Isbn: 9781484261026 - creating good data: a guide to dataset structure and data representation (24 resultados)

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Paperback. Condición: New. 1st ed. Create good data from the start, rather than fixing it after it is collected. By following the guidelines in this book, you will be able to conduct more effective analyses and produce timely presentations of research data.Data analysts are often presented with datasets for exploration and study that are poorly designed, leading to difficulties in interpretation and to delays in producing meaningful results.  Much data analytics training focuses on how to clean and transform datasets before serious analyses can even be started. Inappropriate or confusing representations, unit of measurement choices, coding errors, missing values, outliers, etc., can be avoided by using good dataset design and by understanding how data types determine the kinds of analyses which can be performed.This book discusses the principles and best practices of dataset creation, and covers basic data types and their related appropriate statistics and visualizations. A key focus of the book is why certain data types are chosen for representing concepts and measurements, in contrast to the typical discussions of how to analyze a specific data type once it has been selected.What You Will LearnBe aware of the principles of creating and collecting dataKnow the basic data types and representationsSelect data types, anticipating analysis goalsUnderstand dataset structures and practices for analyzing and sharingBe guided by examples and use cases (good and bad)Use cleaning tools and methods to create good dataWho This Book Is ForResearchers who design studies and collect data and subsequently conduct and report the results of their analyses can use the best practices in this book to produce better descriptions and interpretations of their work. In addition, data analysts who explore and explain data of other researchers will be able to create better datasets.…

Paperback. Condición: New. 1st ed. Create good data from the start, rather than fixing it after it is collected. By following the guidelines in this book, you will be able to conduct more effective analyses and produce timely presentations of research data.Data analysts are often presented with datasets for exploration and study that are poorly designed, leading to difficulties in interpretation and to delays in producing meaningful results.  Much data analytics training focuses on how to clean and transform datasets before serious analyses can even be started. Inappropriate or confusing representations, unit of measurement choices, coding errors, missing values, outliers, etc., can be avoided by using good dataset design and by understanding how data types determine the kinds of analyses which can be performed.This book discusses the principles and best practices of dataset creation, and covers basic data types and their related appropriate statistics and visualizations. A key focus of the book is why certain data types are chosen for representing concepts and measurements, in contrast to the typical discussions of how to analyze a specific data type once it has been selected.What You Will LearnBe aware of the principles of creating and collecting dataKnow the basic data types and representationsSelect data types, anticipating analysis goalsUnderstand dataset structures and practices for analyzing and sharingBe guided by examples and use cases (good and bad)Use cleaning tools and methods to create good dataWho This Book Is ForResearchers who design studies and collect data and subsequently conduct and report the results of their analyses can use the best practices in this book to produce better descriptions and interpretations of their work. In addition, data analysts who explore and explain data of other researchers will be able to create better datasets.…

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Paperback. Condición: New. 1st ed. Create good data from the start, rather than fixing it after it is collected. By following the guidelines in this book, you will be able to conduct more effective analyses and produce timely presentations of research data.Data analysts are often presented with datasets for exploration and study that are poorly designed, leading to difficulties in interpretation and to delays in producing meaningful results.  Much data analytics training focuses on how to clean and transform datasets before serious analyses can even be started. Inappropriate or confusing representations, unit of measurement choices, coding errors, missing values, outliers, etc., can be avoided by using good dataset design and by understanding how data types determine the kinds of analyses which can be performed.This book discusses the principles and best practices of dataset creation, and covers basic data types and their related appropriate statistics and visualizations. A key focus of the book is why certain data types are chosen for representing concepts and measurements, in contrast to the typical discussions of how to analyze a specific data type once it has been selected.What You Will LearnBe aware of the principles of creating and collecting dataKnow the basic data types and representationsSelect data types, anticipating analysis goalsUnderstand dataset structures and practices for analyzing and sharingBe guided by examples and use cases (good and bad)Use cleaning tools and methods to create good dataWho This Book Is ForResearchers who design studies and collect data and subsequently conduct and report the results of their analyses can use the best practices in this book to produce better descriptions and interpretations of their work. In addition, data analysts who explore and explain data of other researchers will be able to create better datasets.…

Taschenbuch. Condición: Neu. Creating Good Data | A Guide to Dataset Structure and Data Representation | Harry J. Foxwell | Taschenbuch | xv | Englisch | 2020 | Apress | EAN 9781484261026 | Verantwortliche Person für die EU: APress in Springer Science + Business Media, Heidelberger Platz 3, 14197 Berlin, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.…

