data presentation book

The Top 20 Data Visualization Books That Should Be On Your Bookshelf

“Most of us need to listen to the music to understand how beautiful it is. But often that’s how we present statistics: we just show the notes, we don’t play the music.” - Hans Rosling, Swedish statistician

datapine is filling your bookshelf thick and fast. Previously, we discussed the top 19 big data books you need to read, followed by our rundown of the world’s top business intelligence books as well as our list of the best SQL books for beginners and intermediates. Now that you’re sold on the power of data analytics in addition to data-driven BI, it’s time to take your journey a step further by exploring how to effectively communicate vital metrics and insights in a concise, inspiring, and accessible format through the power of visualization.

Data visualization, or ‘data viz’ as it’s commonly known, is the graphic presentation of data. These aesthetically striking portrayals are the most effective way to succinctly translate large segments of complex data to a wide audience. Successful visualizations are aesthetically beautiful, providing layers of detail that generate deeper dimensions of insight and whole new layers of understanding. They can be fun and interactive, too.

The field of online data visualization is growing, and whether you’re a data viz expert or just getting started, there is a wide range of books that will help you learn new skills and remain ahead of the pack.

Not sure where to start? A mere Amazon search of this topic returns over 15k items. That’s a colossal number of books on visualization. And while some of them we consider the best books on data visualization, some are really not.

But don’t fret, because we’ve conducted the research and reading on your behalf, refining our findings to create our list of the world’s best 20 data visualization books.

This list is in no particular order, but what we promise you is that these are 20 of the best books on data visualization available today, and you’ll find there’s something for everybody.

Here we’ve included prose based on visualization history, theory, psychology, and practical implementation as well as intricate graphical presentation tips and a visually stunning coffee table book for inspiration.

But before we delve into our list of books on data visualization, here are some facts and figures for your inspiration.

Data visualization: What You Need To Know

• 90% of the information transmitted to the brain is visual – a testament to the power and potential of data visualization.
• People are 30 times more likely to absorb high-quality infographics than plain text.
• Studies suggest that if a scientific claim is presented in only words or numbers, 68% of people will deem the information to be accurate and truthful. But if the same insights or metrics are presented in a simple graph, the number rises to 97% .
• A BI strategy that leverages data visualization will provide an ROI of $13.01 back on every dollar spent.

Now that we’ve established the importance and potency of visualization in the digital age, let’s take a look at our rundown of the best data visualization books.

1) “The Visual Display of Quantitative Information” by Edward R. Tufte

**click for book source**

You can’t make a list of data visualization books without including Edward Tufte . Tufte is considered to be amongst the foremost pioneers in the field and this really is considered the best book on data visualization out there. “The Visual Display of Quantitative Information” is one of his most famous data visualization books. It covers the theory and design of data graphics and provides 250 illustrations of the best and worst examples. Though printed in 1983, it remains a classic and a bestseller on Amazon. Tufte provides an astute introduction to core visualization theories, building on these concepts throughout the book, and frankly, this should be on every data viz bookshelf.

2) “Storytelling With Data: A Data Visualization Guide for Business Professionals” by Cole Nussbaumer Knaflic

Laszlo Bock, former SVP of People Operations at Google, introduces one of the most beautiful data visualization books in our lineup in a way that nobody else can:

“In ‘Storytelling With Data,’ Cole has created an of-the-moment complement to the work of data visualization pioneers like Edward Tufte. She’s worked at and with some of the most data-driven organizations on the planet as well as some of the most mission-driven, data-free institutions. In both cases, she’s helped sharpen their messages and their thinking.”

You may be able to create complicated, technical, and colorful visualizations and load them with a ton of data. But, if your target audience can’t decipher them, what’s the point? In the end, visualizations are meant to convey or highlight what’s happening with your data – they’re supposed to tell a story. That is why “Storytelling With Data” is on our list.

Cole Knaflic’s mission is to teach readers. “Don’t simply show your data – tell a story with it,” and her offering, which we consider to be one of the world’s best data visualization books, demonstrates how to go beyond conventional tools to reach the root of your data while using a dashboard storytelling process to create an engaging and informative presentation in a way that is accessible to everyone.

The author recently published an “expanded follow-up” to her book called “Storytelling With Data: Let’s Practice!”. In this guide-style publication, Cole Knaflic presents exercises and real-world examples to put all the knowledge you gain from the first piece into practice.

3) “The Big Book Of Dashboards: Visualizing Your Data Using Real-World Business Scenarios” by Steve Waxler, Jeffrey Shaffer, and Andy Cotgreave

Our next best book to learn data visualization is the “The Big Book Of Dashboards”. Written by professionals in the field, it provides readers with the perfect mix of the theoretical part of using data visualizations and the practical part of actually applying this knowledge to real-life scenarios. 

With dozens of real-world dashboard examples coming from various formats such as print, desktop, or mobile, and different industries such as finance, healthcare, human resources, sports, marketing, and more, this ultimate handbook on business dashboards will give you all the necessary tools and knowledge to build an effective dashboard using data visualizations. In addition to the examples, the book also offers insights into the psychological factors that go into building an engaging and efficient dashboard. 

With more than 30 years of experience combined, these authors put into words the best practices you should follow as well as the challenges you would most likely encounter during your dashboard design process and give you the necessary tools to deal with all of them. It is a definitive reference for anyone who wants to master the art of dashboarding. 

4) “Data Sketches: A Journey Of Imagination, Exploration, And Beautiful Data Visualizations” by Nadieh Bremer and Shirley Wu

This next piece is not a typical data visualization book that shows you the theoretical aspect of using visuals to understand data. Data Sketches is a publication that documents the creative process of authors Nadie Bremer and Shirley Wu in creating 24 data visualization projects. These projects explore different topics which include the Olympics, the Royals, myths, presidents, and more, exploring different technologies, visualization, and design techniques to show that data viz is an equal part of science and art. 

In addition to providing a behind-the-scenes look into the author's processes through sketches and notes, it also offers detailed insights on core data visualization concepts, data gathering and sketching techniques for the web, as well as practical lessons based on the challenges they faced during the creation of these 24 projects. 

Data Sketches is a book covered in full-color images that will lead you through the complete process of creating a data visualization project. You will also find the finalized version of the 24 projects and learn how powerful data storytelling can be. 

Since its publication in 2021, it has been praised by various professionals in the industry. Here is an excerpt from Andy Kirk, another data viz author that you will find later on this list:

“This book brings the perfect blend of ingredients together for a nourishing recipe of inspiration and knowledge beneficial to beginners and experienced practitioners alike. Nadieh and Shirley are generational talents. Through their data visualization work, they relentlessly exhibit a wide spectrum of capabilities across the creative, editorial, analytical, and technical dimensions”.

5) "Data Visualization - A Practical Introduction" by Kieran Healy

If you’re searching for a book that combines theory and practice in a comprehensive and hands-on approach to visualization, you need to have this one on your bookshelf. Written by a professor of sociology at Duke University, this book provides researchers and students instructions on using R and ggplot2 in an innovative and coherent manner.

Whether you are a beginner or an advanced user of data, statistics, and visualizations, this fun, and readable book, authored by Kieran Healy , will show you the unique world of visualizations that can be effectively used for communicating data in a graphical way.

6) “How Charts Lie: Getting Smarter About Visual Information” by Alberto Cairo

While displaying data in beautiful charts, graphs, and diagrams is the most successful way to uncover hidden trends and patterns in our data, it doesn’t come without challenges. When not used correctly, charts can lie. The information in them can be misleading or misunderstood in a number of ways, a tactic that has been used for decades by journalists, politicians, and advertisers to manipulate audiences. 

Under that premise, renowned journalist and data visualization expert, Alberto Cairo builds his latest publication: “How Chats Lie: Getting Smarter About Visual Information”. In this book, Cairo uses a wide range of examples to teach readers how to understand graphs and charts and how to spot the misleading visualizations we often see in the media. It is a perfect guide for beginners that need to understand how charts work and the bad practices that need to be avoided when presenting data in a visual way.  

7) “Infographics Designers’ Sketchbooks” by Steven Heller and Rick Landers

Focusing less on data analytics and more on eye-grabbing presentations, “Infographics Designers’ Sketchbooks” is a data visualization book that will inspire your creative efforts from start to finish.

What's particularly impressive about this data visualization design book is the fact that it offers a rare glimpse into the minds of 50 of the world's most prolific graphic designers through their personal sketchbooks. By examining the creative process of the planet’s best visualists, you’ll be able to spot the link between data and drawings. As a result, you’ll gain the tools and inspiration you need to create a wealth of stunning infographics that will help you present critical information that enlightens and creates value rather than excluding and causing confusion.

