Is Python or R better for big data?

If you’re passionate about the statistical calculation and data visualization portions of data analysis, R could be a good fit for you. If, on the other hand, you’re interested in becoming a data scientist and working with big data, artificial intelligence, and deep learning algorithms, Python would be the better fit.

Why is Python good for data analysis?

Python focuses on both simplicity and readability, while also providing a plethora of useful options for data analysts/scientists. As a result, even novices can easily use its relatively simple syntax to create effective solutions for complex scenarios, with just a few lines of code.

What are the similarities and differences between R and Python?

R vs Python Comparison Table

R	Python
R is a command line interpreted language.	Python strives for simple syntax. It has a similarity to the English language.
R is developed for data analysis; hence it has more powerful statistical packages.	Python’s statistical packages are less powerful.

What is the disadvantage of using R in enterprise level large scale solutions?

R lacks basic security. It is an essential part of most programming languages such as Python. Because of this, there are many restrictions with R as it cannot be embedded in a web-application.

What are the limitations of R?

Disadvantages of R Programming

• Weak Origin. R shares its origin with a much older programming language “S”.
• Data Handling. In R, the physical memory stores the objects.
• Basic Security. R lacks basic security.
• Complicated Language. R is not an easy language to learn.
• Lesser Speed.
• Spread Across various Packages.

How is Python used in Big Data?

If the data volume is increased, Python easily increases the speed of processing the data, which is tough to do in languages like Java or R. This makes Python and Big Data fit with each other with a grander scale of flexibility. These were some of the most significant benefits of using Python for Big Data.