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
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.
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 Analytics