Data Science Engineering

Author Martin Kleppmann helps you navigate the diverse data landscape by examining the pros and cons of various technologies for processing and storing data. Software keeps changing, but the fundamental principles remain the same. With this book, software engineers and architects will learn how to apply those ideas in practice, and how to make full use of data in modern applications. 

This comprehensive guide to Microsoft Azure Data Engineering is designed to elevate your skills in data analytics, pipeline development, and Azure cloud solutions. Dive deep into essential concepts like data integration, ETL, SQL, Data Lakes, storage management, Data Governance, Data Strategy and big data architecture. This eBook is a Number #1 Best Seller in the following categories: #1 Data Warehousing #1 Computer Engineering #1 SQL Programming In today's job market, skilled Azure Data Engineers are rare, and their compensation reflects that rarity. They enjoy substantial salaries and the...

Expand your skills from being a basic data scientist to becoming an expert data scientist ready to solve real-world data-centric issues. Discover two comprehensive manuscripts in one audiobook.

Data engineering has grown rapidly in the past decade, leaving many software engineers, data scientists, and analysts looking for a comprehensive view of this practice. With this practical book, you'll learn how to plan and build systems to serve the needs of your organization and customers by evaluating the best technologies available through the framework of the data engineering lifecycle.

This book provides a comprehensive exploration of feature engineering, hyperparameter tuning, model interpretability, and production-level machine learning practices. Designed for practitioners and advanced learners, it delves into the techniques, tools, and strategies required to build robust, scalable, and explainable machine learning systems. From automated pipelines to drift detection and continuous monitoring, each chapter bridges theory with practical implementation to equip readers with the skills needed to deploy and maintain high-performing models in real-world environments.

Book Title: Handbook for Prompt Engineering: A Complete Guide and Examples for Prompt Engineering Science from Scratch to Application Unlock the full potential of AI-driven interactions with the definitive guide to prompt engineering. "Handbook for Prompt Engineering: A Complete Guide and Examples for Prompt Engineering Science from Scratch to Application" provides an authoritative and comprehensive exploration of the principles, techniques, and best practices essential for mastering prompt engineering in natural language processing (NLP) and artificial intelligence (AI). About the Book: ...

You are going to need more than technical knowledge to succeed as a data scientist. Build a Career in Data Science teaches you what school leaves out, from how to land your first job to the lifecycle of a data science project, and even how to become a manager. Build a Career in Data Science is your guide to landing your first data science job and developing into a valued senior employee.

Data Science for Dummies is the perfect starting point for IT professionals and students who want a quick primer on all areas of the expansive data science space. With a focus on business cases, the book explores topics in big data, data science, and data engineering, and how these three areas are combined to produce tremendous value. If you want to pick-up the skills you need to begin a new career or initiate a new project, listening to this book will help you understand what technologies, programming languages, and mathematical methods on which to focus. 

Data science experts Foster Provost and Tom Fawcett, Data Science for Business introduces the fundamental principles of data science, and walks you through the "data-analytic thinking" necessary for extracting useful knowledge and business value from the data you collect. This guide also helps you understand the many data-mining techniques in use today. 

Data Analytics for Absolute Beginners is your no-stress, no-nonsense guide to understanding and applying data analysis, even if you've never worked with data before. This hands-on, beginner-friendly guide is packed with practical examples, visual explanations, and two bonus Python exercises (with video tutorials) to walk you through real-world analysis scenarios.

Practical Data Science with R lives up to its name. It explains basic principles without the theoretical mumbo-jumbo and jumps right to the real use cases you'll face as you collect, curate, and analyze the data crucial to the success of your business. You'll apply the R programming language and statistical analysis techniques to carefully explained examples based in marketing, business intelligence, and decision support.

As a software engineer, you’ll encounter countless programming challenges that initially seem confusing, difficult, or even impossible. Don’t despair! Many of these “new” problems already have well-established solutions. Advanced Algorithms and Data Structures teaches you powerful approaches to a wide range of tricky coding challenges that you can adapt and apply to your own applications. Providing a balanced blend of classic, advanced, and new algorithms, this practical guide upgrades your programming toolbox with new perspectives and hands-on techniques.

James Gleick, the author of the best sellers Chaos and Genius, now brings us a work just as astonishing and masterly: a revelatory chronicle and meditation that shows how information has become the modern era’s defining quality—the blood, the fuel, the vital principle of our world. The story...

Are your data engineering and analytics teams overwhelmed by a fragmented ecosystem that makes generating actionable business insights nearly impossible? Today, enterprises are generating more data than ever, but most are paralyzed by disjointed tools, isolated data lakes, and complex multi-cloud architectures. When your data scientists and business analysts are forced to operate in silos using disconnected systems, the result is crippling inefficiency. This duplication of effort leads to the proliferation of Shadow IT, exorbitant costs from unshared computing resources, and massive ...

Data science happens in code. The ability to write reproducible, robust, scaleable code is key to a data science project's success—and is absolutely essential for those working with production code. This practical book bridges the gap between data science and software engineering, and clearly explains how to apply the best practices from software engineering to data science.

Inside this book, readers will learn how to: Apply a five-question framework before writing a single line of architecture — identifying what decision the system supports, who consumes its output, how fresh that output must be, what a wrong output costs, and what the system does when it is unavailable Design the data layer for ML workloads from first principles — covering data contracts, schema versioning, lineage tracking, and the boundary discipline that prevents the silent cross-team failures most ML incidents trace back to Build feature engineering pipelines with point-in-time ...

In this book, you’ll find methods and techniques to organize your data, extract the information, and analyze it. There’s a way to make sure your company is going in the right direction, and this audiobook will reveal the secret behind a successful business.

Would you like to learn to use Python extracting meaningful insight from data to grow your business but you reckon it will be too complex? As a beginner, you will be probably afraid that programming is difficult. Learning data analysis and data mining can take months, and the possibility to give up before mastering them could be high. The best solution is a complete programming manual with hands-on projects and practical exercises. This guide is structured as a course with seven chapters for seven days and studied special exercises to apply what you learned step-by-step.

A plain‑English guide that reveals the hidden physics inside everyday machines—so you can finally see how the hardware around you actually works. Machines shape almost everything you touch: the car in the driveway, the furnace in the basement, the refrigerator humming in the kitchen, the power plant miles away that keeps the lights on. Behind every one of them is the quiet, elegant discipline that turns physical law into working hardware. This book is a plain-English tour of that discipline, written for readers who want to understand what is really happening inside pumps, engines, coils...

"Time Series Analysis" is a comprehensive, practical, and accessible guide for students and professionals seeking to master the art and science of forecasting. In today's data-driven world, the ability to analyze time-dependent data is a critical skill across industries—from finance and engineering to retail and climate science. This book provides a seamless learning pathway, taking you from the foundational principles of time series analysis to the cutting-edge of deep learning. Key Features of This Book: 1. Progressive Learning Path: Logically progresses from simple statistical methods ...