The School of Commonwealths

The School of Commonwealths is an innovative model of the education system in which traditional classroom-based activities are transformed into globally connected communication systems (Pierzchalski, 2022). These systems are created by linking academic classes with similar topics between any universities worldwide. The linking is facilitated using GitHub. In the proposed model, students still study in the halls of the university but additionally use the GitHub service to communicate with each other, involving the review of assignments/projects analogous to the work of scientists who review each other’s publications. In this way, students develop critical thinking and foster a culture of collaboration.

To effectively start and manage the School of Commonwealths, you will need the GitHub CLI extension SoC.

The course schedule template

Date	Topic	Repository for lab & home activities	Submission deadline	Lecture notes
dd.mm.yy	Hello R!	soc-datascience-hello	+1w	Welcome to data science! Meet the toolkit: programming Meet the toolkit: version control & collaboration
dd.mm.yy	Data visualization	soc-datascience-viz	+1w	Data and visualisation Visualising data with ggplot2 Visualising numerical data Visualising categorical data
dd.mm.yy	Data wrangling	soc-datascience-wrang	+1w	Tidy data Grammar of data wrangling Working with a single data frame Working with multiple data frames Tidying data
dd.mm.yy	Spatial data	soc-datascience-spatial	+1w	Data types Data classes Data import
dd.mm.yy	Effective data visualization	soc-datascience-reshape	+1w	Data recode Effective data visualization
dd.mm.yy	Simpson's paradox	soc-datascience-paradox	+1w	Scientific studies and confounding Simpson’s paradox Doing data science
dd.mm.yy	Collaboration on Github Work on projects	soc-datascience-collabor soc-datascience-project		Web scraping Scraping top 250 movies on IMDB Web scraping considerations
dd.mm.yy	Web scraping	soc-datascience-scrap	+1w	Functions Iteration
dd.mm.yy	Ethics and Data Science	soc-datascience-bias project proposals peer reviews	+1w	Misrepresentation Data privacy Algorithmic bias
dd.mm.yy	Modelling data	soc-datascience-fitting	+1w	The language of models Fitting and interpreting models Modeling non-linear relationships Models with multiple predictors More models with multiple predictors
dd.mm.yy	Classification and model building	soc-datascience-nfitting	+1w	Logistic regression Prediction and overfitting Feature engineering
dd.mm.yy	Model validation	soc-datascience-valid	+1w	Cross validation
dd.mm.yy	Uncertainty quantification	soc-datascience-hypo	+1w	Quantifying uncertainty Bootstrapping Hypothesis testing Inference overview
dd.mm.yy	Text analysis (only lecture notes) Work on projects	soc-datascience-wrapup	+1w	Text analysis Comparing texts Interactive web apps Machine learning
dd.mm.yy	Bayesian inference (only lecture notes)	projects presentations		Interactive data visualization Interactive data visualization and reporting Bayesian inference

Library

• R for Data Science (2e): https://r4ds.hadley.nz/
• Statistical Inference via Data Science: https://moderndive.com/index.html
• Posit Cheatsheets: https://posit.co/resources/cheatsheets/
• Git Guide: https://github.com/git-guides

References

Pierzchalski, M. (2022). Szkoła Rzeczypospolitych [Polish version]. In A. B. Kwiatkowska & M. M. Sysło (Eds.), Informatyka w edukacji (pp. 128–138). ISBN 978-83-8180-645-9. Wydawnictwo Adam Marszałek. https://iwe.mat.umk.pl/materials/art2022/16.pdf. English version: The School of Commonwealths. https://doi.org/10.5281/zenodo.18096714

Licence

The course materials are madified version of datasciencebox under Creative Commons Attribution-ShareAlike 4.0 International Public License