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

# Misconception about data
When interpreting our plots, we should always seek to validate what we see in the context of the real world. If, for
instance, the data is too good to be true, then it probably isn’t
true.
## Example
For example, residual plots, the points that don’t fall out right
next to the regression line, should generally contain a visually
random element. They should, in other words, present no
visible pattern. That would suggest a bias in the data.



Learn to see the considerations other than line regression and hovering data.

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

Other Considerations

Misconception about data