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

# Challenge
Welcome to your third challenge. This problem has been designed for you to practice freely, so try to solve it on your own first. Take some time, and think about the different concepts we’ve explored in the course so far.

If you feel stuck, you can always check out the solution review provided in the next lesson.

Good luck!

# Jupyter Notebook

The Jupyter Notebook below contains your third challenge. Click it to launch the application.


Use what you've learned so far to perform the following exercise, which will help familiarize you with the concepts that we'll need later on.

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

Challenge 3 — Scatter Plot

Challenge