Jafari Roy (25 results)

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Paperback or Softback. Condition: New. Hands-On Data Preprocessing in Python: Learn how to effectively prepare data for successful data analytics. Book.

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Paperback. Condition: New. Get your raw data cleaned up and ready for processing to design better data analytic solutionsKey FeaturesDevelop the skills to perform data cleaning, data integration, data reduction, and data transformationMake the most of your raw data with powerful data transformation and massaging techniquesPerform thorough data cleaning, including dealing with missing values and outliersBook DescriptionHands-On Data Preprocessing is a primer on the best data cleaning and preprocessing techniques, written by an expert who's developed college-level courses on data preprocessing and related subjects. With this book, you'll be equipped with the optimum data preprocessing techniques from multiple perspectives, ensuring that you get the best possible insights from your data. You'll learn about different technical and analytical aspects of data preprocessing - data collection, data cleaning, data integration, data reduction, and data transformation - and get to grips with implementing them using the open source Python programming environment. The hands-on examples and easy-to-follow chapters will help you gain a comprehensive articulation of data preprocessing, its whys and hows, and identify opportunities where data analytics could lead to more effective decision making. As you progress through the chapters, you'll also understand the role of data management systems and technologies for effective analytics and how to use APIs to pull data. By the end of this Python data preprocessing book, you'll be able to use Python to read, manipulate, and analyze data; perform data cleaning, integration, reduction, and transformation techniques, and handle outliers or missing values to effectively prepare data for analytic tools.What you will learnUse Python to perform analytics functions on your dataUnderstand the role of databases and how to effectively pull data from databasesPerform data preprocessing steps defined by your analytics goalsRecognize and resolve data integration challengesIdentify the need for data reduction and execute itDetect opportunities to improve analytics with data transformationWho this book is forThis book is for junior and senior data analysts, business intelligence professionals, engineering undergraduates, and data enthusiasts looking to perform preprocessing and data cleaning on large amounts of data. You don't need any prior experience with data preprocessing to get started with this book. However, basic programming skills, such as working with variables, conditionals, and loops, along with beginner-level knowledge of Python and simple analytics experience, are a prerequisite.

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Paperback. Condition: New. Get your raw data cleaned up and ready for processing to design better data analytic solutionsKey FeaturesDevelop the skills to perform data cleaning, data integration, data reduction, and data transformationMake the most of your raw data with powerful data transformation and massaging techniquesPerform thorough data cleaning, including dealing with missing values and outliersBook DescriptionHands-On Data Preprocessing is a primer on the best data cleaning and preprocessing techniques, written by an expert who's developed college-level courses on data preprocessing and related subjects. With this book, you'll be equipped with the optimum data preprocessing techniques from multiple perspectives, ensuring that you get the best possible insights from your data. You'll learn about different technical and analytical aspects of data preprocessing - data collection, data cleaning, data integration, data reduction, and data transformation - and get to grips with implementing them using the open source Python programming environment. The hands-on examples and easy-to-follow chapters will help you gain a comprehensive articulation of data preprocessing, its whys and hows, and identify opportunities where data analytics could lead to more effective decision making. As you progress through the chapters, you'll also understand the role of data management systems and technologies for effective analytics and how to use APIs to pull data. By the end of this Python data preprocessing book, you'll be able to use Python to read, manipulate, and analyze data; perform data cleaning, integration, reduction, and transformation techniques, and handle outliers or missing values to effectively prepare data for analytic tools.What you will learnUse Python to perform analytics functions on your dataUnderstand the role of databases and how to effectively pull data from databasesPerform data preprocessing steps defined by your analytics goalsRecognize and resolve data integration challengesIdentify the need for data reduction and execute itDetect opportunities to improve analytics with data transformationWho this book is forThis book is for junior and senior data analysts, business intelligence professionals, engineering undergraduates, and data enthusiasts looking to perform preprocessing and data cleaning on large amounts of data. You don't need any prior experience with data preprocessing to get started with this book. However, basic programming skills, such as working with variables, conditionals, and loops, along with beginner-level knowledge of Python and simple analytics experience, are a prerequisite.

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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! This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

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Condition: New. Whether you re a data analyst new to programming or already familiar with it, this book will teach you the optimum techniques for data preprocessing from both technical and analytical perspectives. You ll explore the world of advanced data manipulation and .