8) “Knowledge is Beautiful” by David McCandless

Boasting near flawless reader reviews on Amazon, this graphically-driven book on data visualization makes an excellent companion when it comes to thriving in the digital age.

“Knowledge is Beautiful” is celebrated data journalist David McCandless ' best-selling publication on the subject, and its concept is simple: using sensational visuals to reveal surprising insights into how the world really works and using this knowledge for your own innovations.

If you’re looking to transform head-turning, jaw-dropping aesthetic concepts that connect with others on a meaningful level while yielding real results, this is one of the best data visualization books you will ever own.

9) “The Book of Circles: Visualizing Spheres of Knowledge” by Manuel Lima

The follow-up to the critically-acclaimed "The Book of Trees and Visual Complexity", this offbeat and wildly valuable book on data visualization delves deep into millennia of circular information innovation and design.

Coupling a kaleidoscopic array of inspirational visual designs throughout the ages with stunning modern-day examples of visualization in action, author Manuel Lima connects the enduring concept of the human relationship with visuals and its application to data in a way that is exciting, engaging, and practical in equal measure. One of the visualizing data best books available today.

10) “Effective Data Visualization: The Right Chart for the Right Data” by Stephanie Evergreen

Excel has been the primary tool to generate visuals and manage data for decades. That said, it is still intimidating software that makes the lives of a lot of users very difficult. With that issue in mind, author Stephanie Evergreen put together “Effective Data Visualization: The Right Chart for the Right Data” as a guide for readers that want to learn how to create Excel charts and graphs in an efficient way.

Using approachable and funny language, Evergreen guides readers into learning the fundamentals of data visualization in Excel. The book includes the types of graphs available and on which scenario they are most appropriate to use, as well as a step-to-step checklist to create them on the tool. Originally published in 2016, it has a second edition that was published in 2019 that includes a rewritten chapter on qualitative data as well as 9 new chart types and shortcuts on Excel. A must-read for anyone looking to become an Excel guru.

11) “Better Data Visualizations: A Guide for Scholars, Researchers, and Wonks” by Jonathan Schwabish

As you’ve probably noticed already with our list of books, visuals make the world better. Every piece of information is better received and perceived when it comes in a visual format. Author Jonathan Schwabis put the value of visuals into perspective with his piece “Better Data Visualizations: A Guide For Scholars, Researchers, And Wonks”. 

Through more than 500 examples, the author aims to teach its readers the value of data visualizations and how to create effective ones in different contexts. Anyone reading this piece can expect to learn the do’s and don'ts of visual design, the core visualization types and how each of them should be used, and how to clearly communicate your message using graphics. 

If you don’t trust our word for it, maybe this excerpt from a reader's review will convince you: “This book is quite extraordinary in regards to giving the presentational theory of graphed information. Truly a must to anyone who wants to tell stories through data” .

12) “Data Visualisation: A Handbook for Data Driven Design” by Andy Kirk

Voted one of the "six best books for data geeks" by The Financial Times, "Data Visualization: A Handbook for Data Driven Design," authored by Andy Kirk , offers a clear, concise, and digestible introduction to the field.

With a host of hand-picked data visualization examples as well as a wealth of real-world tips, methods, approaches, and insights, this really is one of the best books on data visualization for those getting started.

The language is accessible, the advice is practical, and in addition to a physical copy of the book, you will also gain access to comprehensive online data analysis tools and support, which includes a selection of exercises so you can practice your skills. Be aware that there is a second edition to this book published in 2019.

13) “Fundamentals of Data Visualization: A Primer on Making Informative and Compelling Figures” by Claus O. Wilke

Being successful when it comes to data visualization is not an easy task. There is a wide range of tools and techniques out there which can make you feel confused and overwhelmed. If this is the case for you, don’t worry, author Claus Wilke data visualization book has you covered. 

In “Fundamentals Of Data Visualization: A Primer On Making Informative And Compelling Figures”, Wilke guides readers through a practical journey to understanding the fundamentals of successful visuals. Covering topics such as the basics of colors, details on chart types, examples of poorly constructed visuals, and tips on how to turn large datasets into a compelling story, this publication is the perfect overview for scientists, engineers, and anyone whose work involves generating data visualizations. No matter the visual analytics tools or programming language you use, after reading this book you should be able to extract the maximum potential out of your visual design process. 

14) “Visualize This: The Flowing Data Guide to Design, Visualization, and Statistics” by Nathan Yau

One of the most valuable visualizing data books of recent times, data viz guru Nathan Yau provides another great practical handbook on visualization and how to approach real-world data in his masterpiece, “Visualize This . ” We recommend this book in color to take full advantage of the step-by-step tutorials on how to visualize and tell stories with data.

For developers, there are code examples in R, Python, and JavaScript. Yau’s website flowingdata.com is another great source for all levels of visualization that also showcases fun data viz experiments.

15) “Good Charts: The HBR Guide to Making Smarter, More Persuasive Data Visualizations” by Scott Berinato

Published in May 2016, this is one of the most relevant visualizing data books on the market. What we like about “Good Charts” is that it’s accessible for the data viz beginner but just as helpful for the more advanced dashboard design user.

Berinato highlights the important point that data viz is no longer being left just to the specialists: data scientists and professional designers. A new generation of data visualization tools, like datapine, and massive amounts of data make it easy for the layperson to create visualizations that communicate ideas far more effectively than spreadsheets and PowerPoint charts. In “Good Charts,” Berinato provides an invaluable guide to how visualization works and how to use data viz to influence and persuade. This is a must-read.

Another gem coming from this author is his second title and the most recent book: Good Charts Workbook: Tips, Tools, and Exercises for Making Better Data Visualizations . The beauty of this publication lies within the practical part; a hands-on guide that will enable you to create your own visualization while teaching you how to develop your own visualization and thinking processes. A must-read for anyone interested in the practical part of data visualization. 

16) “Information Dashboard Design: Displaying Data for At-a-glance Monitoring” by Stephen Few

Don’t let the lackluster cover fool you, or as they say: don’t judge a book by its cover, because this visualizing data book has plenty to offer in the way of visualization. Few guides are so helpful in providing a wide range of good and bad dashboard examples . Moreover, author Stephen Few offers a great introduction to the principles of data visualization and design theory. If you wonder which graph displays data in top-to-bottom fashion, then this piece is for you. Oh, and if you become a Stephen Few fan, he has multiple visualization and analytics books to check out.

17) “Information Graphics” by Sandra Rendgen, Julius Wiedemann

Looking for inspiration? Or just love data viz as an art? One of the most beautiful data visualization books is a great coffee table book or one to keep next to your desk for when you’re in a data viz rut.

It has a little of everything, providing over 400 examples of information graphics from around the world, covering journalism art, government, education, business, and more. The introductory essays on data visualization history and theory as well as data journalism are also a good read, and the accompanying images are worth a long look.

18) “Cartographies of Time: A History of the Timeline” by Daniel Rosenberg

This book won’t help you get your business intelligence dashboard up and running for next week’s board meeting, but history buffs will be enthralled by the comprehensive history of graphic representations of time in Europe and the United States from 1450 to the present. The accompanying graphics prove that visualization isn’t just a modern-day science. We have been striving to draw time long before the conception of dashboards!

19) “Semiology of Graphics: Diagrams, Networks, Maps” by Jacques Bertin

We round out this list with another visualization pioneer, Jacques Bertin. Originally published in 1967 in French, Bertin’s Semiology of Graphics holds a significant place in the theory of information design.

Founded on Jacques Bertin’s practical experience as a cartographer, Part One attempts to synthesize principles of graphic communication with the logic of standard rules applied to writing and topography. Part Two brings Bertin’s theory to life, presenting a close study of graphic techniques, including shape, orientation, color, texture, volume, and size in an array of more than 1,000 maps and diagrams.

20) “Beautiful Visualization, Looking at Data Through the Eyes of Experts” by Julie Steele, Noah Iliinsky

This piece is part of O’Reilly’s “Beautiful” series, with each book a collection of essays from people who work in the field. “Beautiful Visualization,” authored by Julie Steele and Noah Iliinsky, describes the design and development of some well-known visualizations by examining the methods of 24 visualization experts.

One of the great things about this big data visualization book is that the authors approach their projects from a variety of perspectives: artists, designers, commentators, scientists, analysts, statisticians, and more. The topics include:

• The importance of storytelling through a simple visualization exercise.
• Color and how it conveys information that our brains recognize before we are cognizant of it.
• Uncovering a method to the madness of air travel with a civilian air traffic visualization that is beautifully portrayed.

