Global temperatures have been a hot topic in recent years as politicians argue about climate change policy and scientist try to identify how the climate is changing. Temperature data from around the world is an important part of this conversation. To measure temperature data scientist combine temperature from the air and ocean surface. This data is collected by weather stations on land and in the oceans.
The Project
As part of the Udacity Data Analysis Nanodegree, the project analyses and compares local and global temperature data. The data was gathered by connecting to a database using SQL and imported into a local Jupyter Notebook running on a Python kernel.
What We Learned
How to approach an analysis project, posing questions and drawing conclusions
Creating a rolling average in Python using the rolling() function
Utilising Matplotlib to visualise data with line charts and box plots, furnished with customised colour, axis, labels and title
The Code and the Report
GitHub repository for the data can be found here
the report can also be read here