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.
Data analysts are often simply presented with datasets for exploration and study which are poorly designed, leading to difficulties in interpretation and to delays in producing usable results. In fact, some analysts report spending up to 80% of their time just getting data ready to be explored so that it can be effectively interpreted. And much data analytics training and published resources focus on how to clean and transform datasets before serious analyses can even begin. Inappropriate or confusing representations, unit of measurement choices, coding errors, missing values, outliers, and others can be avoided by using good data item selection, good dataset design and collection, and by understanding how data types determine the kinds of analyses that 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.
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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