To summarize, here is our list of the best books on data visualization:

• “The Visual Display of Quantitative Information” by Edward R. Tufte
• “Storytelling With Data: A Data Visualization Guide for Business Professionals” by Cole Nussbaumer Knaflic
• “The Big Book of Dashboards: Visualizing Your Data Using Real-World Business Scenarios” by Steve Wexler, Jeffrey Shaffer, Andy Cotgreave
• “Data Sketches: Journey Of Imagination, Exploration, And Beautiful Visualizations” by Nadieh Bremer, Shirley Wu
• “Data Visualization – A Practical Introduction” by Kieran Healy
• “How Charts Lie: Getting Smarter About Visual Information” by Alberto Cairo
• “Infographics Designers’ Sketchbooks” by Steven Heller and Rick Landers
• “Knowledge is Beautiful” by David McCandless
• “The Book of Circles: Visualizing Spheres of Knowledge” by Manuel Lima
• “Effective Data Visualization: The Right Chart For The Right Data” by Stephanie Evergreen 
• “Better Data Visualizations: A Guide for Scholars, Researchers, and Wonks” by Jonathan Schwabish
•  “Data Visualisation: A Handbook for Data Driven Design” by Andy Kirk
• “Fundamentals of Data Visualization: A Primer on Making Informative and Compelling Figures” by Claus O. Wilke
• “Visualize This: The Flowing Data Guide to Design, Visualization, and Statistics” by Nathan Yau
• “Good Charts: The HBR Guide to Making Smarter, More Persuasive Data Visualizations” by Scott Berinato 
• “Information Dashboard Design: Displaying Data for At-a-glance Monitoring” by Stephen Few
• “Information Graphics” by Sandra Rendgen, Julius Wiedemann
• “Cartographies of Time: A History of the Timeline” by Daniel Rosenberg
• “Semiology of Graphics: Diagrams, Networks, Maps” by Jacques Bertin
• “Beautiful Visualization, Looking at Data Through the Eyes of Experts” by Julie Steele, Noah Iliinsky

Now, it’s time to step away from those Excel spreadsheets, forget those static tables, and start telling a real story with your data. As you may have gathered, data visualization is the best way to squeeze every last drop of value from the metrics and insights that are available at your fingertips, and now that you’ve got the tools you need for success, it’s time to get going.

Do you want to get a head start on deciding what data visualizations you should start to build while waiting for your books to arrive? Check out our 14-day trial , completely free!

21 Best Data Visualization Books, According to Experts

From general interest to advanced texts, here’s the best data visualization books to dive into.

Want to level up your data visualization knowledge? You may need to head to the library.

With the world more technologically connected, data science and the need for visualizing data have swelled in significance. And though the nature of data and new tools is ever changing, so many core best practices in data visualization remain evergreen. As such, some of the best data visualization books have (pardon the pun) a long shelf life.

“A lot of what changes is just software, [but] a lot of the concepts remain very constant,” said Alli Torban, a Washington, D.C.-based data visualization consultant. “Even a 10-year-old book still has a lot it can teach you.”

We asked four data visualization experts to select and discuss a few of their favorite data visualization books. Our experts include:

• Cole Nussbaumer Knaflic , CEO of storytelling with data, author of Storytelling with Data: A Data Visualization Guide for Business Professionals and Storytelling with Data: Let’s Practice! and host of the SWD podcast.
• Alli Torban , an information design consultant and host of the Data Viz Today podcast.
• Randy Krum , founder of data visualization and infographics design firm InfoNewt, and author of Cool Infographics: Effective Communication with Data Visualization and Design .
• Tamara Munzner , a computer science professor at the University of British Columbia and author of Visualization Analysis and Design .

According to them, these data visualization books are worth picking up.

21 Best Data Visualization Books

Ranging from casual reads to practical applications and advanced theory deep-dives, here are the must-read data visualization books according to experts.

Best Data Visualization Books

• Data Sketches  by Nadieh Bremer and Shirley Wu
• Information Graphics  by Sandra Rendgen
• Avoiding Data Pitfalls  by Ben Jones
• Better Data Visualizations  by Jonathan Schwabish
• Good Charts Workbook  by Scott Berinato
• How Charts Lie  by Alberto Cairo
• Info We Trust  by RJ Andrews
• Data at Work  by Jorge Cam ões
• The Big Book of Dashboards  by Steve Wexler, Jeffrey Shaffer and Andy Cotgreave
• Design for Information  by Isabel Meirelles

General Interest Data Visualization Books

1. cartography. by kenneth field.

This book — written by a professional cartographer — isn’t solely about data visualization. Or is it? After all, a map can be thought of as a way to visualize data when the where matters. This is one of the most impressive books I’ve ever encountered. That’s not only due to its sheer heft — there are more than 500 pages packed with content — but also because of the thoughtful way Field structures and organizes so much information in a way that doesn’t overwhelm.

Each two-page spread focuses on a single aspect (for example, a map type or design principle). The text is formatted for easy scanning and paired with a variety of beautiful images. Topics are color-coded and alphabetized, so it’s easy to find items of interest or explore a concept that piques your curiosity. It turns out that much of the design rationale that goes into making a good map can direct efforts visualizing data in graphical form as well. (Knaflic)

Buy: Bookshop , Amazon

2. Data Sketches: A Journey of Imagination, Exploration, and Beautiful Data Visualizations by Nadieh Bremer and Shirley Wu

Data Sketches has a special place in my heart. It’s a tour de force — sort of like a coffee table book meets tech, a great book if you know something about data viz and want to go to the next level. It’s an amazing resource for people who want to level up.

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History of Data Visualization Books

3. information graphics by sandra rendgen.

A great coffee table book. It charts the evolution of visual communication design, with hundreds of graphics. The audience is people who are interested in information design, but it has general interest appeal. Anyone who picks it up will find something interesting.

The chapters are broken up by location, time, category and hierarchy, then every page has giant examples. I like to flip through when I need inspiration, to get a quick sense of how other people have solved similar design problems. So if I’m doing something with, say, time series, I’ll just flip through some dozens of pages of various time based-infographics and end up with a ton of ideas. Also, it’s just a really beautiful book. (Torban)

4. Semiology of Graphics: Diagrams, Networks, Maps by Jacques Bertin

Semiology was written in the 1960s, and Bertin’s insights are even more noteworthy given the technological constraints of the day. He pioneered methods for systematic data analysis that are now done with interactive computation, but he had to try them out as manual operations with paper and scissors and even built some mechanical devices .

The book contains amazing systematic diagrams. On the other hand, the English translation is terrible, and Bertin was admittedly idiosyncratic. A modern English speaking viz person would use very different terms than the ones used in the book. You wouldn’t say “implantation,” you’d say layout. But if you want to go deep and you’re interested in the history of the ideas, there is absolutely nothing like it.

In data viz, there’s visual encoding and there’s interaction. And we have much more deep theory about how to visually encode data than we do about interacting with it. So I think a lot of the ideas here, which are about visual encoding, do hold up. (Munzner)

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5. avoiding data pitfalls: how to steer clear of common blunders when working with data and presenting analysis and visualizations by ben jones.

I recommend Avoiding Data Pitfalls frequently. Conversational in tone and packed with relatable examples, this book details seven pitfalls that Jones has fallen into or seen others stumble over, in an effort to spare readers from making the same mistakes.

Each chapter focuses on a single peril, explaining the specific relevant dangers and illustrating through example, helping you learn how to recognize and avoid them. Though there is but a single chapter focused specifically on data visualization (pitfall No. 6: graphical gaffes), the book demonstrates throughout the types of questions and thought processes that anyone who visualizes data should be pondering when analyzing their data. Great for those early in their data analysis and visualization journey, the lessons outlined serve as an important reminder of issues that can be easily overlooked by more experienced analysts as well. (Knaflic)

6. Better Data Visualizations: A Guide for Scholars, Researchers, and Wonks by Jonathan Schwabish

Published in early 2021 and including a stellar variety of visualizations (more than 500 examples), Better Data Visualizations reads as an enjoyable and educational encyclopedia of graphs.

The book is organized into three sections. It begins with a brief primer on data visualization best practices. Part two is the bulk of the book: chart types. Schwabish dives deep into different types of graphs that go well beyond the standards of lines and bars. He includes insightful discussion about why and when each works — and when they don’t — with many examples. The final section explores design and provides direction for developing and using a data visualization style guide.

