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 underutilization of data. These tools are available in different professional setups to run different types of analyses.
Data Analysis With R And Python
We will compare data analysis with R and Python. Two tools are used for data analysis across industries. 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.
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 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.