Creating good data : a guide to dataset structure and data representation
著者
書誌事項
Creating good data : a guide to dataset structure and data representation
（Books for professionals by professionals）
Apress, c2020
- : pbk
大学図書館所蔵 全1件
注記
Includes bibliographical references and index
内容説明・目次
内容説明
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 Learn
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
Who This Book Is For
Researchers 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.
目次
Introduction
Goal: The problem of dataset cleaning and why better design is needed
Who this book is for
Chapter 1: Basic Data Types
Goal: understanding data types
Nominal, ordinal, interval, ratio, other
How/why to choose specific representations
Chapter 2: Planning Your Data Collection
Goal: preventive action, avoiding data creation errors
Anticipating your required analysis
The goals of descriptive statistics and visualizations
The goals of relationship statistics and visualizations
Independent and dependent variables
Chapter 3: Dataset Structures
Goal: Understanding how to structure/store data
Types of datasets
.csv, SQL, Excel, Web, JSON,
Sharing data (open formats)
Managing datasets
Chapter 4: Data Collection Issues
Goal: Understanding how to collect data
Understand and avoid Bias
Sampling
Chapter 5: Examples and Use Cases
Goal: Illustrate good & not so good datasets
Chapter 6: Tools for Dataset Cleaning
Goal: still need some data cleanup? here's some help
Data cleaning using R, Python, commercial tools (e.g., Tableau)
Annotated References
Goal: include helpful data design and cleaning references
「Nielsen BookData」 より