Paperback. Condición: New. 1st ed. Create good data from the start, rather than fixing it after it is collected. By following the guidelines in this book, you will be able to conduct more effective analyses and produce timely presentations of research data.Data analysts are often presented with datasets for exploration and study that are poorly designed, leading to difficulties in interpretation and to delays in producing meaningful results.  Much data analytics training focuses on how to clean and transform datasets before serious analyses can even be started. Inappropriate or confusing representations, unit of measurement choices, coding errors, missing values, outliers, etc., can be avoided by using good dataset design and by understanding how data types determine the kinds of analyses which can be performed.This book discusses the principles and best practices of dataset creation, and covers basic data types and their related appropriate statistics and visualizations. A key focus of the book is why certain data types are chosen for representing concepts and measurements, in contrast to the typical discussions of how to analyze a specific data type once it has been selected.What You Will LearnBe aware of the principles of creating and collecting dataKnow the basic data types and representationsSelect data types, anticipating analysis goalsUnderstand dataset structures and practices for analyzing and sharingBe guided by examples and use cases (good and bad)Use cleaning tools and methods to create good dataWho This Book Is ForResearchers who design studies and collect data and subsequently conduct and report the results of their analyses can use the best practices in this book to produce better descriptions and interpretations of their work. In addition, data analysts who explore and explain data of other researchers will be able to create better datasets.…

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Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Create good datafrom the start, rather than fixing it afterit is collected. By following the guidelines in this book, you will be able to conduct more effective analyses and produce timely presentations of research data.Data analysts are often presented with datasets for exploration and study that are poorly designed, leading to difficulties in interpretation and to delays in producing meaningful results. Much data analytics training focuses on how to clean and transform datasets before serious analyses can even be started. Inappropriate or confusing representations, unit of measurement choices, coding errors, missing values, outliers, etc., can be avoided by using good dataset design and by understanding how data types determine the kinds of analyses which can be performed.This book discusses the principles and best practices of dataset creation, and covers basic data types and their related appropriate statistics and visualizations. A key focus of the book is whycertain data types are chosen for representing concepts and measurements, in contrast to the typical discussions of howto analyze a specific data type once it has been selected.What You Will LearnBe aware of the principles of creating and collecting dataKnow the basic data types and representationsSelect data types, anticipating analysis goalsUnderstand dataset structures and practices for analyzing and sharingBe guided by examples and use cases (good and bad)Use cleaning tools and methods to create good dataWho This Book Is ForResearchers who design studies and collect data and subsequently conduct and report the results of their analyses can use the best practices in this book to produce better descriptions and interpretations of their work. In addition, data analysts who explore and explain data of other researchers will be able to create better datasets. 124 pp. Englisch.…

Condición: New. Print on Demand pp. 120.

Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Beginning-Intermediate user level|Shows you how to clearly represent measurements, quantities, and characteristics relevant to researchTeaches you how to avoid time-consuming data cleaning prior to analysisPermit clear and accurate statisti.…

Taschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Create good datafrom the start, rather than fixing it afterit is collected. By following the guidelines in this book, you will be able to conduct more effective analyses and produce timely presentations of research data.Data analysts are often presented with datasets for exploration and study that are poorly designed, leading to difficulties in interpretation and to delays in producing meaningful results. Much data analytics training focuses on how to clean and transform datasets before serious analyses can even be started. Inappropriate or confusing representations, unit of measurement choices, coding errors, missing values, outliers, etc., can be avoided by using good dataset design and by understanding how data types determine the kinds of analyses which can be performed.This book discusses the principles and best practices of dataset creation, and covers basic data types and their related appropriate statistics and visualizations. A key focus of the book is whycertain data types are chosen for representing concepts and measurements, in contrast to the typical discussions of howto analyze a specific data type once it has been selected.What You Will LearnBe aware of the principles of creating and collecting dataKnow the basic data types and representationsSelect data types, anticipating analysis goalsUnderstand dataset structures and practices for analyzing and sharingBe guided by examples and use cases (good and bad)Use cleaning tools and methods to create good dataWho This Book Is ForResearchers who design studies and collect data and subsequently conduct and report the results of their analyses can use the best practices in this book to produce better descriptions and interpretations of their work. In addition, data analysts who explore and explain data of other researchers will be able to create better datasets.…

Condición: New. PRINT ON DEMAND pp. 120.

Taschenbuch. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Create good data from the start, rather than fixing it after it is collected. By following the guidelines in this book, you will be able to conduct more effective analyses and produce timely presentations of research data.Data analysts are often presented with datasets for exploration and study that are poorly designed, leading to difficulties in interpretation and to delays in producing meaningful results. Much data analytics training focuses on how to clean and transform datasets before serious analyses can even be started. Inappropriate or confusing representations, unit of measurement choices, coding errors, missing values, outliers, etc., can be avoided by using good dataset design and by understanding how data types determine the kinds of analyses which can be performed.This book discusses the principles and best practices of dataset creation, and covers basic data types and their related appropriate statistics and visualizations. A key focus of the book is why certain data types are chosen for representing concepts and measurements, in contrast to the typical discussions of how to analyze a specific data type once it has been selected.What You Will LearnBe aware of the principles of creating and collecting dataKnow the basic data types and representationsSelect data types, anticipating analysis goalsUnderstand dataset structures and practices for analyzing and sharingBe guided by examples and use cases (good and bad)Use cleaning tools and methods to create good dataWho This Book Is ForResearchers who design studies and collect data and subsequently conduct and report the results of their analyses can use the best practices in this book to produce better descriptions and interpretations of their work. In addition, data analysts who explore and explain data of other researchers will be able to create better datasets.Springer-Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 124 pp. Englisch.…