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 has been created in the last two years alone. As the use of data increases, several tools have come out, which helps one unlock its potential. Increasing the use of such tools is helping companies and individuals overcome the issue of underutilization of data. These tools are available in different professional setups to run different types of analyses. In this article, we will compare data analysis with R and Python, two tools used for data analysis across industries. R and Python are both open-source programming languages, so new libraries and tools are being added to them. Most of the tasks that we perform through R can also be done using Python.
Python is the most popular programming language today, and its developers are always in high demand. As it continues to increase in popularity, it’s become 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. Another difference between R and 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 used for statistical analysis, while Python provides a more general approach to data science.
R is one of the easiest languages for beginners. While it develops a narrow perspective on the world of programming, this helps beginners remain focused and not get lost in the world of programming. This phenomenon of focused approach is well explained by Hick’s law, which 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.