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

These graphs have some interesting overlaps and we could practice our historical contextualization skills to hypothesize the events that caused them. But, for the purposes of this course, let's focus on one specific insight: how do the Compound Annual Growth Rates (CAGR) compare between our three data sets?

Learn how to calculate the rate of increase for CPI and S&P and how the Compound Annual Growth Rates (CAGR) compare between our three data sets.

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

Calculate the Rate of Increase for Both CPI and S&P