Is it really possible to learn data analytics in 30 days?

Yup. If you’re willing to put in the effort, I’m pretty sure you can master this stuff in 30 days or less. Or your money cheerfully refunded.

Ok. Just kidding about the refund bit…it is free, right?

But there are some conditions. The first of those is that you have to be willing to make mistakes. Big, embarrassing mistakes. Sure, you might be able to just cut and paste my code into your own Jupyter Notebook environment and, a few clicks later, produce the same cool plots. But you won’t learn all that much in the process.

The real learning will only come when you build your own Python/Jupyter environment and then try to figure out why simple commands aren’t running the way they should. (Hint: it’s probably a dumb syntax mistake, the result of you misunderstanding your code, or related to the specific version of Python you’re running).

And where will you encounter the greatest, turbo-charged growth? When you force yourself to carefully think through the data cleaning and code choices I made, wonder why they’re failing for you, and change things so it will work in your environment.

All that will take time and effort. But you did say you wanted to learn data analytics, didn’t you?

I have made my code available as Jupyter Notebooks on GitHub. Feel free to save yourself some typing, but just remember that instant, error-free success isn’t guaranteed.

Having trouble with data sources or Python syntax? Looking to make the world a better place by sharing what you’ve already learned? Subscribe to the “Teach Yourself” community on Reddit.

Want to give back to the project? Writing an honest Amazon review for the book version can help other people find and enjoy this content.

Finally, here’s a list of the key skills found in this curriculum and where you can find them.

Learning Objectives:

Objective Chapter
APIs 1-Comparing Wages
5-Representative Government
Cleaning data sources 1-Comparing Wages
3-US Storm Data
Data types 2-Wages and CPI
3-US Storm Data
Functions 1-Comparing Wages
JSON manipulation 7-Birthdays and Elite Athletes
Pandas methods 3-US Storm Data
Real-world integration 2-Wages and CPI
Regression lines 4-Property Rights
Scatter plots 4-Property Rights
Scatter plot labels 6-Wealth and Mental Illness
Source data gotchas 2-Wages and CPI
Understanding histograms 7-Birthdays and Elite Athletes
Understanding the domain problem 2-Wages and CPI
3-US Storm Data
6-Wealth and Mental Illness
Visually optimizing plots 1-Comparing Wages

Good luck!

David Clinton

The copyright for all the content on this site belongs to David Clinton, Bootstrap IT, and DBC Technology Services Inc.