Here are 44 books that Semiology of Graphics fans have personally recommended if you like
Semiology of Graphics.
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Colin Koopman researches and teaches about technology ethics at the University of Oregon, where he is a Professor of Philosophy and Director of the interdisciplinary certificate program in New Media & Culture. His research pursuits have spanned from the history of efforts in the early twentieth century to standardize birth certificates to our understanding of ourselves as effects of the code inscribed into our genes. Koopman is currently at work on a book that will develop our understanding of what it takes to achieve equality and fairness in data systems, tentatively titled Data Equals.
W.E.B. Du Bois is widely acknowledged as the leading activist for racial equality of his generation. But until very recently little had been known of his deep commitment to the pursuit of equality within and through data technology. As Du Bois was preparing notes for his famous 1903 book The Souls of Black Folk, he was also preparing an exposition of what we would today call “infographics” (or what the editors of this volume aptly call “data portraits”) for exhibition at the 1900 Paris Exposition world’s fair. This volume handsomely reproduces for the first time a full-color complete set of Du Bois’s charts, graphs, maps, and ingenious spirals. A beautiful book to live with, it also subtly transforms one’s understanding of the history of racial progress and inequality in America.
"As visually arresting as it is informative."-The Boston Globe
"Du Bois's bold colors and geometric shapes were decades ahead of modernist graphic design in America."-Fast Company's Co.Design
W.E.B. Du Bois's Data Portraits is the first complete publication of W.E.B. Du Bois's groundbreaking charts, graphs, and maps presented at the 1900 Paris Exposition.
Famed sociologist, writer, and Black rights activist W.E.B. Du Bois fundamentally changed the representation of Black Americans with his exhibition of data visualizations at the 1900 Paris Exposition. Beautiful in design and powerful in content, these data portraits make visible a wide spectrum of African American culture, from…
In sixth grade, my teacher tried to teach the class how to read line charts – and something fell into place for me. Ever since then, I’ve tried to sort data into forms that we can use to make sense of it. As a researcher at Microsoft, I consulted with teams across the organization – from sales to legal; and from Excel to XBox – to help them understand their data. At Honeycomb, I design tools for software operations teams to diagnose their complex systems. These books each gave me an “ah-hah” moment that made me think differently about the craft of creating visualization. They now sit on my shelf in easy reach – I hope you find them fascinating too.
I learned Tukey’s name about as soon as I learned that data visualization existed as more than a menu in Excel and a personal obsession. Tukey coined the term “exploratory data analysis,” and so tapped into a passion for swimming around in all the interesting rows and columns. Tukey was working before computers were widespread, and so I got a view of how he saw data: working against the constraints of pencil and paper; keeping your hand moving as fast as possible. While the explorations we can do with gigabytes of memory and powerful rendering are very different, the goal of getting information into your head as fast as possible is unchanged.
This title is part of the Pearson Modern Classics series. Pearson Modern Classics are acclaimed titles at a value price. Please visit www.pearson.com/statistics-classics-series for a complete list of titles.
The approach in this introductory book is that of informal study of the data. Methods range from plotting picture-drawing techniques to rather elaborate numerical summaries. Several of the methods are the original creations of the author, and all can be carried out either with pencil or aided by hand-held calculator.
0134995457 / 9780134995458 EXPLORATORY DATA ANALYSIS (CLASSIC VERSION), 1/e
In sixth grade, my teacher tried to teach the class how to read line charts – and something fell into place for me. Ever since then, I’ve tried to sort data into forms that we can use to make sense of it. As a researcher at Microsoft, I consulted with teams across the organization – from sales to legal; and from Excel to XBox – to help them understand their data. At Honeycomb, I design tools for software operations teams to diagnose their complex systems. These books each gave me an “ah-hah” moment that made me think differently about the craft of creating visualization. They now sit on my shelf in easy reach – I hope you find them fascinating too.
I’ve always felt a desire to make the world make sense through data – that numbers and structure could help unlock hidden meanings. When I read this novel, I felt seen: it’s told from the perspective of T. S. Spivet – a 12-year-old boy who has the same urge. Spivet thoroughly documents the world around him, sketching an ant he sees in the grass, and drawing schematics and maps of the spaces he travels through on his quest to travel to the Smithsonian Institution. The book’s margin is lavishly illustrated with Spivet’s diagrams – in seeing the world through his eyes, it felt like how I see it through my own.
A brilliant, boundary-leaping debut novel tracing twelve-year-old genius map maker T.S. Spivet's attempts to understand the ways of the world
When twelve-year-old genius cartographer T.S. Spivet receives an unexpected phone call from the Smithsonian announcing he has won the prestigious Baird Award, life as normal-if you consider mapping family dinner table conversation normal-is interrupted and a wild cross-country adventure begins, taking T.S. from his family ranch just north of Divide, Montana, to the museum's hallowed halls.
