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Creating good data = a guide to data...

Foxwell, Harry J.

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Creating good data = a guide to dataset structure and data representation /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Creating good data/ by Harry J. Foxwell.
Reminder of title:	a guide to dataset structure and data representation /
Author:	Foxwell, Harry J.
Published:	Berkeley, CA :Apress : : 2020.,
Description:	xv, 105 p. :ill., digital ;24 cm.
[NT 15003449]:	Chapter 1: The Need for Good Data -- Chapter 2: Basic Data Types and When to Use Them -- Chapter 3: Representing Quantitative Data -- Chapter 4: Planning Your Data Collection and Analysis -- Chapter 5: Good Datasets -- Chapter 6: Good Data Collection -- Chapter 7: Dataset Examples and Use Cases -- Chapter 8: Cleaning your Data -- Chapter 9: Good Data Anayltics -- Appendix A: Recommended Reading.
Contained By:	Springer Nature eBook
Subject:	Data sets. -
Online resource:	https://doi.org/10.1007/978-1-4842-6103-3
ISBN:	9781484261033

Creating good data = a guide to dataset structure and data representation /
Foxwell, Harry J.

Creating good dataa guide to dataset structure and data representation /[electronic resource] :by Harry J. Foxwell. - Berkeley, CA :Apress :2020. - xv, 105 p. :ill., digital ;24 cm.

Chapter 1: The Need for Good Data -- Chapter 2: Basic Data Types and When to Use Them -- Chapter 3: Representing Quantitative Data -- Chapter 4: Planning Your Data Collection and Analysis -- Chapter 5: Good Datasets -- Chapter 6: Good Data Collection -- Chapter 7: Dataset Examples and Use Cases -- Chapter 8: Cleaning your Data -- Chapter 9: Good Data Anayltics -- Appendix A: Recommended Reading.

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. You will: Be aware of the principles of creating and collecting data Know the basic data types and representations Select data types, anticipating analysis goals Understand dataset structures and practices for analyzing and sharing Be guided by examples and use cases (good and bad) Use cleaning tools and methods to create good data.

ISBN: 9781484261033

Standard No.: 10.1007/978-1-4842-6103-3doiSubjects--Topical Terms:

3490294
Data sets.

LC Class. No.: QA76.9.D345 / F69 2020

Dewey Class. No.: 005.72

Creating good data = a guide to dataset structure and data representation /
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505 0 $a Chapter 1: The Need for Good Data -- Chapter 2: Basic Data Types and When to Use Them -- Chapter 3: Representing Quantitative Data -- Chapter 4: Planning Your Data Collection and Analysis -- Chapter 5: Good Datasets -- Chapter 6: Good Data Collection -- Chapter 7: Dataset Examples and Use Cases -- Chapter 8: Cleaning your Data -- Chapter 9: Good Data Anayltics -- Appendix A: Recommended Reading.
520 $a 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. You will: Be aware of the principles of creating and collecting data Know the basic data types and representations Select data types, anticipating analysis goals Understand dataset structures and practices for analyzing and sharing Be guided by examples and use cases (good and bad) Use cleaning tools and methods to create good data.
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