Exploration of climate change data for Trilogy bootcamp through University of California San Diego.
The purpose of this project was to compare climate, energy consumption, population and disaster datasets over a 30 year time period. These comparisons helped us (Stephen Hong, Trevor Kleinstuber, Laura Paakh May, Sagar Patel, and Thompson Tang) to develop our skills in forming hypothesis, coding in python, creating visualizations, and linear regressions. We wanted to visualize climate change related data and take a closer look at how this scientific phenomenon is effecting the Earth.
- Inferential Statistics
- Data Visualization
- Predictive Modeling
- Python
- Pandas, jupyter
- etc.
For the project we focused on comparing and visualizing the following datasets: Global disasters, total energy consumption, petroleum consumption, nuclear consumption, natural gas consumption, coal consumption, clean energy consumption, Uber passengers usage by year, global temperature, and population density. We set out on proving the hyphothesis that climate chnage is indeed happening and having these effects. The data we used was restricted to 1986-2016 because it is where our data overlaped in all sources. If we had more time we could have searched for data that encompassed a time length of at least 500 years (for some datasets).
- data exploration/descriptive statistics
- data processing/cleaning
- statistical modeling
- writeup/reporting
