Actions for Avoiding data pitfalls : how to steer clear of common blunders when working with data and presenting analysis and visualizations

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Avoiding data pitfalls : how to steer clear of common blunders when working with data and presenting analysis and visualizations / Ben Jones

Author
Jones, Ben, 1978-
Published
Hoboken, New Jersey : John Wiley & Sons, Inc., [2020]
Physical Description
1 online resource (xii, 258 pages)
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Contents
Machine generated contents note: ch. 1 The Seven Types of Data Pitfalls -- Seven Types of Data Pitfalls -- Pitfall 1 Epistemic Errors: How We Think About Data -- Pitfall 2 Technical Traps: How We Process Data -- Pitfall 3 Mathematical Miscues: How We Calculate Data -- Pitfall 4 Statistical Slipups: How We Compare Data -- Pitfall 5 Analytical Aberrations: How We Analyze Data -- Pitfall 6 Graphical Gaffes: How We Visualize Data -- Pitfall 7 Design Dangers: How We Dress up Data -- Avoiding the Seven Pitfalls -- "I've Fallen and I Can't Get Up" -- ch. 2 Pitfall 1 Epistemic Errors -- How We Think About Data -- Pitfall 1A The Data-Reality Gap -- Pitfall 1B All Too Human Data -- Pitfall 1C Inconsistent Ratings -- Pitfall 1D The Black Swan Pitfall -- Pitfall 1E Falsifiability and the God Pitfall -- Avoiding the Swan Pitfall and the God Pitfall -- ch. 3 Pitfall 2 Technical Trespasses -- How We Process Data -- Pitfall 2A The Dirty Data Pitfall -- Pitfall 2B Bad Blends and Joins -- ch. 4 Pitfall 3 Mathematical Miscues -- How We Calculate Data -- Pitfall 3A Aggravating Aggregations -- Pitfall 3B Missing Values -- Pitfall 3C Tripping on Totals -- Pitfall 3D Preposterous Percents -- Pitfall 3E Unmatching Units -- ch. 5 Pitfall 4 Statistical Slipups -- How We Compare Data -- Pitfall 4A Descriptive Debacles -- Pitfall 4B Inferential Infernos -- Pitfall 4C Slippery Sampling -- Pitfall 4D Insensitivity to Sample Size -- ch. 6 Pitfall 5 Analytical Aberrations -- How We Analyze Data -- Pitfall 5A The Intuition/Analysis False Dichotomy -- Pitfall 5B Exuberant Extrapolations -- Pitfall 5C Ill-Advised Interpolations -- Pitfall 5D Funky Forecasts -- Pitfall 5E Moronic Measures -- ch. 7 Pitfall 6 Graphical Gaffes -- How We Visualize Data -- Pitfall 6A Challenging Charts -- Pitfall 6B Data Dogmatism -- Pitfall 6C The Optimize/Satisfice False Dichotomy -- ch. 8 Pitfall 7 Design Dangers -- How We Dress up Data -- Pitfall 7A Confusing Colors -- Pitfall 7B Omitted Opportunities -- Pitfall 7C Usability Uh-Ohs -- ch. 9 Conclusion -- Avoiding Data Pitfalls Checklist -- The Pitfall of the Unheard Voice.
Summary
"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"--
Subject(s)
ISBN
9781119278177 electronic book
1119278171 electronic book
9781119278191 electronic book
1119278198 electronic book
9781119278207 electronic book
1119278201 electronic book
9781119278160 paperback
1119278163 paperback
Bibliography Note
Includes bibliographical references and index.
View MARC record | catkey: 29247927