PROJECTS

Big Data: COVID-19

Introduce your students to the art of data science through contextual learning. They’ll uncover trends and develop narratives using the global Covid-19 database.

Project timeline

Phase1
4Weeks timeframe

Prepare & launch: Teachers get ready and launch Big Data: Covid-19, using our helpful guidance documents.

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Phase2
11Weeks timeframe

Background research & skills development: With access to our support materials, students develop the knowledge and skills required to successfully complete research. This includes seminars and training packages on the virology of COVID-19 and epidemiology analysis using Excel and ‘R’, a mathematical statistics package. 

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Phase3
18Weeks timeframe

Student research: Students explore the global COVID-19 database, making correlations and statistical analyses of public health data.

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Phase4
9Weeks timeframe

Artefact development and conference:  Students produce an article, academic poster presentation or academic paper, based on their research process and/or findings with the aim of exhibiting at IRIS’ conference.

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This project is for UK state schools and colleges. It’s free and fully supported by our team. If you are a teacher and would like to start this project at your school, click the join button at the top right.

Overview

Skill level
Moderate /
Advanced
Age
suitability
16+
SubjectsPARTICLE PHYSICS/ COMPUTER SCIENCE/MATHS
SubjectsPARTICLE PHYSICS/ COMPUTER SCIENCE/MATHS

The volume of data available in our modern world is endless. Learning what good data looks like and how to decipher it will continue to be a valuable skill.

 

Big Data: Covid-19 introduces to students to the art of data science through contextual learning. They start by gaining context into SARS-CoV-2, then move onto big data. Lessons involve working with real virology, SARS-CoV-2 data, providing insight into how epidemiologists model pandemics.

 

Once they’ve got the basics down, budding data scientists get to further their skills using the global Covid database. Students learn to use Excel and its Data Analysis package to develop a narrative using basic statistics, creating linear regressions, and plotting histograms. Once they master this, they move onto R, a mathematical statistics programming language.

A new, a vast, and a powerful language is developed for the future use of analysis, in which to wield its truths so that these may become of more speedy and accurate practical application for the purposes of mankind than the means hitherto in our possession have rendered possible.

Ada Lovelace1843