T.S. sets out alone, leaving before dawn with a plan to hop a freight train and hobo east. Once aboard, his adventures step into…
I have obsessed with maps my whole life, but I guess the main drive for studying them is my enjoyment of outdoor spaces, as a hiker, a mountaineer, and as a sailor: always with a paper map at hand. If you use GPS (a wonderful innovation) you will not only lose some of your precious orientation abilities but above all you will look less at the environment around you. I feel that paper maps do a great favor to my brain and to my enjoyment of places. The books below are a great tribute to maps; they helped me understand them better, and this affected the way I use them.
If you draw a map, you have many choices of symbols, colors, types of lines, sizes of characters, and so on. We may think these are just arbitrary choices perpetuated by tradition, but MacEachren successfully shows that we better conceive of those items as solutions to communication problems in a subtle dialogue with the Gestalt requirements of visual perception. Not any symbol will do. The symbols must be fit for minds like ours.
I learned a lot from this visual approach to maps.
Now available in paperback for the first time, this classic work presents a cognitive-semiotic framework for understanding how maps work as powerful, abstract, and synthetic spatial representations. Explored are the ways in which the many representational choices inherent in mapping interact with information processing and knowledge construction, and how the resulting insights can be used to make informed symbolization and design decisions. A new preface to the paperback edition situates the book within the context of contemporary technologies. As the nature of maps continues to evolve, Alan MacEachren emphasizes the ongoing need to think systematically about the ways people interact…
I am a leader in analytics and AI strategy, and have a broad range of experience in aviation, energy, financial services, and the public sector. I have worked with several major organizations to help them establish a leadership position in data science and to unlock real business value using advanced analytics.
Data scientists and analytics specialists are great at building models and algorithms, but often wrap them in a presentation or dashboard that diminishes their value and reduces the likelihood of their work being adopted. This book encourages practitioners to always consider the last mile and to pay as much attention to presentation and aesthetics as we do to the model itself.
Master the art and science of data storytelling-with frameworks and techniques to help you craft compelling stories with data.
The ability to effectively communicate with data is no longer a luxury in today's economy; it is a necessity. Transforming data into visual communication is only one part of the picture. It is equally important to engage your audience with a narrative-to tell a story with the numbers. Effective Data Storytelling will teach you the essential skills necessary to communicate your insights through persuasive and memorable data stories.
Narratives are more powerful than raw statistics, more enduring than pretty charts. When…
I’m an applied statistician and academic researcher/lecturer at New Zealand’s oldest university – the University of Otago. R facilitates everything I do – research, academic publication, and teaching. It’s the latter part of my job that motivated my own book on R. From first-year statistics students who have never seen R to my own Ph.D. students using R to implement novel and highly complex statistical methods and models, my experience is that all ultimately love the ease with which the R language permits exploration, visualisation, analysis, and inference of one’s data. The ever-growing need in today’s society for skilled statisticians and data scientists means there's never been a better time to learn this essential language.
For those intending to use R with an eye on the popular 'Tidyverse' suite of packages – which facilitate the handling, manipulation, and visualisation of data sets – it's hard to go past this book. From the founding contributors of the RStudio/Tidyverse worlds, this is a great way to learn about this dialect of R against the overarching backdrop of statistical data analysis and data science.
Learn how to use R to turn raw data into insight, knowledge, and understanding. This book introduces you to R, RStudio, and the tidyverse, a collection of R packages designed to work together to make data science fast, fluent, and fun. Suitable for readers with no previous programming experience, R for Data Science is designed to get you doing data science as quickly as possible. Authors Hadley Wickham and Garrett Grolemund guide you through the steps of importing, wrangling, exploring, and modeling your data and communicating the results. You'll get a complete, big-picture understanding of the data science cycle, along…
I started my career as a research scientist building machine learning algorithms for weather forecasting. Twenty years later, I found myself at a precision agriculture startup creating models that provided guidance to farmers on when to plant, what to plant, etc. So, I am part of the movement from academia to industry. Now, at Google Cloud, my team builds cross-industry solutions and I see firsthand what our customers need in their data science teams. This set of books is what I suggest when a CTO asks how to upskill their workforce, or when a graduate student asks me how to break into the industry.
It is not enough for a data scientist to be able to analyze data and build ML models. You have to be able to communicate the insights to decision-makers concisely and accurately. This book shows you bad and good visualizations — you’ll be surprised by how often you would have defaulted to the bad way without the guidance provided by this book!
