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Should I learn R or Python for Data Science?

Data science is now an essential part of the core operations of many companies. Data science is seeing a renaissance thanks to increased data access, more efficient computing, and a focus on analytics-driven business decisions. The global demand for Big Data is expected to rise at a CAGR of 10.9% from $179.6 billion in 2019 to $301.5 billion in 2023, according to Analytics Insight. Concurrently, big data industry funding will increase by 13.5% to $4.5 billion by 2023, from $2.7 billion in 2019.

This means that the demand for data science professionals will also rise. More and more businesses are now data-hungry, requiring data scientists to aid analytical decision-making. In fact, the US Bureau of Labor Statistics estimates that the number of workers in the data science industry will rise by around 28% by 2026. To put that 28% into perspective, that equates to approximately 11.5 million new jobs in the sector.

Noticing the growing demand in the sector, many analytically-oriented professionals are trying to switch or start a new career in the field of data science. However, every beginning is hard, and so is choosing where to start from. 

According to O’Reilly’s survey, the two most popular data science tools are Python and R. It’s difficult to choose between those two incredibly versatile data analytics languages. R for mathematical analysis and Python for general-purpose programming were both created in the early 1990s and are both free and open source. They are mandatory for those interested in machine learning, interacting with massive databases, or making dynamic data visualizations.

 

Python vs. R

Python and R are both free and open-source programming languages that can be used on Windows, Mac OS X, and Linux. Both languages are capable of handling almost any data analysis challenge and are considered relatively simple to learn, particularly for beginners.

However, as a beginner, you should start somewhere. That’s why it’s smart to analyze both languages, which is what we’ll do in this article. It’s important to highlight that one language isn’t better than the other: it all comes down to what you need in your work. 

R’s usability was designed with statisticians in mind, giving it concrete advantages such as key features for data visualization. On the other hand, Python is more general-purpose. Let’s dive in deeper and see what this means. 

 

What is Python? 

Python is a general-purpose high-level programming language that is known for its conciseness and readability. It’s ideal for collecting vast volumes of data from the internet, developing machine learning algorithms, and incorporating data science into broader tech projects.

When we say high-level, we think of languages that have a syntax that humans can easily understand and learn. On the other hand, low-level languages are designed to be easily understood by a machine. The code you write in a high-level language gets translated into machine code, so the program can understand it and execute it. Python, along with Java and C, which are also high-level languages, is one of the most widely used programming languages among global software developers. 

Python is widely used in data science, web development, and a wide range of other software applications. Many people want to study Python for data science whether they are either familiar with the language or have used it in the past. And if you’re new to data science, Python is a user-friendly language that’s simple to pick up once you’re up and running.

 

Python has become a popular data science language as a result of the numerous libraries that have been developed. To get started with Python, you’ll need to download a few different kits:

 

  • Numpy is a Python library for dealing with large-dimensional arrays.
  • Pandas is a programming language that can be used to manipulate and analyze data.
  • Matplotlib and Seaborn is a library for creating data visualizations
  • SciPy, scikit-learn, and statsmodels for hypothesis testing and model fitting 

 

Python is also especially well suited for large-scale machine learning deployment. Its deep learning and machine learning libraries, such as scikit-learn, Keras, and TensorFlow, enable data scientists to create sophisticated data models that can be integrated directly into a production system. There’s also Jupyter Notebooks, which is an open-source web application that lets you share documentation with live Python code, calculations, visualizations, and data science explanations.

 

What is R? 

R is a versatile mathematical programming language designed for data processing and data analysis. It’s ideal for uncovering patterns and trends in your results, as well as constructing mathematical models and making stunning data visualizations.

 

R’s many possibilities can be divided into three categories:

  • Data manipulation
  • Statistical analysis
  • Data visualization

 

The majority of people learn R to deal with data rather than to create software applications. R’s data structures and variable forms are simple to use for data manipulation and interpretation since they were created with this in mind. Furthermore, R has a number of built-in data science features, so you won’t have to think about downloading libraries while you’re just getting started.

For simplified statistical analysis, visualization, and reporting, R is widely used within RStudio, an interactive software environment (IDE). Shiny allows R programs to be used directly and interactively on the internet. You’ll want to get acquainted with packages like tidyverse, dplyr, and ggplot2, once you get more comfortable with R. Packages, are pieces of code that help you do things like organizing your data, produce great graphics, teach machine learning models, and more. You won’t have to write data science features from scratch if you use kits like these. At the time of writing, there were over 13,000 R packages available on the Comprehensive R Archive Network (CRAN).

 

Differences between Python and R 

The approach to data science is where the two languages differ the most. Wide communities embrace both open source programming languages and are constantly expanding their libraries and resources. However, although R is mostly used for mathematical analysis, Python offers a broader approach to data manipulation.

Python has a readable syntax that is simple to understand, similar to C++ and Java. Programmers who want to perform data processing and deep learning in scalable development environments commonly use it. Python may be used to integrate facial recognition into a smartphone API or to create a machine learning framework, for example.

R is a statistical programming language that strongly relies on statistical models and advanced analytics. For deep statistical research, data scientists use R, which is backed by just a few lines of code and spectacular data visualizations. Some of its uses include user experience monitoring or genomics testing.

One of the most obvious differences also lies in data collection. Python can handle a wide range of data types, from comma-separated value (CSV) files to web-sourced JSON. SQL tables can also be imported directly into Python code. The Python requests library makes it simple to capture data from the web and use it to create datasets in web creation. R, on the other hand, was created for data analysts who need to import data from Excel, CSV, and text files. R data frames may be created from files created in Minitab or SPSS format. 

 

Which one to choose: Python or R? 

Should you learn Python or R? There’s no wrong answer to this question. Both are in-demand skills that will allow you to complete almost every data analytics challenge you come across. It will eventually come down to your experience, ambitions, and career aspirations to determine which one is best for you.

 

Here are some factors to think about when you make your decision:

 

  • Experience. Python has a linear and seamless learning curve thanks to its simple syntax. It’s thought to be a good language for newbies. R’s advanced functionality is complicated, making it more difficult to gain experience.
  • Company practices. R is a non-programming statistical instrument used by researchers, engineers, and scientists. Python is a production-ready programming language that is used in a variety of industries, science, and engineering workflows.
  • Project specifications. R has unrivaled libraries for data exploration and testing, making it best suited for statistical learning. Python is a better option for machine learning and large-scale applications, especially data analysis in web apps.
  • Data visualization. R programs are suitable for displaying data in visually appealing graphics. Python programs, on the other hand, are easier to incorporate into an engineering system.

 

Conclusion 

Stack Overflow and RedMonk, among other common programming language indexes, show that Python is by far a more popular language in the wider software world. While this does not actually imply that it is stronger, it does indicate that it is more commonly adopted and has a larger audience for continuing support and growth.

However, it’s important to note that most organizations use both languages. For example, you might use R for early data analysis and discovery and then turn to Python when it’s time to deploy data items.

Both languages will do the majority of the work. You should choose the one that best fits your desires as well as the tool that your coworkers are using. It is preferable if you all speak the same language. Learning the second programming language is easier after you’ve mastered the first.

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