Time series data is everywhere; from temperature records, to unemployment rates, to the S&P 500 Index. Another rich source of time series data is Google Trends, where you can freely download the search interest of terms and topics from as far back as 2004. This project dives into manipulating and visualizing Google Trends data to find unique insights. In this project’s guided variant, you will explore the search data underneath the Kardashian family's fame and make custom plots to find how the most famous Kardashian/Jenner sister has changed over time. In the unguided variant, you will analyze the worldwide search interest of five major internet browsers to calculate metrics such as rolling average and percentage change.
- 1The sisters and Google Trends
- 2Better "kolumn" names
- 3Pesky data types
- 4From object to integer
- 5From object to datetime
- 6Set month as index
- 7The early Kim hype
- 8Kylie's rise
- 9Smooth out the fluctuations with rolling means
- 10Who's more famous? The Kardashians or the Jenners?
Data Science Educator
David graduated from Queen's University with a dual degree in Chemical Engineering and Economics. After working for a year, he discovered online education (in the early MOOC era) and became enamored with its potential. He has since created content to help people navigate the space, including a DIY data science master's program, Class Central's Data Science Career Guide, courses for Udacity's Data Analyst Nanodegree program, and several DataCamp courses and projects. Visit his website to say hi!