Effective visualization is the best way to communicate information from the increasingly large and complex datasets in the natural and social sciences. But with the increasing power of visualization software today, scientists, engineers, and business analysts often have to navigate a bewildering array of visualization choices and options.
This practical book takes you through many commonly encountered visualization problems, and it provides guidelines on how to turn large datasets into clear and compelling figures. What visualization type is best for the story you want to tell? How do you make informative figures that are visually pleasing? Author Claus O. Wilke…
I am not very good at making things. I am good enough to appreciate the craftsmanship of those much better than me. I am more of an ideas person, perhaps why I ended up with a PhD in Philosophy of Science. But I have always held a secret admiration—with a tinge of envy—for people who are makers. As I went deeper into my career as a philosopher of science, I became aware that the material/making aspect of science—and technology—was largely ignored by ideas-obsessed philosophers. So, this is where I focused my attention, and I’ve loved vicariously being able to be part of making the world.
When I was a kid, one of my favorite books was The Way Things Work, not the more recent David Macaulay book—which is also good—but the earlier 1967 book by T. Lodewijk. With great diagrams, it showed how complicated machines work.
Randall Munroe's Thing Explainer, while less comprehensive, similarly captures this magic for me. It has great diagrams and simple clarifying text—self-consciously limited to the 1,000 words people use the most. I could stare at the diagrams for hours, learning about everything from cameras (“picture takers”) to submarines (“boats that go under the sea”).
From the No. 1 bestselling author of What If? - the man who created xkcd and explained the laws of science with cartoons - comes a series of brilliantly simple diagrams ('blueprints' if you want to be complicated about it) that show how important things work: from the nuclear bomb to the biro.
It's good to know what the parts of a thing are called, but it's much more interesting to know what they do. Richard Feynman once said that if you can't explain something to a first-year student, you don't really get it. In Thing Explainer, Randall Munroe takes…
I am a financial data scientist. I think it is important that data scientists are highly specialized if they want to be effective in their careers. I run a business called Conlan Scientific out of Charlotte, NC where me and my team of financial data scientists tackle complicated machine learning problems for our clients. Quant trading is a gladiator’s arena of financial data science. Anyone can try it, but few succeed at it. I am sharing my top five list of math books that are essential to success in this field. I hope you enjoy.
One of my favorite professors, Gretchen Martinet, used this to teach a course called “Mathematical Statistics” when I was at the University of Virginia. It is an extremely profound course full of dense but fundamental mathematical proofs in classical statistics.
You will learn why the formula for the normal distribution is the way it is, why the sum of squares appears everywhere in statistics, and how to fit a linear regression by hand. In the same way calculus elevates our understanding of rates of changes, the book elevates your understanding of samples, averages, and distributions. Quant trading requires an intuitive sense of how data, models, and aggregates work, making this content essential for your success.
Modern Mathematical Statistics with Applications, Second Edition strikes a balance between mathematical foundations and statistical practice. In keeping with the recommendation that every math student should study statistics and probability with an emphasis on data analysis, accomplished authors Jay Devore and Kenneth Berk make statistical concepts and methods clear and relevant through careful explanations and a broad range of applications involving real data.
The main focus of the book is on presenting and illustrating methods of inferential statistics that are useful in research. It begins with a chapter on descriptive statistics that immediately exposes the reader to real data. The…
I taught for 45 years at Ithaca College broken by two years as Fulbright Professor in West Africa at the University of Liberia. During my years in academia, I developed several new courses including a popular “Math in Africa” class and the first U.S. course for college credit in chess theory. I’ve always had a passion for and continue to have strong interests in (1) national educational and social issues concerning equal access to math education for all and (2) teaching others about the power of mathematics and statistics to help one more deeply understand social issues.
This book is kind of a fun crash course in statistics which covers all the basic concepts at an introductory level.
The cartoons are a little bit dated, but still entertaining. There are lots of pictures and graphs which are a pleasure if you are a visual learner. The reader will come away with many useful tools to help understand real world problems.
I’m a retired math professor, but still got a real kick out of this book and especially appreciated the many good examples referenced such as gender discrimination in salaries and racial discrimination in jury selection. I recommended it to many of my struggling students.
Updated version featuring all new material. If you have ever looked for P-values by shopping at P mart, tried to watch the Bernoulli Trails on "People's Court," or think that the standard deviation is a criminal offense in six states, then you need The Cartoon Guide to Statistics to put you on the road to statistical literacy. The Cartoon Guide to Statistics covers all the central ideas of modern statistics: the summary and display of data, probability in gambling and medicine, random variables, Bernoulli Trails, the Central Limit Theorem, hypothesis testing, confidence interval estimation, and much more-all explained in simple,…
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