Data Analytics for Emerging Technologies

By: Ajit Singh

Narrated by: Virtual Voice

"Data Analytics for Emerging Technologies" is a comprehensive and practical guide designed to navigate the dynamic intersection where data analytics meets the transformative technologies of Artificial Intelligence (AI), the Internet of Things (IoT), and Blockchain. This textbook serves as an essential resource for undergraduate (B.Tech) and postgraduate (M.Tech) students in Computer Science, Information Technology, Electronics, and other engineering disciplines. It is also an invaluable asset for researchers, data professionals, and technology enthusiasts seeking to upskill and stay ahead of the curve.


Key Features:


1. Foundational Clarity: Begins with a clear and concise introduction to the fundamental principles of Data Analytics, AI, IoT, and Blockchain, making the book accessible even to those new to the topics.
2. Real-World Case Studies: Each chapter is enriched with practical case studies, such as predictive maintenance in smart factories, fraud detection in blockchain networks, and customer sentiment analysis using AI.
3. Simplest Practical Examples: Complex concepts are explained using simple, relatable, real-life examples, like using IoT for smart city traffic management or blockchain for securing a food supply chain.
4. Focus on Convergence: A dedicated chapter explores the powerful synergies achieved by combining AI, IoT, and Blockchain, showcasing integrated systems like secure, AI-optimized supply chains.
5. Hands-On Capstone Project: The final chapter guides the reader through a live, working capstone project—"A Smart Healthcare Monitoring System"—integrating IoT data collection, AI-based anomaly prediction, and Blockchain for data integrity, complete with step-by-step code and explanation.
6. Up-to-Date Content: Covers the latest tools, techniques, and platforms used in the industry, including Python libraries (Pandas, Scikit-learn, TensorFlow), Big Data technologies (Apache Spark), and cloud services (AWS, Azure, GCP).
7. Ethical Framework: Provides a robust discussion on the critical aspects of data governance, bias in AI, explainability (XAI), and data privacy regulations like GDPR.
8. Structured for Learning: Each chapter follows a logical flow with clearly defined learning objectives, numbered topics, and summaries to reinforce understanding.


We begin by laying a strong foundation, introducing the core concepts of data analytics and the emerging technologies of AI, IoT, and Blockchain. We then dive deep into how data is acquired, processed, and analyzed within the context of each of these technologies. The central theme of convergence is explored in a dedicated chapter, showcasing how these technologies can be orchestrated to create robust, intelligent, and secure systems.