While the subtitle suggests a more limited audience, this is an excellent resource for anyone who analyzes and communicates with data. I’m also a big fan of Schwabish’s related One Chart at a Time video series. (Knaflic)

7. Good Charts Workbook: Tips, Tools, and Exercises for Making Better Data Visualizations by Scott Berinato

A great book for a new practitioner, since it offers hands-on practice. It’s written in a style that almost feels like Berinato is a nice mentor or co-worker, and he’s just walking you through. It has quiz-like prompts, asking, for example, why a specific pie chart is hard to read, and what could make it better. Then there’s sketch space to draw what you’d do instead. The data sets he gives aren’t complex, so you can easily sketch them.

And there’s a discussion section where he doesn’t necessarily give answers, but explains how he’d think about it. In data viz, designers can sometimes think the way they do it is the [only] way, so I appreciate his measured approach: This is how I’d probably do it, and these are my considerations. (Torban) 

8. How Charts Lie: Getting Smarter about Visual Information by Alberto Cairo

A must-read guide if you’re designing charts that will be published. Cairo is in data journalism, and he’s dedicated to ensuring data visualizations are done objectively and don’t skew or misrepresent the data .

He’s spoken for years about how some poor visualization choices in the news media have led to misunderstanding of data, whether that’s weather data — hurricane maps — and also election data . It’s a great treatise on how data can be visualized in misleading ways, and for practitioners, what to avoid so that you don’t get strongly criticized by visualizing something in a manner you didn’t intend to do. (Krum)

Cairo’s books are super accessible, very clearly written. His background as a journalist is obvious. He works very hard to get people to think about the use of visuals. For anyone intent on data literacy, on making charts that don’t mislead, he’s your go-to. (Munzner)

9. Info We Trust: How to Inspire the World with Data by RJ Andrews

Info We Trust is a beautiful book. It feels substantial. The colors are vibrant. The language is poetic. The content is inspiring.

Nearly every inch is filled with information: margins are packed with quotes from wide-ranging sources and other relevant tidbits. While I’ve spoken with others who have found this distracting, I thought it gave insight into the extensive research that went into the book and a peek at ancillary paths not taken. The text and margins are interspersed with hand-drawn images and graphs that help reinforce concepts. The chapters are relatively short in length but dense in ideas and abstracts, which provide a good balance.

Info We Trust is definitely not a how-to book, yet it is interlaced with practical advice. At one point, Andrews discusses sparking curiosity in your audience. He says — and I’m paraphrasing — that good stories leave space for the audience to forge connections. The book itself does this beautifully, making astute observations but allowing the reader room to connect and extrapolate to their own work. (Knaflic) 

10. The Wall Street Journal Guide to Information Graphics: The Dos and Don'ts of Presenting Data, Facts, and Figures by Dona M. Wong

It’s written from a journalist’s perspective — with principles to make good charts for the media. But it’s also very helpful for those who don’t necessarily have a data background. Wong’s clear examples address specific, common issues. For instance, there’s a section on good y-axis increments — what an awkward increment looks like, and what to do instead. There’s also a very helpful section on log scales. The book is pretty prescriptive; it’s in the subtitle: the dos and don’ts. So if you’re looking for a foothold, this is a great, example-rich starting point.

Also, she explains math concepts very clearly, such as how to calculate a percent change, which, if you’re creating a chart, those kinds of calculations are things you have to do. But if you don’t necessarily have a math background, it can be confusing and hard to find by Googling. (Torban)  

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Business-Focused Data Visualization Books

11. cool infographics: effective communication with data visualization and design by randy krum.

I wrote this for people who use infographics, whether for internal communication, such as employee education or as a sales tool, or for public-facing, external marketing. It teaches you how to publish your designs online along with the SEO skills needed — how to set up a landing page and optimize for search. So many companies will design an infographic, throw it on their company blog or Facebook page, and then do nothing with it. You have to prepare before you publish.

Also, a full chapter is dedicated to infographic resumes, to help use the power of data visualization to market yourself. All the power of visualizing corporate data — letting readers quickly digest and retain information — applies to your personal information when applying for jobs as well. The chapter looks at the types of data that people visualize for resumes, plus questions like whether to separate the infographic from your text resume or integrate them together, and how to handle automated submission systems if you have an infographic resume. (Krum)

12. Data at Work: Best Practices for Creating Effective Charts and Information Graphics in Microsoft Excel by Jorge Camões

A great overview of visual perception and information theory, focused on communicating in a business environment. It goes through concepts like preattentive attributes, colors and how to choose the right chart for your data. Camões includes links for all his examples, which were built in Excel — the most common tool out there. So it doesn’t overwhelm people into thinking they need Tableau or Adobe Illustrator to build something beautiful. 

You can download and explore his charts via the links, but the book is not an Excel how-to. It’s all about choosing the best design, communicating effectively and making your visualizations stand out, rather than look like every other Excel chart out there. He also writes about not simply relying on the default chart gallery. (Krum)

13 and 14. Storytelling with Data: A Data Visualization Guide for Business Professionals and Storytelling with Data: Let's Practice! by Cole Nussbaumer Knaflic

My books, Storytelling With Data and the follow-up Let’s Practice! , focus on making graphs that make sense in the business world and weaving them into compelling and action-inspiring stories. They codify and expand upon the lessons that we teach in our workshops, sharing practical tips and strategies that you can use in your next report or presentation .

The first book, Storytelling With Data , is meant to be read. The popularity it’s maintained since publication is likely in a large part due to its simplicity. It’s a relatively quick read that allows you to immediately appreciate the power of the lessons, demonstrated through real-world examples. Once you’ve read it cover to cover, it can serve as a great desktop resource to those making graphs and presentations. 

Let’s Practice! invites you to do exactly that — practice. The same lessons that structure the first book guide this exercise-based book. Three sections of exercises comprise each chapter. In the first section, I pose a scenario for readers to work through on their own, but also detail my solution and the thought process that drove my decisions, sharing a good deal of content and many examples. This is followed by a section of unsolved exercises that are great for those who’d like additional independent practice or who teach others. In the final exercise section of each chapter, I break down strategies and outline how you can apply them to your next project.

Getting good at communicating with data takes practice, and this book will help you flourish. Everyone is invited to explore the interactive online companion resource, SWD community , too. (Knaflic)

15. The Back of the Napkin: Solving Problems and Selling Ideas with Pictures by Dan Roam

A classic. Here, Roam teaches how to communicate visually by focusing on hand drawings. He dives into business communication using diagrams, charts, maps and visual explanations. His visual thinking codex is all about which charts and diagrams go best with which kinds of information.

[The point is:] Don’t be afraid to visualize. Even if you’re a bad artist, like I am, you can still get your point across — and it’ll come across better if you visualize it versus anything else. No matter your drawing or design skills, you will benefit from this book.

I do a lot of our work, whether it’s presentation or infographic design, that involves visual metaphors, using icons and illustrations to communicate concepts, rather than visualizing data. He goes into a lot of that here as well. (Krum)

16. The Big Book of Dashboards: Visualizing Your Data Using Real-World Business Scenarios by Steve Wexler, Jeffrey Shaffer and Andy Cotgreave

This is the perfect book for business analysts , with countless examples of real dashboards, arranged by different scenarios: a sales dashboard for executives, rankings over time, complaint tracking, churn — things that you actually do in business contexts. Each chapter includes an example and explains why that dashboard works, and there are thoughtful discussions throughout each section on various practical and design considerations. Sometimes within a chapter, the co-authors disagree, so it’s really helpful to hear different sides. 

I don’t do dashboards too often, but when I do, it’s convenient to be able to look up exactly what I need based on a given use case. There has been some dashboard backlash, and I agree, they’re not the most exciting thing, but people still need and use them. So you need resources for how to do them well. (Torban)

17. The Big Picture: How to Use Data Visualization to Make Better Decisions—Faster by Steve Wexler

Here, Wexler goes into some data-viz theory, but he really focuses on how to get organizations to adopt a data visualization mentality, how to effectively communicate data internally and how to get executives to start looking at charts that aren’t just bar charts.

I call them the big three: bar charts, line charts and pie charts. There are people who don’t like to go beyond those borders. They don’t want to see a Sankey diagram or a scatter plot. Wexler writes about how to move your organization into those other visual styles that will actually help your people understand the data better. Sometimes that means visualizing the same data in a few different ways to get better understanding.

Companies have so much data, and it keeps growing. They feel an imperative to use it to make better business decisions. Here, Wexler directly connects that to visualizations. If you want to make effective decisions with your data, people need to understand it. (Krum)

More on Data Science 6 Ways to Make Data-Visualization Literacy Part of Company Culture

Advanced Data Visualization Books

18. design for information: an introduction to the histories, theories, and best practices behind effective information visualizations by isabel meirelles.

