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

# Get financial data from World Bank
The financial data we’re using here comes from the [World
Bank data](https://data.worldbank.org/indicator/NY.GDP.PCAP.PP.CD) and presents per capita gross
domestic product numbers from 2019.

import pandas as pd
import numpy as np
import matplotlib as plt
import matplotlib.pyplot as plt
gdp = pd.read_csv('WorldBank_PerCapita_GDP.csv')
print (gdp)

# Get health data from GHD
The health data we got from the GHD Results Tool on the [Global
Health Data](http://ghdx.healthdata.org/gbd-results-tool) Exchange. We used their drop-down configurations to select data by a country that focused on the prevalence of individual illnesses measured as a rate per **100,000**. These records also represent **2019**.
We'll select just the two columns that are relevant for us, (`Country` and `Val`, which is the average prevalence rate of the illness per 100,000 over the reporting period. Here’s what our data will look like:

Getting financial data and our assumptions about that 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

Fetching Data and Our Assumptions

Get financial data from World Bank