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

Learn about our findings and how to accept uncertainty as an answer..
# Some thoughts about data

Let's first think about our dataset. Perhaps it's a problem between the number of diagnosed cases and the true number of those living with it. Though in that case, wouldn't all conditions be more diagnosed in higher GDP nations, since many would assume those nations have more doctors?  

Perhaps there are more variables that we're not including in our charts that would provide a clear answer. Or perhaps there is some missing piece of information that requires more professional study by sociologists and medical professionals to find.

In truth, the real value of some analytics work is not to explain something, but to point out something that needs explaining; a gap in knowledge for scientists to study further and understand. We can theorize many things from this data, but we'll need more information before we can discuss causation at all.


Learn to see what are our assumptions about data of different disease.

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

Thinking about Our Data