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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Jupyter.tar.gz

jupitor

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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

# Import libraries
To better understand the data and visualize anomalies, we’ll import another couple of libraries that are part of the Plotly family of tools. You may need to manually install on your host using `pip install plotly` before these will work. Here are the libraries we’ll need to import:
```
import plotly.graph_objs as go
import plotly.express as px
```

Learn to understand the data better and visualize anomalies in the data by adding hover visibility.


Adding Hover Visibility

Learn to understand the data better and visualize anomalies in the data by adding hover visibility.

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

Adding Hover Visibility

Import libraries