Python vs. R: Which language to Choose for Data Science?
Are you looking for the best language to kick-off your career in data science and wondering about the strengths and weaknesses of both languages?
What is Python?
Python is an open-source programming language deployed for statistical and machine learning models with the capability to support object-oriented, procedural and functional programming. Being a general-purpose language with a syntax that is intuitive and easily interpretable, Python is used widely in data science, web development and software applications. It is a product-ready language that can easily integrate with other work flows and provides a comprehensive collection of libraries that help you when implementing your code.
It gained immense popularity due to its speed, code readability and diverse functionalities. Programmers appreciate Python for its ease of deployment and reproducibility. Its focus on simplicity and readability allows first time users to have a linear and smooth learning curve. Therefore beginners can write their first programs quickly.
Disadvantages and shortcomings include the requirement of rigorous testing as errors show up in runtime, visualizations being convoluted and results being not as eye-pleasing or informative as provided by R.
What is R?
As Python, R is an open-source programming language used by statisticians and engineers to build algorithms and techniques for statistical modelling and data analysis. Its functionalities were developed for statisticians, giving it field-specific advantages in data visualization. It is designed for exploration of patterns and trends within data, building statistical models and creating data visualizations. Programmers use R for the purpose of working with data, instead of for building software applications.
R includes a significant number of inbuilt libraries that offer a wide variety of statistical and graphical techniques including regression analysis, statistical tests, clustering and time-series analysis. It has the capability of creating powerful charts and dashboard quality graphs to demonstrate and monitor metrics. R provides cutting-edge interface packages that communicate between open-source languages allowing users to string workflows together.
It is considered the best tool to create aesthetic graphs and visualizations and is built around a command line. However, many R users work inside of RStudio, an environment that provides a data editor, debugging support and a window that holds graphics. R was developed with little intention of simplicity and an easy-to-understand syntax. Also, users find it more time consuming to identify appropriate packages and many R libraries show dependencies to each other. R is considered to run slowly when code is written poorly and beginners’ learning curves for R is relatively steep and available documentation is not user-friendly.
Demand for both languages is increasing and offered salaries and opportunities are attractive. Tech companies and startups rely on these technologies to expand faster and achieve data driven growth. Any business that is looking to enhance their data analysis techniques understands the value of both Python and R. Therefore, both languages are in great demand and can be used to solve a wide range of unique problems.
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