Ten Valid Points for Learning Python for Data Science

Ten Valid Points for Learning Python for Data Science

Data science is a vast field with tons of entry points, depending on where and how you want to start

Python is easy to learn

Python is easy to learn

Coding can be intimidating, especially for a beginner. But Python is the exception.

 It’s easy to read

 It’s easy to read

Python has a clean and simple syntax that mirrors English, so whatever you build will be understood by you and many people

It’s popular

It’s popular

It’s one of the most widely used languages in data science (and elsewhere)If you want to get into data science, you won’t get far without knowing at least some Python

Huge Community of Pythonistas

Huge Community of Pythonistas

learning Python for data science is that you’ll get access to an incredible community of Pythonistas and become one yourself.

Comprehensive set of data science libraries

Comprehensive set of data science libraries

Libraries like Pandas, statsmodels, NumPy, SciPy, and Scikit-Learn are very popular in the data science communities.

Teaches the basics

You can easily learn data science basics with Python just by running through some basic tutorials

Data cleanup is simple.

Data cleanup is simple.

Two of the libraries I mentioned early, NumPy and Pandas, are really great at cleaning data.

Communication

Communication

Data science is not just lines of code — it means communicating the results with key stakeholders.

Quick prototypes

Quick prototypes

Most data scientists utilise prototypes to go around this problem and stress-test their ideas before fully developing them.

Job security

Job security

Python is a great language to learn for data science, but you may use the same skills to get work in other areas of computer science as well.