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	Submission Deadline	Lecture notes
06.10	Hello R!	soc-datascience-hello	13.10	Welcome to data science! Meet the toolkit: Programming Meet the toolkit: Version control & collaboration
13.10	Data visualization	soc-datascience-viz	20.10	Data and visualisation Visualising data with ggplot2 Visualising numerical data Visualising categorical data
20.10	Data wrangling	soc-datascience-wrang	27.10	Tidy data Grammar of data wrangling Working with a single data frame Working with multiple data frames Tidying data
27.10	Spatial data	soc-datascience-spatial	10.11	Data types Data classes Data import
10.11	Effective data visualization	soc-datascience-reshape	17.11	Data recode Effective data visualization
17.11	Simpson’s paradox	soc-datascience-paradox	24.11	Scientific studies and confounding Simpson’s paradox Doing data science
24.11	Collaboration on Github Work on projects	soc-datascience-collabor soc-datascience-project		Web scraping Scraping top 250 movies on IMDB Web scraping considerations
01.12	Web scraping	soc-datascience-scrap	08.12	Functions Iteration
08.12	Ethics and Data Science	soc-datascience-bias project proposals peer reviews	15.12	Misrepresentation Data privacy Algorithmic bias
15.12	Modelling data	soc-datascience-fitting	22.12	The language of models Fitting and interpreting models Modeling non-linear relationships Models with multiple predictors More models with multiple predictors
22.12	Classification and model building	soc-datascience-nfitting	12.01	Logistic regression Prediction and overfitting Feature engineering
12.01	Model validation	soc-datascience-valid	19.01	Cross validation
19.01	Uncertainty quantification	soc-datascience-hypo	26.01	Quantifying uncertainty Bootstrapping Hypothesis testing Inference overview
26.01	Text analysis Work on projects	soc-datascience-wrapup	02.01	Text analysis Comparing texts Interactive web apps Machine learning
02.02	Bayesian inference	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

Materials are madified version of datasciencebox under Creative Commons Attribution-ShareAlike 4.0 International Public License