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.

teach yourself data analytics in 30 days.png

Jupyter.tar.gz

jupitor

code

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

## Importing income group data 
For the income of different countries, we'll use the worldbank's #key# GNI: Gross National Income #key# per capita data in USD ([CSV file](https://api.worldbank.org/v2/en/indicator/NY.GNP.PCAP.CD?downloadformat=csv)). The data provides the income of different nations between the years 1960 and 2020. For our analysis we will only use the data from 2018 to 2020. We'll ignore the first four rows as they just contain information about the data and not the data itself. A snapshot of the income data CSV file is shown below.



Learn to import, classify, and plot income data.

Before We Begin

Comparing Wages With Consumer Price Index Data

Wages and CPI: Reality Check

Working With Major US Storm Data

Time Series Analysis and Visualization Using Python and Plotly

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

Classifying Incomes of The World

Overview

Importing income group data