國立高雄大學圖資館 |

Language: English

Back

Creating good dataa guide to dataset...

Foxwell, Harry J.

Creating good dataa guide to dataset structure and data representation /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Creating good databy 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.
Contained By:	Springer Nature eBook
Subject:	Data sets.
Online resource:	https://doi.org/10.1007/978-1-4842-6103-3
ISBN:	9781484261033$q(electronic bk.)

Creating good dataa 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$q(electronic bk.)

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

877092
Data sets.

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

Dewey Class. No.: 005.72

Creating good dataa guide to dataset structure and data representation /
LDR:02908nmm a2200325 a 4500 001 585844
003 DE-He213
005 20201231134201.0
006 m d
007 cr nn 008maaau
008 210323s2020 cau s 0 eng d
020 $a 9781484261033$q(electronic bk.)
020 $a 9781484261026$q(paper)
024 7 $a 10.1007/978-1-4842-6103-3 $2 doi
035 $a 978-1-4842-6103-3
040 $a GP $c GP
041 0 $a eng
050 4 $a QA76.9.D345 $b F69 2020
072 7 $a UN $2 bicssc
072 7 $a COM021000 $2 bisacsh
072 7 $a UN $2 thema
082 0 4 $a 005.72 $2 23
090 $a QA76.9.D345 $b F795 2020
100 1 $a Foxwell, Harry J. $3 349262
245 1 0 $a Creating good data $h [electronic resource] : $b a guide to dataset structure and data representation / $c by Harry J. Foxwell.
260 $a Berkeley, CA : $b Apress : $b Imprint: Apress, $c 2020.
300 $a xv, 105 p. : $b ill., digital ; $c 24 cm.
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.
650 0 $a Data sets. $3 877092
650 0 $a Electronic data processing $x Data preparation. $3 224687
650 0 $a Big data. $3 609582
650 1 4 $a Big Data. $3 760530
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer Nature eBook
856 4 0 $u https://doi.org/10.1007/978-1-4842-6103-3
950 $a Business and Management (SpringerNature-41169)