Book Notes
2027
- 📚Designing Data-Intensive ApplicationsMartin Kleppmann — The definitive guide to building reliable, scalable, and maintainable data systems.
- 📚Statistical Rethinking: A Bayesian CourseRichard McElreath — A brilliant introduction to Bayesian statistics with a focus on practical modeling and causal thinking.
2026
- 📖Beyond Cracking the Coding InterviewGayle Laakmann McDowell, Mike Mroczka, Aline Lerner, Nil Mamano — A comprehensive guide that goes beyond technical preparation to cover the full software engineering interview process.
- 📖Hands-On Machine Learning with Scikit-Learn and PyTorchAurélien Géron — A practical guide to machine learning fundamentals using Scikit-Learn and PyTorch, covering everything from simple linear regression to deep neural networks.
- 📖Causal Inference in Python: Applying Causal Inference in the Tech IndustryMatheus Facure — A practical guide to applying causal inference techniques in tech, data science, and business decision-making using Python.
- 📖OpenIntro StatisticsDavid Diez, Mine Çetinkaya-Rundel, Christopher D. Barr — A comprehensive and open-access introduction to statistical methods, probability, and data analysis.