Introduction to Data Analysis using Python/Jupyter Notebook

Python is a popular programming language in sciences, engineering and social sciences to analyze data. It is also a popular scripting language in web application and computer-aided design communities. Python is a great first programming language to learn as the programming syntax are easily readable. It is also freely available and there are loads of learning materials in YouTube, GitHub, stack overflow etc.

RCP has partnered with a local charitable organization, DigiLocal, to create a project guide for introducing young people to data analysis using Python. DigiLcoal organizes free technology clubs in various under-represented communities around Bristol for their young people to promote problem solving skills and resilience within a friendly environment: https://digilocal.org.uk/about-us/

The project guide uses Jupyter notebook for analyzing air quality data set maintained by Department of Environment, Food and Rural Affairs (DEFRA). Young people are guided through processing NO2 measurement data set obtained from London and Bristol, before and after the COVID-19 pandemic, using pandas module. Through simple statistical analysis (average and standard deviation) using NumPy module, they can find out whether the pandemic has resulted in any change in air quality of these two cities. They are also guided to visualize their analysis through simple plotting using matplotlib module. The guide should also prepare them to analyze seasonal dependence of NO2 concentration in the two cities as an independent challenge! The guide, Jupyter notebook and the datasets are uploaded in a repository linked here: https://github.com/rc13564/Digilocal_AirWeBreathe

This guide could be easily modified to include air pollution measurements from various areas of interest within the UK. All materials are free to use for non-profit purposes.

Published by rcpchem

Rabi Chhantyal-Pun is an Assistant Professor of Chemistry at the University of Nottingham

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