Your 2025 Blueprint to Master Data Science—From Zero to Generative AI Hero!
This book is your all-in-one launchpad into data science in 2025. Part 1 nails the mindset: computational + inferential thinking + real-world relevance, straight from Berkeley's Data 8. You'll master the CRISP-DM lifecycle, craft killer project proposals, and dissect Walmart's inventory genius. Day-one tools? Python (NumPy, Pandas), Git branching, SQL window functions, and Tableau dashboards. Ethics isn't an afterthought—fairness, bias audits, EU AI Act compliance, and DAMA-DMBOK governance are baked in. Visualization? Tufte's rules, GeoPandas maps, and public-health climate dashboards. Part 2 is the ML engine: hypothesis tests, A/B frameworks, causal DAGs with DoWhy, linear/logistic regression, decision trees to XGBoost, PCA, K-means, and Kaggle-winning feature hacks. Privacy? Differential privacy, federated learning, and HIPAA-safe GenAI. Every chapter ends with job-ready tutorials, LeetCode SQL, and real case studies—no fluff, just code you can run today.
Other books teach yesterday's tricks; this one arms you for 2025's frontier. While competitors recycle 2019 Kaggle notebooks, we weave Generative AI reality checks—LLM hallucinations vs. causal rigor—into every model. You won't just predict; you'll deploy production-grade pipelines with Airflow, dbt, Great Expectations, and Evidently monitoring. No ivory-tower theory: every concept ties to revenue, risk, or regulation. Unlike dense textbooks, our bite-sized code labs, Git workflows, and compliance checklists get you hired faster. This is the only guide that treats ethics, governance, and GenAI as core muscles, not side quests.
Copyright © 2025 Azhar ul Haque Sario. This work is independently produced under nominative fair use and has no affiliation with UC Berkeley, Stanford, DAMA, DASCA, or any cited institution or company.
