Free Ebook #MakeoverMonday: Improving How We Visualize and Analyze Data, One Chart at a Time
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#MakeoverMonday: Improving How We Visualize and Analyze Data, One Chart at a Time
Free Ebook #MakeoverMonday: Improving How We Visualize and Analyze Data, One Chart at a Time
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From the Back Cover
Praise for #MakeoverMonday "We humans can't learn alone or only by studying rules and principles. We learn in collaboration — copying and getting feedback from others — and by practicing, to grasp how principles apply to the real world, and when it's appropriate to tweak them or even break them. This book, the product of the Makeover Monday collaborative initiative, exemplifies this understanding of how learning really works. If you've already read some classics in the literature about data visualization, the next step is to grab this book and peruse its many before-and-after examples. You'll love some, dislike others, and even loathe a few, but in the process of mulling over all them you'll become a better visualization designer." —Alberto Cairo, Knight Chair in Visual Journalism at the University of Miami, and author of The Truthful Art: Data, Charts, and Maps for Communication "Visualizing data effectively is something you learn through practicing: making something, getting feedback, and iterating to refine and improve. The weekly #MakeoverMonday project has been a place for many people to do exactly that. With #MakeoverMonday: Improving How We Visualize and Analyze Data, One Chart at a Time, Andy and Eva have curated the thousands of visuals they've seen into a guide packed with lessons, tips, anecdotes, and examples. If you work with data, you'll appreciate the varied and creative approaches, learn mistakes to avoid, and be inspired to do your own practicing to take your data visualization game to the next level!" —Cole Nussbaumer Knaflic, author of storytelling with data "#MakeoverMonday grew from a weekly blog series into a worldwide social data project, with some of the best talent in the data visualization community participating each week. This book compiles years of learning and hundreds of visualizations from that project into a practical guide that will prove essential for anyone working with data." —Jeffrey A. Shaffer, Co-author of The Big Book of Dashboards, Adjunct Professor teaching Data Visualization at the University of Cincinnati "Real-word case studies? Captivating visualizations? Practical career tips? This book has it all. Phenomenal." —Kirill Eremenko, CEO, SuperDataScience
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About the Author
ANDY KRIEBEL is Head Coach at The Information Lab Data School and a member of the Tableau Zen Master Hall of Fame. The Makeover Monday series originated from his blog at vizwiz.com, where he shares data visualization tips, tricks, and best practices. EVA MURRAY is the Head of Business Intelligence at Exasol, a Tableau Ambassador, and a 2018 Tableau Zen Master. She has co-hosted Makeover Monday since 2017, and blogs about Tableau, travel, and triathlon at trimydata.com.
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Product details
Paperback: 496 pages
Publisher: Wiley; 1 edition (October 9, 2018)
Language: English
ISBN-10: 9781119510772
ISBN-13: 978-1119510772
ASIN: 1119510775
Product Dimensions:
7.3 x 1.2 x 9.2 inches
Shipping Weight: 2.5 pounds (View shipping rates and policies)
Average Customer Review:
4.7 out of 5 stars
10 customer reviews
Amazon Best Sellers Rank:
#67,180 in Books (See Top 100 in Books)
Through practice, patience and an extreme sense of giving, Andy and Eva have grown Makeover Monday in to a tidal wave of educational value. The lessons learned are captured beautifully in this book and it is a wonderful resource that will become invaluable for you are a resource.
This book is a powerful toll to boost your data visualization skills. With hundreds of examples taken from an online social community of data practitioners, MakeoverMonday (the book) makes tacit the knowledge that Andy and Eva carries on their vast experience in data visualization. It is a pleasure to be part of this community and to be able to help communicating data better, one viz at at time :)
Do you need to learn the best data viz design practices and need to learn them fast? Do you need to be inspired on how to build a framework to practice your design skills regularly so that your work keeps improving? Then this book is for you. Every chapter in this book is easy to read and packed with practical advice on how to communicate data insights effectively. Andy Kriebel and Eva Murray have written a masterpiece and as a bonus have added a ton of captivating viz examples to check out.
