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 thebook 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

Autorentext

Harry J. Foxwell is a professor. He teaches graduate data analytics courses at George Mason University in the department of Information Sciences and Technology and he designed the data analytics curricula for his university courses. He draws on his decades of experience as Principal System Engineer for Oracle and for other major IT companies to help his students understand the concepts, tools, and practices of big data projects. He is co-author of several books on operating systems administration. He is a US Army combat veteran, having served in Vietnam as a Platoon Sergeant in the First Infantry Division. He lives in Fairfax, Virginia with his wife Eileen and two bothersome cats.

Klappentext
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

Inhalt

Introduction

Goal: The problem of dataset cleaning and why better design is needed

Who this book is for

Chapter 1: Basic D**ata T**ypes

Goal: understanding data types

Nominal, ordinal, interval, ratio, other

How/why to choose specific representations

Chapter 2: Planning Y**our 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 S**tructures**

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 I**ssues**

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 D**ataset C**leaning

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