Python for Data Science
Why is data science using Python?
Python vs. R: What’s the difference?
Which is better for a data scientist: Python or R?
- An extensive standard library: Python covers areas like string processing, internet protocols, software engineering, and operating system interfaces
- High stability: Python has new, stable releases that are issued roughly every 18 months
- Ease of use: Python is easy to use with its simple syntax and readability, which makes the code easy to understand and maintain
- High-quality plots: publication-quality plots (including mathematical symbols and formulae) can be produced easily
- Vast package ecosystem: R is readily extensible, and packages exist for most statistical techniques
- What is the problem you’re looking to solve?
- Is it statistic-heavy or could it be tackled in a different manner?
- What kind of learning curve are you prepared to take on? (Keep in mind that R has a steeper learning curve than Python)