What did I like about the product?It was as advertised.
a must read for anyone in this field, or interested in analytics careers
Over the last decade or so there has been an explosion in publishingbooks on visualization of data. What does this book offer in a crowdedmarket that is different or better?If the title is puzzling, that is easy to explain. The book has grownout of a website (Amazon protocol forbids giving a URL, but Google worksas usual) featuring a weekly cycle. Datasets are posted, visualizationsare invited, and then discussed, and then round again. Here I focusentirely on the book as an independent publication. If that seemsunfair, or not what you seek, then please hope for other reviews. Ifyou are familiar already with the website, you are well placed to readthe book and write your own review. As I submit this, there is a commonAmazon phenomenon, some very brief ecstatic reviews lacking any detail.The book is free of precise internet references for the datasets used orthe graphics shown, so it does not seem that readers are stronglyexpected to make use of the website.The target readership appears to be mainly professionals in designfields, in early or mid-career, who already have as their main or solework preparing visualizations for others, either for colleagues or forexternal audiences. Such people are already presumed proficient inTableau or some other broadly similar software.Turn and turn about, there is no detail here on how to use any software,so how much would the book offer say users of spreadsheets, statistical,or scientific software? Not quite the same question: how much might itappeal to say students, researchers, or teachers in science or socialscience, or to those in business or government or other institutions?Such people may need to develop graphics for their data. Graphics isjust part of what they do, but sometimes a very important part. Theshort answer to both questions is not so much.Contrary to a suggestion on p.29, I cannot see that this is at allsuitable for beginners, as what is written presumes previousintroductions to visualization. Conversely, and this gets very odd,much of the writing seems based on the idea that readers learned littlein high school or university courses and are lacking in personal andsocial confidence. That seems to underlie numerous little motivationalriffs. Here is a sample:"if you want to be great, you need patience, dedication and practice"(p.30)"By taking your time, slowing down, and thinking through the analysis,you will create better work" (p.47)"Paying attention to all characteristics of the data you are workingwith and how it is presented is critical for success" (p.159)"Being great at anything does not come easy; the work you put in will beworth it, though" (p.190)"If you provide feedback to someone who has not asked for it, it ispossible that the recipient will find it offensive and might questionyour motive for providing feedback." (p.257)"What is important is that you continue to develop your communicationskills as well as your technical skills." (p.263)Advising against unsolicited feedback would rule out most useful Amazonreviews. What else to say? People and cultures are different. Sending inbug reports is part of good citizenship in many programming communities.Maintaining public errata pages is a sure sign showing that authors careabout correctness. Responding to specific criticism with silence orhostility indicates, but only after the fact, who did not deserve areviewer's time and effort.No doubt such advice is all sincere and well meant. But if you just wanta book on graphics, not yet another book on life skills or careerdevelopment, you will need to skim and skip such homilies, which you mayfind irritating or pointless.There is a great variety of graph examples in this book, variety insubject matter, simplicity and style. But to me the over-ridingimpression was of a jumble.The dual interest (see the subtitle) in visualization and analysis ofdata is unevenly supported. The centre of gravity here is much nearer toinformation or presentation graphics than to statistical or scientificgraphics. That is fine in broad terms, yet in detail the authors' stanceon statistical methods is curious and contradictory. They feel a need togive explanations of mean and median and to explain (twice over atpp.80ff, 92ff: why was that?) that averages calculated for coarsercategories from finer data usually need weighting. Yet slightly moreadvanced material (ordinal variable, p.100; R-square, p.121; 25% and 75%percentiles, p.123) is assumed familiar.Readers knowing some statistics need to watch out. Variance is sometimesused to mean variation, and sometimes deviation from mean, but not inits usual statistical sense. Significant is just a filler word, meaningperhaps major or striking, and also is not used in its usual technicalsense. Nor is parameter, although I don't understand what it does meanhere. Summarizing is used to mean summing or totaling, although that ispoor word choice. Skewed here means biased, and does not connoteskewness of distribution. Histogram is, to statistical people, notanother word for bar chart. Metric is employed as a catch-all for anyquantitative variable or summary.What did I like? There are many good examples of bar and line charts,but those should be familiar from early education. Less usually, dotplots showing distributions and Cleveland dot charts showing amounts arealso well exemplified. So are tile maps, in which each small area isrepresented by a tile of identical size and shape. More should have beenmade of cycle plots (one clear example on p.130).Two valuable tactics recur in examples. First, highlighting just oneseries or subset, with the rest as backdrop, usually in a subdued