This book explains how we visually perceive information, plus different ways available to visualize data — charts, but also maps, network diagrams, a lot of flowcharts, trees and other ways to visualize information that may not specifically be a table of data. It’s definitely for [data-viz] practitioners, not for, say, a product manager or someone just dealing with Excel charts. This is more advanced, like if you’re designing timelines of the history of your company.

It’s almost 10 years old now, but the concepts about structure — relational, hierarchical, spatial, temporal or textual structures — don’t change. Whether you use R or Tableau, or draw by hand, the ideas of communicating effectively in a certain structure are consistent and tool-agnostic. (Krum)

[This book is] a carefully curated walk through the world of visualization, specifically from a designerly viewpoint, but one that goes deep. It’s not just a pretty coffee table book; it really does explore the ideas. (Munzner)

Buy: Amazon

19. Interactive Visual Data Analysis by Christian Tominski and Heidrun Schumann

This one is a bit scholarly and technical, focused on visual analytics, which is a particular sub-branch of visualization. It goes thoroughly back and forth between human-in-the-loop analysis and computational methods. This was the first really good visual analytics textbook out there. For those who do want to go deep into the interplay between computational and visual concepts, it’s great. (Munzner)

20. The Elements of Graphing Data by William S. Cleveland

For scientists and engineers who want to make scientific plots or charts for research papers, Elements of Graphing Data is astounding. Cleveland comes out of the statistics tradition. There was a legendary Bell Labs cohort, with John Tukey, who invented the term “ exploratory data analysis .” Cleveland was his associate and very much an intellectual successor. This is where to go deep into, for instance, how exactly to make a statistical plot and all the issues you should think about. Recommended for people who come from very quantitative backgrounds. (Munzner)

21. Visualization Analysis and Design by Tamara Munzner

I wrote Visualization Analysis and Design to scratch my own itch: How do I show people that visualization is a systematic way of thinking, not just a grab bag of this technique and that technique? If you’re interested in diving deep and thinking systematically, it’s a good resource. It’s not just for people with a computer science background. 

Admittedly, it has its dense moments, but I avoid math and jargon. I try to build bridges and present a big picture, but in a way that might make sense for a class textbook. But people definitely read it outside of classrooms — people in neighboring fields who want to know what this visualization thing is all about. (Munzner)

One of my favorite chapters is “No Unjustified 3D.” Usually you’ll just hear, “Well, if you do 3D, then your marks get distorted and you can’t compare values as well.” But she goes further, explaining a number of different issues. And then she talks about cases where 3D actually works and provides examples. It’s a very reasoned consideration of all the decisions you have to make. The examples are more on the academic, scientific side, but it’s a very measured approach to thinking like a data-viz designer. (Torban)

Responses have been edited for length and clarity.

Recent Data Science Articles

Fundamentals of Data Visualization

Claus o. wilke.

The book is meant as a guide to making visualizations that accurately reflect the data, tell a story, and look professional. It has grown out of my experience of working with students and postdocs in my laboratory on thousands of data visualizations. Over the years, I have noticed that the same issues arise over and over. I have attempted to collect my accumulated knowledge from these interactions in the form of this book.

The entire book is written in R Markdown, using RStudio as my text editor and the bookdown package to turn a collection of markdown documents into a coherent whole. The book’s source code is hosted on GitHub, at https://github.com/clauswilke/dataviz . If you notice typos or other issues, feel free to open an issue on GitHub or submit a pull request. If you do the latter, in your commit message, please add the sentence “I assign the copyright of this contribution to Claus O. Wilke,” so that I can maintain the option of publishing this book in other forms.

This work is licensed under the Attribution-NonCommercial-NoDerivatives 4.0 International License.

• General Communication & Media Studies

Winning The Room: Creating and Delivering an Effective Data-Driven Presentation

ISBN: 978-1-119-82310-0

February 2022

The Visual Display of Quantitative Information

clothbound

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Home Blog Design Understanding Data Presentations (Guide + Examples)

Understanding Data Presentations (Guide + Examples)

In this age of overwhelming information, the skill to effectively convey data has become extremely valuable. Initiating a discussion on data presentation types involves thoughtful consideration of the nature of your data and the message you aim to convey. Different types of visualizations serve distinct purposes. Whether you’re dealing with how to develop a report or simply trying to communicate complex information, how you present data influences how well your audience understands and engages with it. This extensive guide leads you through the different ways of data presentation.

Table of Contents

What is a Data Presentation?

What should a data presentation include, line graphs, treemap chart, scatter plot, how to choose a data presentation type, recommended data presentation templates, common mistakes done in data presentation.

A data presentation is a slide deck that aims to disclose quantitative information to an audience through the use of visual formats and narrative techniques derived from data analysis, making complex data understandable and actionable. This process requires a series of tools, such as charts, graphs, tables, infographics, dashboards, and so on, supported by concise textual explanations to improve understanding and boost retention rate.

Data presentations require us to cull data in a format that allows the presenter to highlight trends, patterns, and insights so that the audience can act upon the shared information. In a few words, the goal of data presentations is to enable viewers to grasp complicated concepts or trends quickly, facilitating informed decision-making or deeper analysis.

Data presentations go beyond the mere usage of graphical elements. Seasoned presenters encompass visuals with the art of data storytelling , so the speech skillfully connects the points through a narrative that resonates with the audience. Depending on the purpose – inspire, persuade, inform, support decision-making processes, etc. – is the data presentation format that is better suited to help us in this journey.

To nail your upcoming data presentation, ensure to count with the following elements:

• Clear Objectives: Understand the intent of your presentation before selecting the graphical layout and metaphors to make content easier to grasp.
• Engaging introduction: Use a powerful hook from the get-go. For instance, you can ask a big question or present a problem that your data will answer. Take a look at our guide on how to start a presentation for tips & insights.
• Structured Narrative: Your data presentation must tell a coherent story. This means a beginning where you present the context, a middle section in which you present the data, and an ending that uses a call-to-action. Check our guide on presentation structure for further information.
• Visual Elements: These are the charts, graphs, and other elements of visual communication we ought to use to present data. This article will cover one by one the different types of data representation methods we can use, and provide further guidance on choosing between them.
• Insights and Analysis: This is not just showcasing a graph and letting people get an idea about it. A proper data presentation includes the interpretation of that data, the reason why it’s included, and why it matters to your research.
• Conclusion & CTA: Ending your presentation with a call to action is necessary. Whether you intend to wow your audience into acquiring your services, inspire them to change the world, or whatever the purpose of your presentation, there must be a stage in which you convey all that you shared and show the path to staying in touch. Plan ahead whether you want to use a thank-you slide, a video presentation, or which method is apt and tailored to the kind of presentation you deliver.
• Q&A Session: After your speech is concluded, allocate 3-5 minutes for the audience to raise any questions about the information you disclosed. This is an extra chance to establish your authority on the topic. Check our guide on questions and answer sessions in presentations here.

Bar charts are a graphical representation of data using rectangular bars to show quantities or frequencies in an established category. They make it easy for readers to spot patterns or trends. Bar charts can be horizontal or vertical, although the vertical format is commonly known as a column chart. They display categorical, discrete, or continuous variables grouped in class intervals [1] . They include an axis and a set of labeled bars horizontally or vertically. These bars represent the frequencies of variable values or the values themselves. Numbers on the y-axis of a vertical bar chart or the x-axis of a horizontal bar chart are called the scale.

Real-Life Application of Bar Charts

Let’s say a sales manager is presenting sales to their audience. Using a bar chart, he follows these steps.

Step 1: Selecting Data

The first step is to identify the specific data you will present to your audience.

The sales manager has highlighted these products for the presentation.

• Product A: Men’s Shoes
• Product B: Women’s Apparel
• Product C: Electronics
• Product D: Home Decor

Step 2: Choosing Orientation

Opt for a vertical layout for simplicity. Vertical bar charts help compare different categories in case there are not too many categories [1] . They can also help show different trends. A vertical bar chart is used where each bar represents one of the four chosen products. After plotting the data, it is seen that the height of each bar directly represents the sales performance of the respective product.

It is visible that the tallest bar (Electronics – Product C) is showing the highest sales. However, the shorter bars (Women’s Apparel – Product B and Home Decor – Product D) need attention. It indicates areas that require further analysis or strategies for improvement.

