PDF Ebook Effective Data Visualization: The Right Chart for the Right Data
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Effective Data Visualization: The Right Chart for the Right Data
PDF Ebook Effective Data Visualization: The Right Chart for the Right Data
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Product details
Paperback: 264 pages
Publisher: SAGE Publications, Inc; 1 edition (May 18, 2016)
Language: English
ISBN-10: 1506303056
ISBN-13: 978-1506303055
Product Dimensions:
7.2 x 0.5 x 9.2 inches
Shipping Weight: 15.5 ounces (View shipping rates and policies)
Average Customer Review:
4.5 out of 5 stars
55 customer reviews
Amazon Best Sellers Rank:
#71,940 in Books (See Top 100 in Books)
The author has a recent Ph.D. from Western Michigan University and runsher own consulting firm. This book on data presentation, her second, isfocused on elementary displays of data using MS Office. It joins anincreasingly crowded market and does not stand up well given someexcellent alternatives.If you have access to a copy and skim through, you may find examples ofunfamiliar graph types that could be useful. That's the most likelypositive use of this book I can imagine. In particular, I agree thatwhat the author calls 'dot plots' (Cleveland dot charts is another name)are often very helpful and should be used more frequently. Similarly,'slope graphs', an old idea under Tufte's new name, can be very good forshowing paired changes as labeled line segments.The book mentions 11 technical reviewers, all from universities. I haveto wonder whether they read the book, or perhaps more likely, how theyfeel about being named if their advice was ignored. Technically, thisbook is unreliable, and shows much evidence of the author's inexperienceand prejudices.Evergreen is negative about scatter plots and lays that on repeatedly:they are a 'complicated visual' (p.161), can be 'confusing to interpret'(p.165), and are suited for '[m]ore sophisticated groups' who have takenan 'advanced statistics course' (p.172). I come from a differenteducational system, evidently, in which scatter plots appear early inchildren's education and are a staple of every introductory statisticscourse. The author's main scatter plot example shows % of people ofcolor in various areas of New York City (y axis) versus number ofmilitary recruits per 100,000 residents (x axis). She repeatedly brushesoff the standard point that the axes would be better reversed (a pointalso poorly handled in her first book). This isn't just a cosmeticchoice: which variable is outcome (response) is crucial to thinkingabout such problems. Evergreen also fails to comment that the pattern ofscatter is strongly nonlinear, so the straight line fitted is absurd forthat reason, and indeed on other grounds too. Evidently, the authorneeded simpler examples that she could handle confidently.Similarly, Evergreen dislikes histograms which are 'not the sexiest'(p.140) and 'feel clunky to me' (p.145). These aren't serious comments,for all that she does continue with examples.The author is evidently no programmer (that's a comment, not acriticism) and focuses on how to get there step by step in MS Office(meaning MS Excel, mostly) using your mouse. Those sceptical of themerit of doing graphics at all within Excel will find their opinionsconfirmed by the dodges and fudges needed even to do some very simplethings. There are some unsystematic comments on using macros instead,which are mostly a distraction. There is little mention of the manyinternet resources available from more expert Excel users. There issome token R code on the author's website, which looks like a waste ofeffort given the mass of outstanding code already available.There are other technical mistakes that should have been caught byreviewers. There is reference to debate on whether bubble charts shouldbe sized by their diameter, radius or circumference (p.17), but there isno such debate. Any issue is over using areas or lengths, as any lengthbasis is equivalent to any other. 'Upper confidence interval' (p.25) isa slip for 'upper confidence limit'. Cluster analysis is not defined(even vaguely) by combining quantitative and qualitative data (p.183),although that is one of its applications.Evergreen broaches the difficulty of paraphrasing technical terms fornon-technical audiences, but some of her own suggestions fall far shortof acceptable. 'Statistically, there's a chance the actual score fallsin this range' is offered (p.26) as an explanation of confidenceinterval: that explanation is not just vague, it's completely vacuous.As other reviews note, no colors are used here except blue and grey. Aside-effect of that, which is not quite inevitable, is that many graphshere depend heavily on minor differences between shades of those colors.It is hard to like this book if, as I did, you find its written styleslangy and immature. Sample vocabulary includes amazing, awesome, baby,cool, cute, freak, geek (often), heaps, hush-hush, incredibly, kid,kinda, loads, nerd (often), ninja (oh so often), pow, rad, rock star(ditto), sorta, stellar, Super Academic, super bad, super cool, supercritical, super famous, super hero, super honest, super interested,super weird, um, whew, wimpy, wow, yay, yeah, yep, yikes, and yowza.