Avoiding Data Pitfalls

How to Steer Clear of Common Blunders When Working with Data and Presenting Analysis and Visualizations

Häftad, Engelska, 2019

459 kr

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Avoid data blunders and create truly useful visualizations Avoiding Data Pitfalls is a reputation-saving handbook for those who work with data, designed to help you avoid the all-too-common blunders that occur in data analysis, visualization, and presentation. Plenty of data tools exist, along with plenty of books that tell you how to use them—but unless you truly understand how to work with data, each of these tools can ultimately mislead and cause costly mistakes. This book walks you step by step through the full data visualization process, from calculation and analysis through accurate, useful presentation. Common blunders are explored in depth to show you how they arise, how they have become so common, and how you can avoid them from the outset. Then and only then can you take advantage of the wealth of tools that are out there—in the hands of someone who knows what they're doing, the right tools can cut down on the time, labor, and myriad decisions that go into each and every data presentation. Workers in almost every industry are now commonly expected to effectively analyze and present data, even with little or no formal training. There are many pitfalls—some might say chasms—in the process, and no one wants to be the source of a data error that costs money or even lives. This book provides a full walk-through of the process to help you ensure a truly useful result. Delve into the "data-reality gap" that grows with our dependence on dataLearn how the right tools can streamline the visualization processAvoid common mistakes in data analysis, visualization, and presentationCreate and present clear, accurate, effective data visualizationsTo err is human, but in today's data-driven world, the stakes can be high and the mistakes costly. Don't rely on "catching" mistakes, avoid them from the outset with the expert instruction in Avoiding Data Pitfalls.

Produktinformation

Utgivningsdatum2019-12-12
Mått185 x 234 x 15 mm
Vikt567 g
FormatHäftad
SpråkEngelska
Antal sidor272
FörlagJohn Wiley & Sons Inc
ISBN9781119278160

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Affärsförhandlingar inom Ekonomi och ledarskap

BEN JONES is the Founder and CEO of Data Literacy, LLC, a company that's on a mission to help people speak the language of data. He's the author of Communicating Data with Tableau and 17 Key Traits of Data Literacy, and he also teaches data visualization at the University of Washington's Continuum College. With over 20 years of experience working as a mechanical engineer, a continuous improvement project leader and mentor, and a business intelligence marketer, Ben has learned a great deal about what to doand what not to dowhen working with data.

Preface ixChapter 1 The Seven Types of Data Pitfalls 1Seven Types of Data Pitfalls 3Pitfall 1: Epistemic Errors: How We Think About Data 3Pitfall 2: Technical Traps: How We Process Data 4Pitfall 3: Mathematical Miscues: How We Calculate Data 4Pitfall 4: Statistical Slipups: How We Compare Data 5Pitfall 5: Analytical Aberrations: How We Analyze Data 5Pitfall 6: Graphical Gaffes: How We Visualize Data 6Pitfall 7: Design Dangers: How We Dress up Data 6Avoiding the Seven Pitfalls 7“I’ve Fallen and I Can’t Get Up” 8Chapter 2 Pitfall 1: Epistemic Errors 11How We Think About Data 11Pitfall 1A: The Data-Reality Gap 12Pitfall 1B: All Too Human Data 24Pitfall 1C: Inconsistent Ratings 32Pitfall 1D: The Black Swan Pitfall 39Pitfall 1E: Falsifiability and the God Pitfall 43Avoiding the Swan Pitfall and the God Pitfall 44Chapter 3 Pitfall 2: Technical Trespasses 47How We Process Data 47Pitfall 2A: The Dirty Data Pitfall 48Pitfall 2B: Bad Blends and Joins 67Chapter 4 Pitfall 3: Mathematical Miscues 74How We Calculate Data 74Pitfall 3A: Aggravating Aggregations 75Pitfall 3B: Missing Values 83Pitfall 3C: Tripping on Totals 88Pitfall 3D: Preposterous Percents 93Pitfall 3E: Unmatching Units 102Chapter 5 Pitfall 4: Statistical Slipups 107How We Compare Data 107Pitfall 4A: Descriptive Debacles 109Pitfall 4B: Inferential Infernos 131Pitfall 4C: Slippery Sampling 136Pitfall 4D: Insensitivity to Sample Size 142Chapter 6 Pitfall 5: Analytical Aberrations 148How We Analyze Data 148Pitfall 5A: The Intuition/Analysis False Dichotomy 149Pitfall 5B: Exuberant Extrapolations 157Pitfall 5C: Ill-Advised Interpolations 163Pitfall 5D: Funky Forecasts 166Pitfall 5E: Moronic Measures 168Chapter 7 Pitfall 6: Graphical Gaffes 173How We Visualize Data 173Pitfall 6A: Challenging Charts 175Pitfall 6B: Data Dogmatism 202Pitfall 6C: The Optimize/Satisfice False Dichotomy 207Chapter 8 Pitfall 7: Design Dangers 212How We Dress up Data 212Pitfall 7A: Confusing Colors 214Pitfall 7B: Omitted Opportunities 222Pitfall 7C: Usability Uh-Ohs 227Chapter 9 Conclusion 237Avoiding Data Pitfalls Checklist 241The Pitfall of the Unheard Voice 243Index 247