The AI Analytics

Beyond the Dashboard

De: Ajit Singh

Narrado por: Virtual Voice

"The AI Analytics: Beyond the Dashboard" is a comprehensive and practical guide designed to equip students and professionals with the essential skills to design, build, and deploy modern artificial intelligence-driven analytics solutions. It bridges the gap between theoretical knowledge of algorithms and the practical expertise required to solve real-world business problems.


Philosophy

The core philosophy of this book is "implementation-first." While a solid theoretical understanding is crucial, the true value of AI Analytics is realized only when models are successfully deployed and integrated into functional systems. This book moves beyond abstract mathematical formulations to focus on the end-to-end lifecycle of an AI Analytics project—from data ingestion and preparation to model training, ethical evaluation, deployment, and operational monitoring. It champions the idea that an analytics professional must be both a scientist and an engineer.


Key Features

1. Application-Centric: Strong emphasis on practical applications in various domains like finance, retail, healthcare, and manufacturing.
2. End-to-End Project Lifecycle: Covers the complete process, including architecture design, model implementation, deployment, and operational functioning.
3. Focus on Modern Practices: Includes dedicated chapters on crucial modern topics such as Interpretable AI (XAI), ethical considerations, and MLOps.
4. Complete Capstone Project: The final chapter guides the reader through building a live, working AI Analytics application with fully explained code.
5. Simple and Explanatory Language: Complex topics are broken down into simple, easy-to-understand components with clear explanations and analogies.


To Whom This Book Is For

1. B.Tech/M.Tech Computer Science Students: An ideal textbook that aligns with modern AI, Machine Learning, and Data Science curricula.
2. Aspiring Data Scientists and AI Engineers: A practical guide to acquiring the hands-on skills required for the industry.
3. Software Developers and IT Professionals: A resource for upskilling and transitioning into the high-demand field of AI and Machine Learning.
4. Business Analysts and Product Managers: A clear explanation of the capabilities and implementation of AI Analytics to drive better product and business decisions.