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 the data
You can get up-to-date GDP data by country from many
sources. We’ll want our data broken down to the per capita level
because that’s the best way to get valuable apples-to-apples
comparisons between countries of different sizes.


## Get data from the World Bank site



We got our GDP data from the World Bank's [site](https://data.worldbank.org/indicator/NY.GDP.PCAP.PP.CD) and our Index of Economic Freedom data from [here](https://www.fraserinstitute.org/economic-freedom/dataset?geozone=world&page=dataset&min-year=1995&max-year=2019&filter=0&sort-field=legalSystemsPropertyRights&sort-reversed=1&date-type=range). Here’s how all the basic setup works.



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

Running GDP shows us that the GDP data set has `248` rows
that include plenty of stuff we don’t want, like some null
values (`NaN`) and at least a few rows at the end with general
values that will get in the way of our focus on countries. We’ll
need to clean all that up, but our code will ignore those
available values because there will be no corresponding rows
in the `ec_index data frame.`

The `dtypes` shows us that the `Year` and `Value` columns are already
formatted as `float64`, perfect for us.



Learn how to get data from different sources and use Jupyter notebook to run python scripts to get data and how to visualize datasets using scatter plots.

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

Getting the Data and Visualizing Datasets Using Scatter Plots

Get the data

Get data from the World Bank site