(Also, if it's news: using MS Excel for presentation graphics doesn'tmake you a geek.)Combined with that style come careless editing and proof-reading. Thereis repeated confusion between it's and its and bizarre typos on thegraphs themselves, such as camradarie, kindney, ful (for 'flu') andindividulized. There is also confusion between i.e. and e.g., affect andeffect, axis and axes, rectangle and oblong, and disbursed anddispersed. Names of key figures in the field (Jorge Cameos, Mike Bostok)are mangled. No editor should have let through `the bottom of Indiana'and `the top of Kentucky' (p.229): the words being reached for might besouthern and northern, or something more precise.In total, writing and editing were poorly done. I evidently don't knowhow blame is to be shared between the author and the publisher.I do find Sage's technical books a very mixed bunch.Better books?Berinato: Good Charts: The HBR Guide to Making Smarter, More PersuasiveData Visualizations.https://www.amazon.com/Good-Charts-Smarter-Persuasive-Visualizations/dp/1633690709Cairo: The Truthful Art: Data, Charts, and Maps for Communication.http://www.amazon.com/Truthful-Art-Data-Charts-Communication/dp/0321934075Camões: Data at Work: Best Practices for Creating Effective Charts andInformation Graphics in Microsoft Excel.http://www.amazon.com/Data-Work-practices-effective-information/dp/0134268636Knaflic: Storytelling with Data: A Data Visualization Guide for BusinessProfessionals.http://www.amazon.com/Storytelling-Data-Visualization-Business-Professionals/dp/1119002257Robbins: Creating More Effective Graphs.http://www.amazon.com/Creating-Effective-Graphs-Naomi-Robbins/dp/047127402XCamões is best of these if you need an Excel-based book. Robbins has thegreatest depth of analysis.
Maybe they were shooting for the minimalist homogenized design for this book which had shocking few examples of what I'd call great data visualization, this was like the chart picker in PowerPoint - very few real-world data visualization examples that pertained to a professional graphic artist/ presentation specialist who dies data vizualuzation constantly. I wanted to learn something from this book - a lot as a matter of fact, but that never happened and I shipped it back for a refund.
This text has been indispensable for me over the last year. I've learned so much about how to present data in a more visually appealing way (conceptually) and how to make that happen in Excel (practically). People in my department regularly come to me for help or to consult on reports and presentations, and my department director was so impressed with one particular report redesign, she called me "a numbers artist".I could barely work Excel well enough to do basic default charts back then, now Stephanie Evergreen has helped me develop a niche in my research group and she can help you too. Skeptical? Check out the photos I've attached on how we used to show data in charts and how we do it now.Bottom line: Buy it!
Ninja techniques, indeed.... WELL explained / developed / taught !!!Actually, I think that the principal thing that it does is to get YOUR head / mind / thinking RIGHT -- & to SEE / FIGURE OUT what SHOULD (REALLY) be shown in the situation -- with the data available...(Only) THEN, do the (amazing) ninja techniques apply -- because, at the CORE, Excel (graphs) are just a 'tool' -- which, ONLY follow the 'commands' of the 'interpreter'... Excel is both 'smart' -- & 'DUMB' -- at once -- but ONLY come to LIFE in the hands / head of a SKILLED 'interpreter' / user... Can't replace (or create) INTELLIGENCE -- & a WELL-THOUGHT -- THROUGH -- message / point...
I am a big fan of Stephanie Evergreen and have both of her books. I must say that I was hoping this second book would have a bit more information about data visualization for Qualitative data. Only chapter 9 has this focus although one can pull bits and pieces from other chapters and the first book. Stephanie, if you are reading this, please put something out there for us Qualitative reachers in program eval. We need your help!
You will want this book if you want a reference book that takes theory and applies it in practical terms. While it focuses on Excel as its application of choice, it also simplifies the concepts about chart choices and data matching in a way that will get you up and running in a short period. I highly recommend this to anyone starting out, wants a good reference book, or needs the least amount of details to achieve good charts for most professional presentations. This is a fundamental "nuts and bolts" type book.
Excellent! At one shot we learn about how to represent data and a beautiful crash course in Excel charts. As someone needing to report on metrics and KPIs regularly this book helped me to do my job better and I could speed up on the go.
Stephanie does a fantastic job outlining an effective way to make data look good and make a point. I found the examples relevant and the steps to create simple, yet effective data visualizations in Excel well done. A good book to have in the library.
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