Resources for data science are anything but scarce. However, finding concise, to-the-point, useful, and thought-provoking resources isn’t as common as one might think. Below, I’ve curated a list of resources that I find succinct, straightforward, practical, and stimulating. The list follows, roughly, the order in which research might be conducted and covers a broad scope of data science topics, from hypothesis testing to NLP and machine learning.

This post will be updated periodically as I discover new gems.

Data Sources for Cross-Cultural Research on Threats

• Vision of Humanity Maps: Interactive maps exploring global peace and sustainability indicators.
• Our World in Data: War and Peace Data Explorers: Comprehensive datasets on conflict, peace, and social indicators.

Big Data for Psychology

• Open Psychometrics Raw Data: Datasets for personality and psychology research.

Sample Size Determination and Power Analysis

• Brysbaert, M. (2019). How many participants do we have to include in properly powered experiments? A tutorial of power analysis with reference tables. Journal of Cognition, 2(1). Access here.

Random Variables and Probability Distributions

• YouTube Playlist: Random Variables and Probability Distributions: A series covering foundational topics in probability.

Software Tutorials

• Statistical Analysis in JASP: A Student’s Guide: Beginner-friendly guide to using JASP for statistical analysis.
• Hands-On Machine Learning with R: A practical guide to machine learning in R.

Data Preprocessing

• The Quartz Guide to Bad Data: Tips for identifying and managing messy data.

Phylogenetic Non-Independence

• Bürkner, P. (2024, September 23). Estimating Phylogenetic Multilevel Models with brms. CRAN vignette: A guide to modeling non-independence in phylogenetic data with brms in R.

Presenting the Results

• UXWing: Free icons and vector graphics for enhancing data presentations.