Learn about Python-based data analytics, discover data manipulation and cleaning techniques, explore Jupyter for insights, and gain skills to plot and visualize data effectively.

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In this course, you will get an introduction to Python-based data analytics. You will learn the inner workings of Python to the point where you can confidently find and manipulate data sources and use a Jupyter environment to derive insights from your data.

By the end of this course, you will be able to find and clean data sources, plot your data, and use Python functionalities that show graphs of data.

It’s your all-in-one guide to teaching yourself data analytics in 30 days.

Data Analytics in Python

# Check the integrity of data
But even before going there, we should confirm that our
plots make sense in the context of their data sources.
Working with our BLS examples, let’s look at graphs to
compare CPI and wages data from both before and after our
manipulation. That way, we can ensure that our math (particularly our fake) didn’t skew things too severely.

Here’s what our CPI data looked like when plotted using the
raw data:

Learn how to check the integrity of data and see the plotted graphs.

Before We Begin

Comparing Wages With Consumer Price Index Data

Wages and CPI: Reality Check

Working With Major US Storm Data

Property Rights and Economic Development

How Representative Is Your Government?

Does Wealth Influence The Prevalence Of Mental Illness?

Do Birthdays Make Elite Athletes?

Does Literacy Impact The Income of People

Conclusion

Appendix

Confirm the Integrity of Your Data

Check the integrity of data