Paperback. Condition: New. Get your raw data cleaned up and ready for processing to design better data analytic solutionsKey FeaturesDevelop the skills to perform data cleaning, data integration, data reduction, and data transformationMake the most of your raw data with powerful data transformation and massaging techniquesPerform thorough data cleaning, including dealing with missing values and outliersBook DescriptionHands-On Data Preprocessing is a primer on the best data cleaning and preprocessing techniques, written by an expert who's developed college-level courses on data preprocessing and related subjects. With this book, you'll be equipped with the optimum data preprocessing techniques from multiple perspectives, ensuring that you get the best possible insights from your data. You'll learn about different technical and analytical aspects of data preprocessing - data collection, data cleaning, data integration, data reduction, and data transformation - and get to grips with implementing them using the open source Python programming environment. The hands-on examples and easy-to-follow chapters will help you gain a comprehensive articulation of data preprocessing, its whys and hows, and identify opportunities where data analytics could lead to more effective decision making. As you progress through the chapters, you'll also understand the role of data management systems and technologies for effective analytics and how to use APIs to pull data. By the end of this Python data preprocessing book, you'll be able to use Python to read, manipulate, and analyze data; perform data cleaning, integration, reduction, and transformation techniques, and handle outliers or missing values to effectively prepare data for analytic tools.What you will learnUse Python to perform analytics functions on your dataUnderstand the role of databases and how to effectively pull data from databasesPerform data preprocessing steps defined by your analytics goalsRecognize and resolve data integration challengesIdentify the need for data reduction and execute itDetect opportunities to improve analytics with data transformationWho this book is forThis book is for junior and senior data analysts, business intelligence professionals, engineering undergraduates, and data enthusiasts looking to perform preprocessing and data cleaning on large amounts of data. You don't need any prior experience with data preprocessing to get started with this book. However, basic programming skills, such as working with variables, conditionals, and loops, along with beginner-level knowledge of Python and simple analytics experience, are a prerequisite.

Paperback. Condition: New. Get your raw data cleaned up and ready for processing to design better data analytic solutionsKey FeaturesDevelop the skills to perform data cleaning, data integration, data reduction, and data transformationMake the most of your raw data with powerful data transformation and massaging techniquesPerform thorough data cleaning, including dealing with missing values and outliersBook DescriptionHands-On Data Preprocessing is a primer on the best data cleaning and preprocessing techniques, written by an expert who's developed college-level courses on data preprocessing and related subjects. With this book, you'll be equipped with the optimum data preprocessing techniques from multiple perspectives, ensuring that you get the best possible insights from your data. You'll learn about different technical and analytical aspects of data preprocessing - data collection, data cleaning, data integration, data reduction, and data transformation - and get to grips with implementing them using the open source Python programming environment. The hands-on examples and easy-to-follow chapters will help you gain a comprehensive articulation of data preprocessing, its whys and hows, and identify opportunities where data analytics could lead to more effective decision making. As you progress through the chapters, you'll also understand the role of data management systems and technologies for effective analytics and how to use APIs to pull data. By the end of this Python data preprocessing book, you'll be able to use Python to read, manipulate, and analyze data; perform data cleaning, integration, reduction, and transformation techniques, and handle outliers or missing values to effectively prepare data for analytic tools.What you will learnUse Python to perform analytics functions on your dataUnderstand the role of databases and how to effectively pull data from databasesPerform data preprocessing steps defined by your analytics goalsRecognize and resolve data integration challengesIdentify the need for data reduction and execute itDetect opportunities to improve analytics with data transformationWho this book is forThis book is for junior and senior data analysts, business intelligence professionals, engineering undergraduates, and data enthusiasts looking to perform preprocessing and data cleaning on large amounts of data. You don't need any prior experience with data preprocessing to get started with this book. However, basic programming skills, such as working with variables, conditionals, and loops, along with beginner-level knowledge of Python and simple analytics experience, are a prerequisite.

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PAP. Condition: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

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! This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

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Taschenbuch. Condition: Neu. Hands-On Data Preprocessing in Python | Learn how to effectively prepare data for successful data analytics | Roy Jafari | Taschenbuch | Kartoniert / Broschiert | Englisch | 2022 | Packt Publishing | EAN 9781801072137 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand.

Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - This book will make the link between data cleaning and preprocessing to help you design effective data analytic solutions Key Features:Develop the skills to perform data cleaning, data integration, data reduction, and data transformation Get ready to make the most of your data with powerful data transformation and massaging techniques Perform thorough data cleaning, such as dealing with missing values and outliers Book Description: Data preprocessing is the first step in data visualization, data analytics, and machine learning, where data is prepared for analytics functions to get the best possible insights. Around 90% of the time spent on data analytics, data visualization, and machine learning projects is dedicated to performing data preprocessing. This book will equip you with the optimum data preprocessing techniques from multiple perspectives. You'll learn about different technical and analytical aspects of data preprocessing - data collection, data cleaning, data integration, data reduction, and data transformation - and get to grips with implementing them using the open source Python programming environment. This book will provide a comprehensive articulation of data preprocessing, its whys and hows, and help you identify opportunities where data analytics could lead to more effective decision making. It also demonstrates the role of data management systems and technologies for effective analytics and how to use APIs to pull data. By the end of this Python data preprocessing book, you'll be able to use Python to read, manipulate, and analyze data; perform data cleaning, integration, reduction, and transformation techniques; and handle outliers or missing values to effectively prepare data for analytic tools. What You Will Learn:Use Python to perform analytics functions on your data Understand the role of databases and how to effectively pull data from databases Perform data preprocessing steps defined by your analytics goals Recognize and resolve data integration challenges Identify the need for data reduction and execute it Detect opportunities to improve analytics with data transformation Who this book is for: Junior and senior data analysts, business intelligence professionals, engineering undergraduates, and data enthusiasts looking to perform preprocessing and data cleaning on large amounts of data will find this book useful. Basic programming skills, such as working with variables, conditionals, and loops, along with beginner-level knowledge of Python and simple analytics experience, are assumed.