Fundamentals of Data Visualization

According to the author, “data visualization is part art and part science.” And, in my view, the author excels at both.What I particularly love about the book is the author's classification of visualizations: ugly, bad, and wrong figures. Pure genius!The book is probably most useful for Data Scientists using R, Python, Power BI, and Tableau.A few personal highlights:- Page 29: Besides from Tableau, I’ve rarely seen so much attention to color-coding continuous and categorical data. I love it.- Page 53: If you’re in Data Science or Machine Learning, you will appreciate the Titanic samples.- Page 61: The importance of not relying on default bins for visualizing a single distribution.- Page 96: Many don’t like pie charts. I love the author's example where a pie chart might have been superior to other visualizations.- Page 112: How visualizing nested proportions (I really don’t like the “sunburst”) can be improved.- Page 233: Common pitfalls in color use.- Page 267: EVERY FIGURE NEEDS A TITLE. Something I still do wrong.- Page 297: Why line drawings have been popular. Here, the author shows a strong historical understanding of visualizations. What more can you ask for?- Page 338: Something I have to remind myself constantly: “never assume your audience can rapidly process visual displays.”- Page 343: how to strike the balance between making a visualization memorable and clear.The book is clearly among my top 10 Data Science books.Franco

Wilke’s “Fundamentals of Data Visualization” fills an important niche: a manual for the professional data scientist who wants to convey the essence of data succinctly, accurately, and in an aesthetically pleasing way. Wilke shows that this is (perhaps surprisingly) difficult, and he does this by showing both good and bad examples of data visualization in order to make this point. The book really attempts to build up the reader’s intuition about what makes a good visualization chapter by chapter. By the end, the lessons learned have become so obvious that when going back to earlier chapters, a single glance at the figure is enough to remind you about what is good (or bad) in the example given. This is not a book that gives programming examples that tell the reader how to achieve a particular figure, and I think this is intentional. The emphasis is on understanding the principles of data visualization, not on supplying a hack for the next figure. (But for those who want to recreate a figure, Wilke provides the entire source code for the book on a web site). All in all, this is probably the best book on data visualization for the practicing scientist out there. The prose is clear and concise, the principles behind the choices he makes are clearly laid out, and the figures are clean and free of clutter. My only gripe is that the colors in the book appear to be somewhat flat and less intense than in the electronic version. Hopefully this can be remedied in the future.

Un muy lindo libro a colores, pero lleno de ejemplos muy simples. Lo he devuelto.

Livre très visuel, avec des conseils très pragmatiques.De nombreux graphiques illustrent parfaitement les concepts théoriques.

This is a practical guide to making data visualisations that convey information to a wide variety of audiences.I liked the focus on the generated images, rather than getting bogged down with coding details. The code is available on github if you want it, but the place for a book like this is to discuss the concepts and purpose of data visualisation such as scale, colour and representation.I feel this book will have a significant longevity - if you are reading this review in ten years time, this book will still be useful.