All we are quite familiar with the buzz words, ‘Big Data and ‘Data Analytics’ by now. Data is being regarded as the new oil of the 21st century. As per a report, 72% of organizations admit that they collect data but never use it because of its complicated nature despite a high ‘Return on Investment (ROI). A 10% increase in data accessibility can result in more than $65 million extra net income for a typical Fortune 1000 company.

90% of the world’s data is available in the last two years alone. As the use of data increases, several tools have come out.  They help one unlock its potential. Increasing the use of such tools is helping companies and individuals overcome the issue of the underutilization of data. These tools are available in different professional setups to run different types of analyses.

Data Science Languages

To understand data and ‘learn’ from it, one needs to know the language for data science through which it can communicate with this data and make sense of it. By language what we’re referring to essentially are the programming languages. So, the usage is in the fields of data science and analytics.
 
Here, we’re looking at two open-source programming languages; Python and R. These are free to download, offer immense accessibility. In addition, a great tool for anyone taking their first step into the world of data analytics.
 
when you’re looking at two languages, the first question that comes to your mind is relevant to how these two languages differ from each other (or might be similar). Also, which one is ‘better than the other.
 
Well, the answer to the latter isn’t that quite straightforward. Mainly so because either language is suitable for almost any data science task. Whether it’s data manipulation or automation to ad-hoc analysis and the exploration of datasets.
 
A case in point of both languages being used is when users might use R for an early-stage data analysis and exploration. Then, switch to Python to ship some data products. Hence, showing that both languages could actually work in cohesion.

Data Analysis With R And Python

We will compare data analysis with R and Python. Both tools are for data analysis across industries of computer science. R and Python are both open-source programming languages. New libraries and tools are being available to them. Most of the tasks that we perform through R are also available using Python.

Strength Assessment For R And Python

Before delving into the differences that exist between the two languages. We need to discover the strengths for each programming language to better understand which particular areas each of the two has a better command over.
 
Python is a general purpose programming language that makes it useful beyond mere data analysis. In fact, with Python code, you have the opportunity to dive into different areas of programming. Also, easy to learn about the intricacies that run the system.
 
This is why it has gained immense popularity for the code-makeup which is heralded for its readability, speed, and ease-of-function/functionality that it offers the users.
 
At the same time, Python is one of the greatest for mathematical computations and to learn how algorithms work. Hence, placing it above other languages is a great tool to understand the systems in place.
 
Lastly, due to its ease of access, Python is considered to be very easy when it comes to deployment. Also, the reproducibility of the end product.
 
R, on the other hand, is probably the best tool when it comes to the creation of graphs and visualizations. In other words, it’s considered to be a game-changer when it comes to visualizations.
 
Obviously, as a programming language, it offers data analysis. But, what makes it stand out from the other languages is the multi-functionality at play. This also poses it as a great tool for statistical analysis.
 
One unique aspect about R, is the “RStudio”, a virtual environment where most users of R work. This environment includes a data editor, debugging support, and a window to hold graphics. This is probably one thing that helps it stand out from other forms of languages since it offers a custom (and exclusive) eco-system that users can benefit from.

Difference Between R And Python

Python is the most popular programming language today. Its developers are always in high demand. As it continues to increase in popularity. Additionally, It is becoming the closest thing to a must-know language for every programmer. R is a language built explicitly by statisticians and is better for analytical tasks. Furthermore, the core difference between R and Python or other programming languages is the array of outputs and visuals available for data analysis. There are many tools in R to communicate results that other languages do not have. However, the R language is mainly available for statistical analysis. Whereas, Python provides a more general approach to data science.

Data Analysis With R

R is one of the easiest languages for beginners. While it develops a narrow perspective on the world of programming. It helps beginners to remain focus and not get lost in the world of programming. The phenomenon of this approach is understandable by Hick’s law.  It states, ‘ the time it takes to make a decision increases with the number and complexity of choices.’ To avoid this trap, it is highly advised to simplify choices for the user by breaking down complex tasks into smaller steps, prevent overwhelming users by highlighting recommended options and use progressive onboarding to cut cognitive load for new users. These strategies can help keep the attention and interest of the users, who in this case, are the learners of these tools.

Data Analysis With Python

Python is versatile. It has variations in libraries that keep on upgrading continuously. The data visualization libraries are quite popular among programmers who like to dig deep into the world of data analysis. While R also allows analytics to a great extent, it doesn’t allow for more in-depth analysis and helps run primary analyses as needed.
 
A word for all programming enthusiasts. If you already know R, transitioning to Python would be only a matter of different syntax!

R or Python: Which Is Better?

At the end of the day, you can’t really say that one of these is better than the other. Simply because they both have the capability to be used in several situations. therefore, it’s better to understand their individual use cases to learn which tool is better used under what circumstances.
 
Python is generally used whenever data analysis needs integration with web applications or if the statistics code needs incorporation within a production database. In other words, Python is a tool that you should be using when integrating data or studying a wide array of data in a rather cumulative manner.
 
Moreover, since it’s a full-fledged programming language. It offers itself as a great tool for the implementation of algorithms in the production sphere.
 
R is a great tool for beginners looking to explore programming and learn more about the art of it all. Here, statistical models can be written with only a few lines of code. This makes it a great gateway into the world of programming for anyone and everyone.
 
When it comes to data analysis, R is more usable. So, when the task requires standalone computing or analysis on individual servers i.e. while Python is a more integrated language, R suits individual programming and studying.

Use Cases For R And Python

However, we’re going to dive in a little deeper to further our understanding of both these programming languages with a few particular cases.

Exploration Of Unstructured Data

Most of the data that exists in the world (almost 80%) are unstructured data i.e. all data such as text, video, and images are indeed unstructured forms. This is where a language like Python takes the win. Because it offers a multitude of packages for natural language processing, image processing, and voice analysis. This enables the user to understand and convert the unstructured data into structured data.

Data Cleaning

Since Python deals with large integrated datasets, it’s unbeatable when it comes to data cleaning. With the usage of its packages, you can easily clean up large sets of data and incorporate them into your analysis.

Exploration And Modeling In R

Once the visualization of data has begun, and the structured or semi-structured data is available. It becomes much easier to carry out data exploration in R.
 
Carrying on tasks such as regression analysis, factor analysis or utilizing tools such as logistic regression and time series analysis are quite simple to implement in R since it offers easy visualizations. More so, with each algorithm having its own package in R. It becomes that much easier to train the dataset and cross-validate it for future reference purposes.

Exploration And Modeling In Python

Python isn’t too far behind either when it comes to data exploration and modeling. Since it too offers packages that help with the data exploration process.
 
With both machine learning and deep learning inculcated into the workflow of Python. It becomes easier to work with large datasets that are taken from the Cloud. This means that the infrastructure of such projects will drive the user to AWS, or the Google Cloud – which means that Python will usually be the default language to use in any such large-scale data science projects.

They’re Both Great

Solving complex problems requires complex solutions and integration. In other words, both R and Python are integral to solving these complex problems. Therefore, it would be unfair to say that one is objectively ‘better than the other.
 
What’s important is for the user to understand the nature of both languages. Then decide which programming language works best for their particular use case.
 
At the end of the day, there obviously will be a competing nature between the two but that’s the most beautiful part of it all. It is this nature that just might enable data scientists task to produce simplistic solutions and efficient codes for individual purposes.