Data Analysis From Scratch With Python: Step By Step Guide

Widespread Use of Python in Data Analysis
There are now many packages and tools that make the use of Python in data
analysis and machine learning much easier. TensorFlow (from Google), Theano,
scikit-learn, numpy, and pandas are just some of the things that make data
science faster and easier.
Also, university graduates can quickly get into data science because many
universities now teach introductory computer science using Python as the main
programming language. The shift from computer programming and software
development can occur quickly because many people already have the right
foundations to start learning and applying programming to real world data
challenges.
Another reason for Python’s widespread use is there are countless resources that
will tell you how to do almost anything. If you have any question, it’s very likely
that someone else has already asked that and another that solved it for you
(Google and Stack Overflow are your friends). This makes Python even more
popular because of the availability of resources online.
Clarity
Due to the ease of learning and using Python (partly due to the clarity of its
syntax), professionals are able to focus on the more important aspects of their
projects and problems. For example, they could just use numpy, scikit-learn, and
TensorFlow to quickly gain insights instead of building everything from scratch.
This provides another level of clarity because professionals can focus more on
the nature of the problem and its implications. They could also come up with
more efficient ways of dealing with the problem instead of getting buried with
the ton of info a certain programming language presents.
The focus should always be on the problem and the opportunities it might
introduce. It only takes one breakthrough to change our entire way of thinking
about a certain challenge and Python might be able to help accomplish that
because of its clarity and ease.

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