Step 3: Colorful Insights

Different colors are used to differentiate each product. It is essential to show a color-coded chart where the audience can distinguish between products.

• Men’s Shoes (Product A): Yellow
• Women’s Apparel (Product B): Orange
• Electronics (Product C): Violet
• Home Decor (Product D): Blue

Bar charts are straightforward and easily understandable for presenting data. They are versatile when comparing products or any categorical data [2] . Bar charts adapt seamlessly to retail scenarios. Despite that, bar charts have a few shortcomings. They cannot illustrate data trends over time. Besides, overloading the chart with numerous products can lead to visual clutter, diminishing its effectiveness.

For more information, check our collection of bar chart templates for PowerPoint .

Line graphs help illustrate data trends, progressions, or fluctuations by connecting a series of data points called ‘markers’ with straight line segments. This provides a straightforward representation of how values change [5] . Their versatility makes them invaluable for scenarios requiring a visual understanding of continuous data. In addition, line graphs are also useful for comparing multiple datasets over the same timeline. Using multiple line graphs allows us to compare more than one data set. They simplify complex information so the audience can quickly grasp the ups and downs of values. From tracking stock prices to analyzing experimental results, you can use line graphs to show how data changes over a continuous timeline. They show trends with simplicity and clarity.

Real-life Application of Line Graphs

To understand line graphs thoroughly, we will use a real case. Imagine you’re a financial analyst presenting a tech company’s monthly sales for a licensed product over the past year. Investors want insights into sales behavior by month, how market trends may have influenced sales performance and reception to the new pricing strategy. To present data via a line graph, you will complete these steps.

First, you need to gather the data. In this case, your data will be the sales numbers. For example:

• January: $45,000
• February: $55,000
• March: $45,000
• April: $60,000
• May: $ 70,000
• June: $65,000
• July: $62,000
• August: $68,000
• September: $81,000
• October: $76,000
• November: $87,000
• December: $91,000

After choosing the data, the next step is to select the orientation. Like bar charts, you can use vertical or horizontal line graphs. However, we want to keep this simple, so we will keep the timeline (x-axis) horizontal while the sales numbers (y-axis) vertical.

Step 3: Connecting Trends

After adding the data to your preferred software, you will plot a line graph. In the graph, each month’s sales are represented by data points connected by a line.

Step 4: Adding Clarity with Color

If there are multiple lines, you can also add colors to highlight each one, making it easier to follow.

Line graphs excel at visually presenting trends over time. These presentation aids identify patterns, like upward or downward trends. However, too many data points can clutter the graph, making it harder to interpret. Line graphs work best with continuous data but are not suitable for categories.

For more information, check our collection of line chart templates for PowerPoint and our article about how to make a presentation graph .

A data dashboard is a visual tool for analyzing information. Different graphs, charts, and tables are consolidated in a layout to showcase the information required to achieve one or more objectives. Dashboards help quickly see Key Performance Indicators (KPIs). You don’t make new visuals in the dashboard; instead, you use it to display visuals you’ve already made in worksheets [3] .

Keeping the number of visuals on a dashboard to three or four is recommended. Adding too many can make it hard to see the main points [4]. Dashboards can be used for business analytics to analyze sales, revenue, and marketing metrics at a time. They are also used in the manufacturing industry, as they allow users to grasp the entire production scenario at the moment while tracking the core KPIs for each line.

Real-Life Application of a Dashboard

Consider a project manager presenting a software development project’s progress to a tech company’s leadership team. He follows the following steps.

Step 1: Defining Key Metrics

To effectively communicate the project’s status, identify key metrics such as completion status, budget, and bug resolution rates. Then, choose measurable metrics aligned with project objectives.

Step 2: Choosing Visualization Widgets

After finalizing the data, presentation aids that align with each metric are selected. For this project, the project manager chooses a progress bar for the completion status and uses bar charts for budget allocation. Likewise, he implements line charts for bug resolution rates.

Step 3: Dashboard Layout

Key metrics are prominently placed in the dashboard for easy visibility, and the manager ensures that it appears clean and organized.

Dashboards provide a comprehensive view of key project metrics. Users can interact with data, customize views, and drill down for detailed analysis. However, creating an effective dashboard requires careful planning to avoid clutter. Besides, dashboards rely on the availability and accuracy of underlying data sources.

For more information, check our article on how to design a dashboard presentation , and discover our collection of dashboard PowerPoint templates .

Treemap charts represent hierarchical data structured in a series of nested rectangles [6] . As each branch of the ‘tree’ is given a rectangle, smaller tiles can be seen representing sub-branches, meaning elements on a lower hierarchical level than the parent rectangle. Each one of those rectangular nodes is built by representing an area proportional to the specified data dimension.

Treemaps are useful for visualizing large datasets in compact space. It is easy to identify patterns, such as which categories are dominant. Common applications of the treemap chart are seen in the IT industry, such as resource allocation, disk space management, website analytics, etc. Also, they can be used in multiple industries like healthcare data analysis, market share across different product categories, or even in finance to visualize portfolios.

Real-Life Application of a Treemap Chart

Let’s consider a financial scenario where a financial team wants to represent the budget allocation of a company. There is a hierarchy in the process, so it is helpful to use a treemap chart. In the chart, the top-level rectangle could represent the total budget, and it would be subdivided into smaller rectangles, each denoting a specific department. Further subdivisions within these smaller rectangles might represent individual projects or cost categories.

Step 1: Define Your Data Hierarchy

While presenting data on the budget allocation, start by outlining the hierarchical structure. The sequence will be like the overall budget at the top, followed by departments, projects within each department, and finally, individual cost categories for each project.

• Top-level rectangle: Total Budget
• Second-level rectangles: Departments (Engineering, Marketing, Sales)
• Third-level rectangles: Projects within each department
• Fourth-level rectangles: Cost categories for each project (Personnel, Marketing Expenses, Equipment)

Step 2: Choose a Suitable Tool

It’s time to select a data visualization tool supporting Treemaps. Popular choices include Tableau, Microsoft Power BI, PowerPoint, or even coding with libraries like D3.js. It is vital to ensure that the chosen tool provides customization options for colors, labels, and hierarchical structures.

Here, the team uses PowerPoint for this guide because of its user-friendly interface and robust Treemap capabilities.

Step 3: Make a Treemap Chart with PowerPoint

After opening the PowerPoint presentation, they chose “SmartArt” to form the chart. The SmartArt Graphic window has a “Hierarchy” category on the left.  Here, you will see multiple options. You can choose any layout that resembles a Treemap. The “Table Hierarchy” or “Organization Chart” options can be adapted. The team selects the Table Hierarchy as it looks close to a Treemap.

Step 5: Input Your Data

After that, a new window will open with a basic structure. They add the data one by one by clicking on the text boxes. They start with the top-level rectangle, representing the total budget.  

Step 6: Customize the Treemap

By clicking on each shape, they customize its color, size, and label. At the same time, they can adjust the font size, style, and color of labels by using the options in the “Format” tab in PowerPoint. Using different colors for each level enhances the visual difference.

Treemaps excel at illustrating hierarchical structures. These charts make it easy to understand relationships and dependencies. They efficiently use space, compactly displaying a large amount of data, reducing the need for excessive scrolling or navigation. Additionally, using colors enhances the understanding of data by representing different variables or categories.

In some cases, treemaps might become complex, especially with deep hierarchies.  It becomes challenging for some users to interpret the chart. At the same time, displaying detailed information within each rectangle might be constrained by space. It potentially limits the amount of data that can be shown clearly. Without proper labeling and color coding, there’s a risk of misinterpretation.

A heatmap is a data visualization tool that uses color coding to represent values across a two-dimensional surface. In these, colors replace numbers to indicate the magnitude of each cell. This color-shaded matrix display is valuable for summarizing and understanding data sets with a glance [7] . The intensity of the color corresponds to the value it represents, making it easy to identify patterns, trends, and variations in the data.

As a tool, heatmaps help businesses analyze website interactions, revealing user behavior patterns and preferences to enhance overall user experience. In addition, companies use heatmaps to assess content engagement, identifying popular sections and areas of improvement for more effective communication. They excel at highlighting patterns and trends in large datasets, making it easy to identify areas of interest.

We can implement heatmaps to express multiple data types, such as numerical values, percentages, or even categorical data. Heatmaps help us easily spot areas with lots of activity, making them helpful in figuring out clusters [8] . When making these maps, it is important to pick colors carefully. The colors need to show the differences between groups or levels of something. And it is good to use colors that people with colorblindness can easily see.

Check our detailed guide on how to create a heatmap here. Also discover our collection of heatmap PowerPoint templates .

