So why Python for analytics?
There are many compelling reasons to add Python to your toolkit.
Here are a few.
First, scalability.
Unlike most analytics tools or self-service business intelligence platforms.
Python is open source, free to use and built for scale.
What do I mean by built for scale?
Well, first, because Python is free, it's easy to rapidly scale across your team or organization.
We don't need to negotiate for budgeting licenses or contracts.
All we need to do is install it on our local machine and get analyzing.
Second, there are no hard limits on the number of rows or columns that can be processed by Python.
This makes it a great alternative to other tools that might run into their own limits.
Second versatility.
With powerful libraries and frameworks, Python can add value at every stage of the analytics workflow,
from data, prep and analysis to machine learning and visualization.
We'll take a look at some of the most important libraries in Python shortly.
The Python community is amazing.
It's one of the largest and most active programming communities in the world, which means you'll have
access to forums where you can share resources, get help, offer support and connect to other users.
I've gotten a lot of great ideas by engaging with the Python community and I'm excited for you to join
it as well.
Automation.
This is perhaps one of the most underrated aspects of Python as a tool in the Analyst Toolkit.
Python can act as a glue between disparate tools and systems, which allows us to automate complex tasks
and workflows out of the box without the need for complicated integrations or custom plug ins.
One example of this would be school to excel.
A traditional workflow might look something like Go to our Ask Your Workbench, write a bunch of ask
you all queries.
Get the table we need.
Export that to a flat file imported into Excel and hope that Excel interprets all of the columns correctly.
You might spend a bit of time formatting our data correctly before we begin analyzing our data.
With Python, we can connect directly to a database, pass through SQL queries, retrieve the data we
need, write that out immediately to excel while making sure that the formatting is correct along the
way.
Automating your workflows like this will supercharger efficiency as an analyst and is one of the reasons
why there's so much demand for Python.
Python skills are valuable and highly sought after and are becoming increasingly popular in the world
of analytics and business intelligence.
One of the reasons so many people are learning Python, after all, is because there are so many opportunities
in this field.
Not only will Python make you a more well-rounded analyst, but it will also open up doors to new roles
and new opportunities.
This isn't to say that Python is the end all be all of analytics tools when it comes to data analytics.
Each tool has unique strengths and weaknesses.
Python shouldn't be the only tool in your stack, but it can add tremendous value when combined with
other tools like Excel, SQL, Power, BI and Tableau.