And the neural network type deep learning frameworks are becoming
very popular for the use cases involving the use of artificial intelligence.
The following are some of the widely used libraries and packages in R:
ggplot2: This is a very strong and versatile tool for making static visualizations.
dplyr and tidyr: These are just data frame manipulation and transformation libraries.
Shiny: A web application framework for R to build interactive dashboards.
Caret: This is an all-in-one machine learning library that does training of your model and also
hyperparameter tuning with a uniform interface to do cross validation.
Python offers more developed, and more diverse library support from broad data science and
machine learning perspectives. If statistical analysis and data visualization make up your main
work process then R libraries are more powerful and specific.
This thesis is about MACHINE LEARNING and ARTIFICIAL INTELLIGENCE.
It is very crucial to find out which one is better and which one would be preferred choice on
other . Both R and python has tools and libraries to solve data science problems.
Python is the most used language in both AI and machine learning. Different machine learning
models are easy to implement and are effective, thanks to the wide variety of libraries—like
Scikit learn, TensorFlow, and Pytorch. Its adaptability makes it a very great language for real
time data analysis and predictive modeling for use with online applications. The development of
deep learning has come, and with the development of deep learning, neural networks have
become an industry standard in creating with frameworks in Python.
While there is also Caret and random Forest in R for machine learning, they don’t have the
power of Python’s full fledged machine learning ecosystem (that is getting better and better).
We seem python is winner over R when it comes to data science and artificial intelligence
because python has more readability and wide range of libraries used by developers around the
world but when we are looking for some specific filed like statistical analysis and academia then
R will be the winner over python
Both R and Python have the capability to visualize and use different resources for that.
There are two broad libraries in Python which assist in creating visualizations namely: Matplotlib
and Seaborn. Matplotlib comes with lots of functions related to charting, but the syntax is a little
wordy. It simplifies some of the tasks based on Matplotlib, and helps to create beautiful and
instructive graphs.
For data visualization, R’s ggplot2 is probably the most powerful tool. The grammar of graphics
on which it is built allows one to write even quite complex charts using simple and
straightforward commands. The strong inclination of R to data analysis and visualization makes
R basically meant for exploratory analysis and reporting.
From this, Python has a wider development and a larger community. There is a lot less R
community it is very niche and very passionate, but all focused on data science and statistics.
Data visualization is a great capability in both R and Python, but how they get there is different
with the different resources used.
There are two big libraries to do visualizations in Python: matplotlib and seaborn. Matplotlib has
lots of functions for charting, but the syntax can be a little bit wordy. Seaborn simplifies many of
Matplotlib tasks while still allowing the creation of well designed and informative graphs.
ggplot2 is the real powerhouse for data visualization and most people will agree. R stands out
from the rest for exploratory data analysis and reporting of possible sources on the basis of a
grammar of graphics, which allows the end user to create very intricate charts using clear and
easy commands.
It does get rapidly bigger, catching up to the great community! But there is a very small but very
niche R community that has a lot of dedication to data science and statistics.
Once again, Python has an advantage for data science models, and how to incorporate them
into larger systems.
Python has wide adaptability and is easily compatible with other systems and languages. It is
being used for software development, automation and web development and hence, it is a great
option to use machine learning models in production. Python makes models simple to deploy
due to its ease of use with web frameworks like Flask or Django.
Python has a much greater degree of integration capabilities compared to R and is mainly used
for data analysis and visualisation. While it is possible to build interactive web apps using R (for
example, using Shiny), R is often less flexible in the sense of interfacing with production
systems.
Python is more appropriate when it comes to deployment and to integrate into production
system. R is better at analyzing and reporting the data though not good in changing and
accepting data from others.
In summary, the choice between Python and R largely depends on your specific needs:
Python is perfect for those who want to learn deep learning, machine learning or are building
scalable data science solutions for production environments.
If you are looking at statistics, data exploration, and data visualization, R is the way to go.
Python and R should not be considered competitors, many data scientists find that they use
both Python and R, because depending on the project it could need either one or both
language.