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Data Engineering with AWS: A practical guide to building scalable and secure enterprise data platforms (English Edition) by Sanjiv Kumar Jha
English | August 28, 2025 | ISBN: 9365890969 | 446 pages | MOBI | 4.31 Mb
Data engineering and AWS form the backbone of modern enterprise data architecture, enabling organizations to harness the exponential growth of data for competitive advantage. As businesses generate petabytes of information daily, the ability to build scalable, secure, and cost-effective data platforms has become critical for survival in today's data-driven economy.

Data Engineering for Machine Learning: Designing Robust Pipelines and Workflows by Nicholas Hopkins
English | June 14, 2025 | ISBN: N/A | ASIN: B0FD8YCVM8 | 194 pages | EPUB | 0.28 Mb
Data Engineering for Machine Learning: Designing Robust Pipelines and Workflows

Data Engineering for Cybersecurity: Designing Scalable Pipelines, Schemas, and Analytics to Power Modern Threat Hunting and Incident Response by John M. Wade
English | December 20, 2025 | ISBN: B0G9T5XFMS | 147 pages | EPUB | 2.80 Mb
Master the Data Behind the Defense

Data Clustering with Python: From Theory to Implementation (Chapman & Hall/CRC The Python Series) by Guojun Gan
English | September 14, 2025 | ISBN: 1032971568 | 248 pages | MOBI | 3.97 Mb
Data clustering, an interdisciplinary field with diverse applications, has gained increasing popularity since its origins in the 1950s. Over the past six decades, researchers from various fields have proposed numerous clustering algorithms. In 2011, I wrote a book on implementing clustering algorithms in C++ using object-oriented programming. While C++ offers efficiency, its steep learning curve makes it less ideal for rapid prototyping. Since then, Python has surged in popularity, becoming the most widely used programming language since 2022. Its simplicity and extensive scientific libraries make it an excellent choice for implementing clustering algorithms.

Data Analytics using Machine Learning Techniques on Cloud Platforms by Seema Rawat, Neelu Jyothi Ahuja, Avita Katal
English | September 22, 2025 | ISBN: 1032491469 | 204 pages | MOBI | 3.54 Mb
Data Analytics using Machine Learning Techniques on Cloud Platforms examines how machine learning (ML) and cloud computing combine to drive data-driven decision-making across industries. Covering ML techniques, loud-based analytics tools and security concerns, this book provides theoretical foundations and real-world applications in fields like healthcare, logistics and e-commerce. It also addresses security challenges, privacy concerns and compliance frameworks, ensuring a comprehensive understanding of cloud-based analytics.

Aldo Dagnino, "Data Analytics in the Era of the Industrial Internet of Things"
English | ISBN: 3030631389 | 2021 | 150 pages | MOBI | 7 MB
This book presents the characteristics and benefits industrial organizations can reap from the Industrial Internet of Things (IIoT). These characteristics and benefits include enhanced competitiveness, increased proactive decision-making, improved creativity and innovation, augmented job creation, heightened agility to respond to continuously changing challenges, and intensified data-driven decision making. In a straightforward fashion, the book also helps readers understand complex concepts that are core to IIoT enterprises, such as Big Data, analytic architecture platforms, machine learning (ML) and data science algorithms, and the power of visualization to enrich the domains experts' decision making. The book also guides the reader on how to think about ways to define new business paradigms that the IIoT facilitates, as well how to increase the probability of success in managing analytic projects that are the core engine of decision-making in the IIoT enterprise. The book starts by defining an IIoT enterprise and the framework used to efficiently operate. A description of the concepts of industrial analytics, which is a major engine for decision making in the IIoT enterprise, is provided. It then discusses how data and machine learning (ML) play an important role in increasing the competitiveness of industrial enterprises that operate using the IIoT technology and business concepts. Real world examples of data driven IIoT enterprises and various business models are presented and a discussion on how the use of ML and data science help address complex decision-making problems and generate new job opportunities. The book presents in an easy-to-understand manner how ML algorithms work and operate on data generated in the IIoT enterprise.

Nitin Jaglal Untwal, "Data Analytics for Finance Using Python "
English | ISBN: 103261823X | 2025 | 138 pages | AZW3 | 3 MB
Unlock the power of data analytics in finance with this comprehensive guide. Data Analytics for Finance Using Python is your key to unlocking the secrets of the financial markets.

Data Analytics for Emerging Technologies by Ajit Singh
English | August 7, 2025 | ISBN: N/A | ASIN: B0FLPVYDWP | 292 pages | EPUB | 0.92 Mb
"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.

Vincent Charles, "Data Analytics and Business Intelligence"
English | ISBN: 1032039043 | 2023 | 274 pages | AZW3 | 9 MB
Business Analytics (BA) is an evolving phenomenon that showcases the increasing importance of using huge volumes of data to generate value for businesses. Advances in BA have offered great opportunities for organisations to improve, innovate, and develop existing or new processes, products, and services. BA is the process of transforming data into actionable insight by using statistical and mathematical analysis, descriptive, prescriptive, and predictive models, machine learning, information systems and network science methods, among others, along with a variety of data, expert knowledge, and fact-based management to support better and faster decision-making.

Data Analytics: From Novice to Mastermind: A Comprehensive Skill-Building Course for Data-Driven Thinking, Insight Discovery, and Professional Growth
English | 17 Jan. 2026 | ASIN: B0GGZ5VZGG | 118 pages | Epub | 443.42 KB
Data analytics is no longer optional. It is the foundation of modern decision-making across business, technology, healthcare, finance, and public policy. This book is designed to take you from zero analytical thinking to confident, professional-level data analysis , even if you have no prior technical background. Unlike tool-only tutorials, this book focuses on how analysts think , not just which buttons they click. You will learn: How data analytics evolved and why it matters today How to understand, collect, clean, and evaluate real-world data How to think analytically instead of guessing How to avoid common statistical and logical mistakes How Excel, SQL, Python, and BI tools are used in practice How to communicate insights clearly to stakeholders How to build a strong analytics portfolio and career path This book emphasizes clarity, real-world examples, and decision-making , not mathematical intimidation or buzzwords. 📌 Who this book is for Beginners starting from scratch Professionals wanting data literacy Career switchers entering analytics roles Anyone who wants to make better decisions with data 📌 Who this book is NOT for Advanced statisticians or academic researchers Readers seeking heavy mathematical proofs If you want to build a strong analytical mindset and practical skills that actually transfer to real work , this book is your starting point