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’ve found succinct, straightforward, practical, and stimulating.

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

Bayesian Statistics

Johnson, A. A., Ott, M. Q., & Dogucu, M. (2022). Bayes rules!: An introduction to applied Bayesian modeling. Chapman and Hall/CRC.
A great, easy-to-understand introduction to Bayesian data analysis. Some calculus and probability help, but the book is accessible even without them.

Inference vs Prediction

• Breiman, L. (2001). Statistical modeling: The two cultures. Statistical science, 16(3), 199-231.

• Shmueli, G. (2010). To explain or to predict?. Statistical science, 289-310.

• Yarkoni, T., & Westfall, J. (2017). Choosing prediction over explanation in psychology: Lessons from machine learning. Perspectives on Psychological Science, 12(6), 1100–1122.

• Ij, H. (2018). Statistics versus machine learning. Nature Methods, 15(4), 233.

• Daoud, A., & Dubhashi, D. (2023). Statistical modeling: the three cultures. Harvard Data Science Review, 5(1).

Interesting Statistics Papers

Cinelli, C., Forney, A., & Pearl, J. (2024). A crash course in good and bad controls. Sociological Methods & Research, 53(3), 1071-1104.

O’Boyle Jr, E., & Aguinis, H. (2012). The best and the rest: Revisiting the norm of normality of individual performance. Personnel Psychology, 65(1), 79-119.

Machine learning

Lantz, B. (2013). Machine learning with R: learn how to use R to apply powerful machine learning methods and gain an insight into real-world applications.
A great source to start with. Its explanation , for example about KNN, are very easy to understand.

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.

Statistics Textbooks

• Chapman & Hall/CRC Texts in Statistical Science
  more than one hundred textbooks mostly written by statisticians

Null Hypothesis Significance Testing

• Szucs, D., & Ioannidis, J. P. (2017). When null hypothesis significance testing is unsuitable for research: a reassessment. Frontiers in human neuroscience, 11, 390.