Here are 42 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…
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.
Maps and data visualization live in my mind as close cousins: geographical coordinates are often the best way to show where data happens, and the techniques that cartographers have worked out can be adapted to the ways I represent visuals. Maps also have some interpretive advantages over abstract data: San Francisco is always west of Washington, DC. That’s not as true of information graphs, where their respective data points might move around depending on what is being plotted and what the axes are.
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’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 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 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…
A noted quantitative hedge fund manager and quant finance author, Ernie is the founder of QTS Capital Management and Predictnow.ai. Previously he has applied his expertise in machine learning at IBM T.J. Watson Research Center’s Human Language Technologies group, at Morgan Stanley’s Data Mining and Artificial Intelligence Group, and at Credit Suisse’s Horizon Trading Group. Ernie was quoted by Bloomberg, the Wall Street Journal, New York Times, Forbes, and the CIO magazine, and interviewed on CNBC’s Closing Bell program. He is an adjunct faculty at Northwestern University’s Master’s in Data Science program and supervises student theses there. Ernie holds a Ph.D. in theoretical physics from Cornell University.
I have used this book to teach my Financial Risk Analytics course at Northwestern University for many years. As a textbook, it is surprisingly easy to read, and the abundant exercises are great. This would be a foundational text to read after you have read my own books. It puts you on solid ground to understand all the financial babble that you may read elsewhere. It includes extensive coverage of most basic topics important to a serious quantitative trader, while not being overly mathematical. Easily understandable if you have basic programming and math background from first year of university.
Everything is practical in this book, which isn’t what you would expect from a textbook! There is no math for math’s sake. I have used the techniques discussed in this book for real trading, and for creating features at my machine learning SaaS predictnow.ai. Examples: What’s the difference between net…
The new edition of this influential textbook, geared towards graduate or advanced undergraduate students, teaches the statistics necessary for financial engineering. In doing so, it illustrates concepts using financial markets and economic data, R Labs with real-data exercises, and graphical and analytic methods for modeling and diagnosing modeling errors. These methods are critical because financial engineers now have access to enormous quantities of data. To make use of this data, the powerful methods in this book for working with quantitative information, particularly about volatility and risks, are essential. Strengths of this fully-revised edition include major additions to the R code…
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.
What if you are faced with a problem for which a standard approach doesn’t yet exist? In such a case, you will need to be able to figure out the approach from the first principles. This book will help you learn how to derive insights starting from raw data.
'A statistical national treasure' Jeremy Vine, BBC Radio 2
'Required reading for all politicians, journalists, medics and anyone who tries to influence people (or is influenced) by statistics. A tour de force' Popular Science
Do busier hospitals have higher survival rates? How many trees are there on the planet? Why do old men have big ears? David Spiegelhalter reveals the answers to these and many other questions - questions that can only be addressed using statistical science.
Statistics has played a leading role in our scientific understanding of the world for centuries, yet we are all familiar with the way…
I’m a historian who’s spent far too much time thinking about how the color magenta contributed to climate change and why eighteenth-century humanitarians were obsessed with tobacco enemas. My favorite historical topics—like sensation, color, and truth—don’t initially seem historical, but that’s exactly why they need to be explored. I’ve learned that the things that seem like second nature are where our deepest cultural assumptions and unconscious biases hide. In addition to writing nonfiction, I’ve been lucky enough to grow up on a ranch, live in Paris, work as an interior design writer, teach high school and college, and help stray dogs get adopted.
I had never really given much thought to counting until I read this book, but in the very first chapter, Stone made me rethink everything I thought I knew about “one fish, two fish, red fish, blue fish.” She shows that every time we count, we’re making cultural assumptions. For example, what counts as a fish? And what makes the color of the fish more relevant than other features? Counting reveals that while these choices may seem intuitive, basic, and meaningless, they have very real impacts on people’s lives. Especially when we use numbers to measure things like merit, poverty, race, and productivity, those fundamental assumptions matter more than we care to admit.
Early in her extraordinary career, Deborah Stone wrote Policy Paradox, a landmark work on politics. Now, in Counting, she revolutionises how we approach numbers and shows how counting shapes the way we see the world. Most of us think of counting as a skill so basic that we see numbers as objective, indisputable facts. Not so, says Stone. In this playful-yet-probing work, Stone reveals the inescapable link between quantifying and classifying, and explains how counting determines almost every facet of our lives-from how we are evaluated at work to how our political opinions are polled to whether we get into…
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