Data Analysis From Scratch With Python: Step By Step Guide

Is Mathematical Expertise Necessary?
Data analysis often means working with numbers and extracting valuable
insights from them. But do you really have to be expert on numbers and
mathematics?
Successful data analysis using Python often requires having decent skills and
knowledge in math, programming, and the domain you’re working on. This
means you don’t have to be an expert in any of them (unless you’re planning to
present a paper at international scientific conferences).
Don’t let many “experts” fool you because many of them are fakes or just plain
inexperienced. What you need to know is what’s the next thing to do so you can
successfully finish your projects. You won’t be an expert in anything after you
read all the chapters here. But this is enough to give you a better understanding
about Python and data analysis.
Back to mathematical expertise. It’s very likely you’re already familiar with
mean, standard deviation, and other common terms in statistics. While going
deeper into data analysis you might encounter calculus and linear algebra. If you
have the time and interest to study them, you can always do anytime or later.
This may or may not give you an edge on the particular data analysis project
you’re working on.
Again, it’s about solving problems. The focus should be on how to take a
challenge and successfully overcome it. This applies to all fields especially in
business and science. Don’t let the hype or myths to distract you. Focus on the
core concepts and you’ll do fine.

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4. Python Quick Review
Here’s a quick Python review you can use as reference. If you’re stuck or need
help with something, you can always use Google or Stack Overflow.
To have Python (and other data analysis tools and packages) in your computer,
download and install Anaconda.
Python Data Types are strings (“You are awesome.”), integers (-3, 0, 1), and
floats (3.0, 12.5, 7.77).
You can do