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Optimizing the Python Code for Big Data: Balancing Coding Complexity against Computational Complexity (Big Data Preparation - Learn by Doing) - Softcover
Synopsis
Big Data is more than just a buzzword—it's a gateway to uncovering groundbreaking insights. Yet, harnessing its potential requires strategic approaches and technical expertise to manage computational and memory resources effectively. This book is your hands-on guide to mastering the art of writing efficient Python code that scales with Big Data challenges.
As the second book in the "Big Data Preparation - Learn by Doing" series, "Optimizing the Python Code for Big Data" builds on the foundation established in the first book. While the first book focuses on defining and optimizing Big Data problem statements, this book dives deeper into the technical aspects of coding and resource management, equipping readers with the tools and strategies to handle complex data processing tasks with Python.
Key Features of the Book:- Resource Optimization: Learn how to effectively manage computational and memory resources to avoid common pitfalls like workspace overload and worker inefficiency.
- Big O Notation Made Simple: Gain a clear understanding of Big O complexities and how they influence code performance.
- Choosing the Right Data Types and Structures: Make informed decisions to prevent wasted RAM, CPU cycles, and runtime.
- Advanced Coding Patterns: Explore hands-on challenges that teach you how to apply patterns like sliding windows, two-pointers, and recursion.
- Vectorization and Broadcasting: Unlock Python's true potential by leveraging advanced techniques to eliminate nested loops and maximize performance.
- Tool Selection Strategies: Develop a framework for choosing the right tools for tasks such as image preprocessing, data restructuring, and algorithm optimization.
Packed with practical examples, coding challenges, and real-world case studies, this book is designed for data scientists, engineers, and Python enthusiasts who are ready to take their skills to the next level. Whether you're optimizing a machine learning pipeline or processing vast datasets, the insights and strategies in this book will empower you to write smarter, faster, and more efficient code.
Who Should Read This Book?This book is perfect for professionals and students in:
- Data Science
- Software Engineering
- Big Data Analytics
- Python Programming
" Optimizing the Python Code for Big Data" is an essential resource if you're serious about advancing your Big Data expertise and improving your Python coding efficiency.
Start your journey to mastering Big Data optimization today!
"synopsis" may belong to another edition of this title.
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Optimizing the Python Code for Big Data (Paperback)
Seller: Grand Eagle Retail, Mason, OH, U.S.A.
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Paperback. Condition: new. Paperback. Big Data is more than just a buzzword-it's a gateway to uncovering groundbreaking insights. Yet, harnessing its potential requires strategic approaches and technical expertise to manage computational and memory resources effectively. This book is your hands-on guide to mastering the art of writing efficient Python code that scales with Big Data challenges.As the second book in the "Big Data Preparation - Learn by Doing" series, "Optimizing the Python Code for Big Data" builds on the foundation established in the first book. While the first book focuses on defining and optimizing Big Data problem statements, this book dives deeper into the technical aspects of coding and resource management, equipping readers with the tools and strategies to handle complex data processing tasks with Python.Key Features of the Book: Resource Optimization: Learn how to effectively manage computational and memory resources to avoid common pitfalls like workspace overload and worker inefficiency.Big O Notation Made Simple: Gain a clear understanding of Big O complexities and how they influence code performance.Choosing the Right Data Types and Structures: Make informed decisions to prevent wasted RAM, CPU cycles, and runtime.Advanced Coding Patterns: Explore hands-on challenges that teach you how to apply patterns like sliding windows, two-pointers, and recursion.Vectorization and Broadcasting: Unlock Python's true potential by leveraging advanced techniques to eliminate nested loops and maximize performance.Tool Selection Strategies: Develop a framework for choosing the right tools for tasks such as image preprocessing, data restructuring, and algorithm optimization.Packed with practical examples, coding challenges, and real-world case studies, this book is designed for data scientists, engineers, and Python enthusiasts who are ready to take their skills to the next level. Whether you're optimizing a machine learning pipeline or processing vast datasets, the insights and strategies in this book will empower you to write smarter, faster, and more efficient code.Who Should Read This Book?This book is perfect for professionals and students in: Data ScienceSoftware EngineeringBig Data AnalyticsPython Programming" Optimizing the Python Code for Big Data" is an essential resource if you're serious about advancing your Big Data expertise and improving your Python coding efficiency.Start your journey to mastering Big Data optimization today! Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Seller Inventory # 9798304571180
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Optimizing the Python Code for Big Data: Balancing Coding Complexity against Computational Complexity (Big Data Preparation - Learn by Doing)
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Optimizing the Python Code for Big Data (Paperback)
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Paperback. Condition: new. Paperback. Big Data is more than just a buzzword-it's a gateway to uncovering groundbreaking insights. Yet, harnessing its potential requires strategic approaches and technical expertise to manage computational and memory resources effectively. This book is your hands-on guide to mastering the art of writing efficient Python code that scales with Big Data challenges.As the second book in the "Big Data Preparation - Learn by Doing" series, "Optimizing the Python Code for Big Data" builds on the foundation established in the first book. While the first book focuses on defining and optimizing Big Data problem statements, this book dives deeper into the technical aspects of coding and resource management, equipping readers with the tools and strategies to handle complex data processing tasks with Python.Key Features of the Book: Resource Optimization: Learn how to effectively manage computational and memory resources to avoid common pitfalls like workspace overload and worker inefficiency.Big O Notation Made Simple: Gain a clear understanding of Big O complexities and how they influence code performance.Choosing the Right Data Types and Structures: Make informed decisions to prevent wasted RAM, CPU cycles, and runtime.Advanced Coding Patterns: Explore hands-on challenges that teach you how to apply patterns like sliding windows, two-pointers, and recursion.Vectorization and Broadcasting: Unlock Python's true potential by leveraging advanced techniques to eliminate nested loops and maximize performance.Tool Selection Strategies: Develop a framework for choosing the right tools for tasks such as image preprocessing, data restructuring, and algorithm optimization.Packed with practical examples, coding challenges, and real-world case studies, this book is designed for data scientists, engineers, and Python enthusiasts who are ready to take their skills to the next level. Whether you're optimizing a machine learning pipeline or processing vast datasets, the insights and strategies in this book will empower you to write smarter, faster, and more efficient code.Who Should Read This Book?This book is perfect for professionals and students in: Data ScienceSoftware EngineeringBig Data AnalyticsPython Programming" Optimizing the Python Code for Big Data" is an essential resource if you're serious about advancing your Big Data expertise and improving your Python coding efficiency.Start your journey to mastering Big Data optimization today! Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Seller Inventory # 9798304571180
Quantity: 1 available