pierzcham/soc-datascience

SoC Data Science Course

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

Licence

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