Rocket science resembles something very tough to understand. Python and R are not rocket science. If you are a beginner in data science with no prior knowledge in programming it will need the same effort to learn R and Python. If you are from a statistical background with no programming knowledge, R is a good choice for you. In terms of data science, both the languages are extremely effective and to become a skilled data professional you must know the use of both R and Python.
They are the two widely-used data analytics tools to execute the day-to-day activities of data scientists. However, there is a doubt or confusion about which language is better than other. It is a hot topic today in the world of data science as both the tools have their pros and cons.
How can a beginner understand which one is good for him or her? It is a challenging task to understand the fact. So, it is better to go through the features of both the languages

Features of R

1.  R is an open-source language thus, highly available. You can install   and   use it as per your choice and needs. It is easy to upgrade.  
2. R can handle large and multifaceted data sets
3. It supports many new statistical developments as it is very flexible and resourceful. You can perform the most precise statistical tasks on Psychometrics, Genetics, and even on Finance with R
4. It is an excellent data visualization tool for data scientists.
5. As there are already-written packages over there, a wide community supports R. It helps you hugely in your tricky analytical jobs.
6. Usually, it is used for academic and research data analysis purposes. 
7. It is easy to learn R. Once you learn the basics of R, you can easily grasp the higher concept of this language.  
8. R packages are exclusively designed to make data analytical and statistical jobs easy. However, some find R processing as a bit slower.

 

Features of Python

1. Python is a purposeful programming language and persons with familiar programming knowledge can easily code with Python. It is coupled with comprehensible features like code readability, easy structure, and trouble-free  execution. Python gives users a scope to less code.
2. With its strong correcting and less-coding features, Python is an excellent option for programmers to step into   the data science field.
3. Python is open-source software hence, cost-effective.  
4. Because of its high-performing ability, it is a good alternative to solve critical business scenarios.
5. Python is the perfect tool for Machine Learning, Deep Learning, and other Data Science courses. 
6. It has a scripting feature. So, you can use it in business applications as well for scientific computing intention. Furthermore, industries widely use this language for data-centric application development.
7. Python offers many functional data analysis packages. 
8. Python is effective for visualization purposes.

 

In case of popularity, Python is more popular than R. It is because R is used by data scientists only where python offers a range of jobs from developer to data scientists with high salary profile. From a data analytics perspective, R is popular but more and more people today are learning Python for its versatile usability and easy syntax. Tech-giant companies such as NASA, Google, Youtube choose Python for its flexible and dynamic features.

To become a skilled data professional or to become an expert Machine Learning professional, Hands-On is the ultimate destination. This data science organization provides several courses and a great learning atmosphere for interested individuals.