Avoiding Data Pitfalls : How to Steer Clear of Common Blunders When Working With Data and Presenting Analysis and Visualizations.

Includes index.

Includes bibliographical references and index.

The seven types of data pitfalls -- Pitfall 1: epistemic errors -- Pitfall 2: technical trespasses -- Pitfall 3: mathematical miscues -- Pitfall 4: statistical slipups -- Pitfall 5: analytical aberrations -- Pitfall 6: graphical gaffes -- Pitfall 7: design dangers -- Conclusion.

"Avoiding Data Pitfalls is a useful resource that points out common data viz. mistakes so that users can avoid making them and notice them when they are made by others. Working with data is so common now, but the vast majority of "data workers" were trained in another technical field like engineering or science. Most were not explicitly taught how to successfully work with today's tools and the types of data at their disposal. This book will provide illustrative examples of common mistakes, first outlining how we often think about data and the "data-reality gap," before walking the reader through each step of successful data visualization, from calculating and analyzing data to eventually presenting it in a way that is both clear and effective. The author will detail common data viz. blunders like cluttered design and ineffective use of color so that the reader can differentiate between a poor presentation and something truly representative and useful"-- Provided by publisher.