Pie charts are circular statistical graphics divided into slices to illustrate numerical proportions. Each slice represents a proportionate part of the whole, making it easy to visualize the contribution of each component to the total.

The size of the pie charts is influenced by the value of data points within each pie. The total of all data points in a pie determines its size. The pie with the highest data points appears as the largest, whereas the others are proportionally smaller. However, you can present all pies of the same size if proportional representation is not required [9] . Sometimes, pie charts are difficult to read, or additional information is required. A variation of this tool can be used instead, known as the donut chart , which has the same structure but a blank center, creating a ring shape. Presenters can add extra information, and the ring shape helps to declutter the graph.

Pie charts are used in business to show percentage distribution, compare relative sizes of categories, or present straightforward data sets where visualizing ratios is essential.

Real-Life Application of Pie Charts

Consider a scenario where you want to represent the distribution of the data. Each slice of the pie chart would represent a different category, and the size of each slice would indicate the percentage of the total portion allocated to that category.

Step 1: Define Your Data Structure

Imagine you are presenting the distribution of a project budget among different expense categories.

• Column A: Expense Categories (Personnel, Equipment, Marketing, Miscellaneous)
• Column B: Budget Amounts ($40,000, $30,000, $20,000, $10,000) Column B represents the values of your categories in Column A.

Step 2: Insert a Pie Chart

Using any of the accessible tools, you can create a pie chart. The most convenient tools for forming a pie chart in a presentation are presentation tools such as PowerPoint or Google Slides.  You will notice that the pie chart assigns each expense category a percentage of the total budget by dividing it by the total budget.

For instance:

• Personnel: $40,000 / ($40,000 + $30,000 + $20,000 + $10,000) = 40%
• Equipment: $30,000 / ($40,000 + $30,000 + $20,000 + $10,000) = 30%
• Marketing: $20,000 / ($40,000 + $30,000 + $20,000 + $10,000) = 20%
• Miscellaneous: $10,000 / ($40,000 + $30,000 + $20,000 + $10,000) = 10%

You can make a chart out of this or just pull out the pie chart from the data.

3D pie charts and 3D donut charts are quite popular among the audience. They stand out as visual elements in any presentation slide, so let’s take a look at how our pie chart example would look in 3D pie chart format.

Step 03: Results Interpretation

The pie chart visually illustrates the distribution of the project budget among different expense categories. Personnel constitutes the largest portion at 40%, followed by equipment at 30%, marketing at 20%, and miscellaneous at 10%. This breakdown provides a clear overview of where the project funds are allocated, which helps in informed decision-making and resource management. It is evident that personnel are a significant investment, emphasizing their importance in the overall project budget.

Pie charts provide a straightforward way to represent proportions and percentages. They are easy to understand, even for individuals with limited data analysis experience. These charts work well for small datasets with a limited number of categories.

However, a pie chart can become cluttered and less effective in situations with many categories. Accurate interpretation may be challenging, especially when dealing with slight differences in slice sizes. In addition, these charts are static and do not effectively convey trends over time.

For more information, check our collection of pie chart templates for PowerPoint .

Histograms present the distribution of numerical variables. Unlike a bar chart that records each unique response separately, histograms organize numeric responses into bins and show the frequency of reactions within each bin [10] . The x-axis of a histogram shows the range of values for a numeric variable. At the same time, the y-axis indicates the relative frequencies (percentage of the total counts) for that range of values.

Whenever you want to understand the distribution of your data, check which values are more common, or identify outliers, histograms are your go-to. Think of them as a spotlight on the story your data is telling. A histogram can provide a quick and insightful overview if you’re curious about exam scores, sales figures, or any numerical data distribution.

Real-Life Application of a Histogram

In the histogram data analysis presentation example, imagine an instructor analyzing a class’s grades to identify the most common score range. A histogram could effectively display the distribution. It will show whether most students scored in the average range or if there are significant outliers.

Step 1: Gather Data

He begins by gathering the data. The scores of each student in class are gathered to analyze exam scores.

Names	Score
Alice	78
Bob	85
Clara	92
David	65
Emma	72
Frank	88
Grace	76
Henry	95
Isabel	81
Jack	70
Kate	60
Liam	89
Mia	75
Noah	84
Olivia	92

After arranging the scores in ascending order, bin ranges are set.

Step 2: Define Bins

Bins are like categories that group similar values. Think of them as buckets that organize your data. The presenter decides how wide each bin should be based on the range of the values. For instance, the instructor sets the bin ranges based on score intervals: 60-69, 70-79, 80-89, and 90-100.

Step 3: Count Frequency

Now, he counts how many data points fall into each bin. This step is crucial because it tells you how often specific ranges of values occur. The result is the frequency distribution, showing the occurrences of each group.

Here, the instructor counts the number of students in each category.

• 60-69: 1 student (Kate)
• 70-79: 4 students (David, Emma, Grace, Jack)
• 80-89: 7 students (Alice, Bob, Frank, Isabel, Liam, Mia, Noah)
• 90-100: 3 students (Clara, Henry, Olivia)

Step 4: Create the Histogram

It’s time to turn the data into a visual representation. Draw a bar for each bin on a graph. The width of the bar should correspond to the range of the bin, and the height should correspond to the frequency.  To make your histogram understandable, label the X and Y axes.

In this case, the X-axis should represent the bins (e.g., test score ranges), and the Y-axis represents the frequency.

The histogram of the class grades reveals insightful patterns in the distribution. Most students, with seven students, fall within the 80-89 score range. The histogram provides a clear visualization of the class’s performance. It showcases a concentration of grades in the upper-middle range with few outliers at both ends. This analysis helps in understanding the overall academic standing of the class. It also identifies the areas for potential improvement or recognition.

Thus, histograms provide a clear visual representation of data distribution. They are easy to interpret, even for those without a statistical background. They apply to various types of data, including continuous and discrete variables. One weak point is that histograms do not capture detailed patterns in students’ data, with seven compared to other visualization methods.

A scatter plot is a graphical representation of the relationship between two variables. It consists of individual data points on a two-dimensional plane. This plane plots one variable on the x-axis and the other on the y-axis. Each point represents a unique observation. It visualizes patterns, trends, or correlations between the two variables.

Scatter plots are also effective in revealing the strength and direction of relationships. They identify outliers and assess the overall distribution of data points. The points’ dispersion and clustering reflect the relationship’s nature, whether it is positive, negative, or lacks a discernible pattern. In business, scatter plots assess relationships between variables such as marketing cost and sales revenue. They help present data correlations and decision-making.

Real-Life Application of Scatter Plot

A group of scientists is conducting a study on the relationship between daily hours of screen time and sleep quality. After reviewing the data, they managed to create this table to help them build a scatter plot graph:

Participant ID	Daily Hours of Screen Time	Sleep Quality Rating
1	9	3
2	2	8
3	1	9
4	0	10
5	1	9
6	3	7
7	4	7
8	5	6
9	5	6
10	7	3
11	10	1
12	6	5
13	7	3
14	8	2
15	9	2
16	4	7
17	5	6
18	4	7
19	9	2
20	6	4
21	3	7
22	10	1
23	2	8
24	5	6
25	3	7
26	1	9
27	8	2
28	4	6
29	7	3
30	2	8
31	7	4
32	9	2
33	10	1
34	10	1
35	10	1

In the provided example, the x-axis represents Daily Hours of Screen Time, and the y-axis represents the Sleep Quality Rating.

The scientists observe a negative correlation between the amount of screen time and the quality of sleep. This is consistent with their hypothesis that blue light, especially before bedtime, has a significant impact on sleep quality and metabolic processes.

There are a few things to remember when using a scatter plot. Even when a scatter diagram indicates a relationship, it doesn’t mean one variable affects the other. A third factor can influence both variables. The more the plot resembles a straight line, the stronger the relationship is perceived [11] . If it suggests no ties, the observed pattern might be due to random fluctuations in data. When the scatter diagram depicts no correlation, whether the data might be stratified is worth considering.

Choosing the appropriate data presentation type is crucial when making a presentation . Understanding the nature of your data and the message you intend to convey will guide this selection process. For instance, when showcasing quantitative relationships, scatter plots become instrumental in revealing correlations between variables. If the focus is on emphasizing parts of a whole, pie charts offer a concise display of proportions. Histograms, on the other hand, prove valuable for illustrating distributions and frequency patterns. 

Bar charts provide a clear visual comparison of different categories. Likewise, line charts excel in showcasing trends over time, while tables are ideal for detailed data examination. Starting a presentation on data presentation types involves evaluating the specific information you want to communicate and selecting the format that aligns with your message. This ensures clarity and resonance with your audience from the beginning of your presentation.

