Data Science

Foundations, Systems, and the 2025 Generative AI Frontier

Azhar ul Haque Sario

• Publisher: Azhar Sario Hungary
• Year: 2025
• eBook ISBN: 9783384755537
• Topic: Computer Science
• Subtopic: Business Mathematics
• Index: Computer Science

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.

Table of contents

• Copyright
• Part 1: Foundations of Data Science and the Data-Driven Mindset
• Part 2: Core Machine Learning and Statistical Inference