colorsuch as gray. Some good examples: pp.44, 81, 103, 142, 203, 204, 253.Second, contrasting two groups in colors such as orange and blue. Somegood examples: pp.111f, 202, 255, 292, 302, 349.In contrast, many of the graphs do not bear up well under criticalscrutiny. Here is a partial list of common problems.Graphs that may be impressive viewed on-line, but as reproduced in thebook are barely readable. Examples: pp.52, 180 (this on a page urgingthe importance of visibility!), 215, 275.Graphs or tables with categories in unhelpful alphabetical order.Howard Wainer parodied this choice long ago as Alabama First! Examples:pp.84ff, 93f, 166, 175, 248ff, 261f, 301, 303, 336, 354, 364, 396, 398,419.Axes that start at zero unnecessarily and so waste space. Examples:pp.150, 342.Graphs that cry out for logarithmic or other transformed scales. (Theyare not thought too technical for financial dailies or weeklies.)Examples: pp.20, 113f, 127f, 293, 359, 407, 416, 424f.Graphs needing but lacking a key or legend. Examples: pp.131ff, 310ff,324, 360, 390.Solid markers or point symbols (always circular blobs!). Open markerswork much better if there is overlap, and markers of different shapessuch as open circles and crosses do so too. If there is argument againstthis, we need to see it. Examples: pp. 39, 101, 275, 282, 330, 407, 416.Axes the wrong way round as compared with the statistical convention ofoutcome or response on the vertical axis. There can be good grounds forsubverting this convention, but I cannot see that they apply here to theexamples on pp.113 and 378.Misuse of circular and radial designs. Such designs can make much senseto show variation with (say) time of day, time of year, or mapdirection. None of the examples here (pp.222f, 354ff, 361f) convince inthat respect.Graphs marred by lack of proof-reading. The authors warn sternly of theneed to check for punctuation and other typos and to show units ofmeasurement. They should have heeded their own advice. Thus graphs ofcarbon dioxide and ozone on pp.120ff variously omit units; show raw databut state that the average is shown; show "CO2" and so don't use asubscript for 2 oxygen atoms; confuse ozone and carbon dioxide. Thereare many other typos or garbled expressions on graphs: a few are"entreprenurial", "consecuent", and this on p.307: "Now, the migrants tomove from their home regions to more distant."Lack of care extends to the writing and copy editing. One person'sinformality is another person's immature style, so you may love what Ido not. To my taste juvenile vocabulary was often overdone (amazing,awesome, fantastic, incredible), as was management and creativity jargon(deliverables, impactful, insightful, influencers, stakeholders,time-boxing, showcasing, Big Ass Numbers). There's a word for"numerically literate", namely numerate (p.190). "versus" is pressedinto service as a all-purpose preposition or even conjunction, meaningsometimes against (that is standard), sometimes since ("versus 2011"),and sometimes compared with. Incidentally, if you generally prefer"compared with" to "compared to", you will be mightily disappointed. Anopportunity to distinguish clearly between "unique" and "distinct" isfluffed on p.107ff.A bigger problem than these small slips, some of which could beconsidered a matter of taste, is repetition. Many points are spun out atlength or in several places. Giving full credit to others' prior work isrightly emphasised, but three times over (pp.182f, 209, 220).This book doesn't try to be academic or scholarly. Even so, the standardof referencing is still poor, including mere name and date referencessuch as Cleveland, Dunn, and Terpenning, 1978 (p.129) or Edward Tufte,1983 (p.163) and various references to internet sources without URLs. Itis good to see mentions of Tufte, but he really didn't inventslopegraphs (p.338): the name may be his, but the idea is much older.Disappointment is the best word for my general reaction. There was anopportunity here to do something excitingly fresh and original using thework that many people put into the website: to show interesting datasetsand then to discuss the pluses and minuses of competing or complementarydesigns. Instead, we have a book no better than any others and onemarred by hurry and mess. Even if you're in the authors' targetreadership, look elsewhere, such as at the books of Isabel Meirelles,Cole Nussbaumer Knaflic, Jorge Camões, Alberto Cairo, or Scott Berinato.
This book is packed with real-world case studies and practical career tips for Data Scientists. Moreover, the visualizations are so interesting that even people who are not in Data Science love browsing through my copy of the book. A must have for an Analytics Professional!
I started participating MM Jan 2018 and just can't say enough good things about the experience and how much it has helped me improve in the last year. So when Andy and Eva announced that they were writing a book, I knew I must buy it! It's very well structured and written. It has all the lessons that Andy and Eva write weekly in the reviews and much more, big topics and minor details that would make a world of difference. It takes one through the steps and good practices of data analysis and visualization with a ton of examples. The order of the topics covered is really thoughtful and makes a lot of sense. They really hammered the message home that sound data analysis is paramount before any visualization. The examples included in the book are really good resource for inspiration. I also really liked the iterate to improve section and the recommended methodologies, very concrete and applicable. Overall, I highly recommend this book and being part of this community!
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December 23, 2010
