This textbook covers the contents of an introductory statistics class, as typically taught to undergraduate psychology, health or social science students.

Fun little introductory book for descriptive statistics, visualizations, then some theory, regressions and at the very end a tiny bit of bayesian stats.
Uses 'jamovi', a statistical software, but can be acommodated with all manner of toole like SPSS, PSPP, R, Pandas, Polars, etc etc

Learning to model causality and make causal inferences with R (though applicable to other data science toolkits). Very nice, and splitting right down the middle of statistical and programmatic learning.

An exhaustive book, free and available online, on publishing workflow.

Getting, preparing, cleaning data. Exploratory analysis and modelling with regression. Creating reproducible documents with quarto. Seems really nice and good to delve into for data analysis.

Statistical inferrence, various python plot types and Correlation vs causation explained in a series of blog-posts. Very beginner-friendly with drawings etc

Statistics concepts explained (and tried to do so in plain english). That means some nuance will be lost but might get you to understand results quicker.

List of resources to delve deeper into data science and/or data engineering. Very interesting suggestions and enough overlap that it's not just a 'random list'

Various statistics courses hosted for free