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

"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...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Jones, Ben, 1978- (Autor)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Hoboken, New Jersey : John Wiley & Sons, Inc., [2020]
Temas:
Acceso en línea:Texto completo (Requiere registro previo con correo institucional)

MARC

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100 1 |a Jones, Ben,  |d 1978-  |e author. 
245 1 0 |a Avoiding data pitfalls :  |b how to steer clear of common blunders when working with data and presenting analysis and visualizations /  |c Ben Jones. 
264 1 |a Hoboken, New Jersey :  |b John Wiley & Sons, Inc.,  |c [2020] 
300 |a 1 online resource (xii, 258 pages) :  |b illustrations, maps 
336 |a text  |b txt  |2 rdacontent 
337 |a computer  |b c  |2 rdamedia 
338 |a online resource  |b cr  |2 rdacarrier 
504 |a Includes bibliographical references and index. 
505 0 |a 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 abberations -- Pitfall 6: Graphical gaffes -- Pitfall 7: Design dangers. 
520 |a "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"--  |c Provided by publisher 
588 0 |a Online resource; title from digital title page (viewed on December 13, 2019). 
590 |a O'Reilly  |b O'Reilly Online Learning: Academic/Public Library Edition 
650 0 |a Information visualization. 
650 0 |a Quantitative research. 
650 6 |a Visualisation de l'information. 
650 6 |a Recherche quantitative. 
650 7 |a Information visualization  |2 fast 
650 7 |a Quantitative research  |2 fast 
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