1. Fact Sheet Dashboard for Data Presentation

Convey all the data you need to present in this one-pager format, an ideal solution tailored for users looking for presentation aids. Global maps, donut chats, column graphs, and text neatly arranged in a clean layout presented in light and dark themes.

Use This Template

2. 3D Column Chart Infographic PPT Template

Represent column charts in a highly visual 3D format with this PPT template. A creative way to present data, this template is entirely editable, and we can craft either a one-page infographic or a series of slides explaining what we intend to disclose point by point.

3. Data Circles Infographic PowerPoint Template

An alternative to the pie chart and donut chart diagrams, this template features a series of curved shapes with bubble callouts as ways of presenting data. Expand the information for each arch in the text placeholder areas.

4. Colorful Metrics Dashboard for Data Presentation

This versatile dashboard template helps us in the presentation of the data by offering several graphs and methods to convert numbers into graphics. Implement it for e-commerce projects, financial projections, project development, and more.

5. Animated Data Presentation Tools for PowerPoint & Google Slides

A slide deck filled with most of the tools mentioned in this article, from bar charts, column charts, treemap graphs, pie charts, histogram, etc. Animated effects make each slide look dynamic when sharing data with stakeholders.

6. Statistics Waffle Charts PPT Template for Data Presentations

This PPT template helps us how to present data beyond the typical pie chart representation. It is widely used for demographics, so it’s a great fit for marketing teams, data science professionals, HR personnel, and more.

7. Data Presentation Dashboard Template for Google Slides

A compendium of tools in dashboard format featuring line graphs, bar charts, column charts, and neatly arranged placeholder text areas. 

8. Weather Dashboard for Data Presentation

Share weather data for agricultural presentation topics, environmental studies, or any kind of presentation that requires a highly visual layout for weather forecasting on a single day. Two color themes are available.

9. Social Media Marketing Dashboard Data Presentation Template

Intended for marketing professionals, this dashboard template for data presentation is a tool for presenting data analytics from social media channels. Two slide layouts featuring line graphs and column charts.

10. Project Management Summary Dashboard Template

A tool crafted for project managers to deliver highly visual reports on a project’s completion, the profits it delivered for the company, and expenses/time required to execute it. 4 different color layouts are available.

11. Profit & Loss Dashboard for PowerPoint and Google Slides

A must-have for finance professionals. This typical profit & loss dashboard includes progress bars, donut charts, column charts, line graphs, and everything that’s required to deliver a comprehensive report about a company’s financial situation.

Overwhelming visuals

One of the mistakes related to using data-presenting methods is including too much data or using overly complex visualizations. They can confuse the audience and dilute the key message.

Inappropriate chart types

Choosing the wrong type of chart for the data at hand can lead to misinterpretation. For example, using a pie chart for data that doesn’t represent parts of a whole is not right.

Lack of context

Failing to provide context or sufficient labeling can make it challenging for the audience to understand the significance of the presented data.

Inconsistency in design

Using inconsistent design elements and color schemes across different visualizations can create confusion and visual disarray.

Failure to provide details

Simply presenting raw data without offering clear insights or takeaways can leave the audience without a meaningful conclusion.

Lack of focus

Not having a clear focus on the key message or main takeaway can result in a presentation that lacks a central theme.

Visual accessibility issues

Overlooking the visual accessibility of charts and graphs can exclude certain audience members who may have difficulty interpreting visual information.

In order to avoid these mistakes in data presentation, presenters can benefit from using presentation templates . These templates provide a structured framework. They ensure consistency, clarity, and an aesthetically pleasing design, enhancing data communication’s overall impact.

Understanding and choosing data presentation types are pivotal in effective communication. Each method serves a unique purpose, so selecting the appropriate one depends on the nature of the data and the message to be conveyed. The diverse array of presentation types offers versatility in visually representing information, from bar charts showing values to pie charts illustrating proportions. 

Using the proper method enhances clarity, engages the audience, and ensures that data sets are not just presented but comprehensively understood. By appreciating the strengths and limitations of different presentation types, communicators can tailor their approach to convey information accurately, developing a deeper connection between data and audience understanding.

[1] Government of Canada, S.C. (2021) 5 Data Visualization 5.2 Bar Chart , 5.2 Bar chart .  https://www150.statcan.gc.ca/n1/edu/power-pouvoir/ch9/bargraph-diagrammeabarres/5214818-eng.htm

[2] Kosslyn, S.M., 1989. Understanding charts and graphs. Applied cognitive psychology, 3(3), pp.185-225. https://apps.dtic.mil/sti/pdfs/ADA183409.pdf

[3] Creating a Dashboard . https://it.tufts.edu/book/export/html/1870

[4] https://www.goldenwestcollege.edu/research/data-and-more/data-dashboards/index.html

[5] https://www.mit.edu/course/21/21.guide/grf-line.htm

[6] Jadeja, M. and Shah, K., 2015, January. Tree-Map: A Visualization Tool for Large Data. In GSB@ SIGIR (pp. 9-13). https://ceur-ws.org/Vol-1393/gsb15proceedings.pdf#page=15

[7] Heat Maps and Quilt Plots. https://www.publichealth.columbia.edu/research/population-health-methods/heat-maps-and-quilt-plots

[8] EIU QGIS WORKSHOP. https://www.eiu.edu/qgisworkshop/heatmaps.php

[9] About Pie Charts.  https://www.mit.edu/~mbarker/formula1/f1help/11-ch-c8.htm

[10] Histograms. https://sites.utexas.edu/sos/guided/descriptive/numericaldd/descriptiven2/histogram/ [11] https://asq.org/quality-resources/scatter-diagram

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Present Your Data Like a Pro

• Joel Schwartzberg

Demystify the numbers. Your audience will thank you.

While a good presentation has data, data alone doesn’t guarantee a good presentation. It’s all about how that data is presented. The quickest way to confuse your audience is by sharing too many details at once. The only data points you should share are those that significantly support your point — and ideally, one point per chart. To avoid the debacle of sheepishly translating hard-to-see numbers and labels, rehearse your presentation with colleagues sitting as far away as the actual audience would. While you’ve been working with the same chart for weeks or months, your audience will be exposed to it for mere seconds. Give them the best chance of comprehending your data by using simple, clear, and complete language to identify X and Y axes, pie pieces, bars, and other diagrammatic elements. Try to avoid abbreviations that aren’t obvious, and don’t assume labeled components on one slide will be remembered on subsequent slides. Every valuable chart or pie graph has an “Aha!” zone — a number or range of data that reveals something crucial to your point. Make sure you visually highlight the “Aha!” zone, reinforcing the moment by explaining it to your audience.

With so many ways to spin and distort information these days, a presentation needs to do more than simply share great ideas — it needs to support those ideas with credible data. That’s true whether you’re an executive pitching new business clients, a vendor selling her services, or a CEO making a case for change.

• JS Joel Schwartzberg oversees executive communications for a major national nonprofit, is a professional presentation coach, and is the author of Get to the Point! Sharpen Your Message and Make Your Words Matter and The Language of Leadership: How to Engage and Inspire Your Team . You can find him on LinkedIn and X. TheJoelTruth

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Data Storytelling: How to Effectively Tell a Story with Data

• 23 Nov 2021

When you hear the term “data analysis,” what do you think of? Your mind may jump to scouring spreadsheets, implementing algorithms, and making mathematical calculations—all “hard skills” of data analysis. Yet, hard skills are useless without their soft skill counterparts. It’s not enough to just analyze data; you need to know how to communicate the story it tells in a clear, compelling manner—a skill called data storytelling .

According to the Bureau of Labor Statistics , the demand for research analysts is expected to grow 25 percent between 2020 and 2030, much faster than the average across all industries. Many companies have begun including data storytelling as a required skill in analyst job descriptions, while others have opted to hire for data storyteller positions to supplement their existing analytics teams’ abilities. Possessing the skills to both analyze data and communicate its insights can help you stand out as a well-rounded candidate.

Here’s a primer on the key components of data storytelling, why storytelling is an impactful communication tool, and how to craft a compelling narrative of your own.

Access your free e-book today.

What Is Data Storytelling?

Data storytelling is the ability to effectively communicate insights from a dataset using narratives and visualizations. It can be used to put data insights into context for and inspire action from your audience.

There are three key components to data storytelling:

• Data: Thorough analysis of accurate, complete data serves as the foundation of your data story. Analyzing data using descriptive , diagnostic , predictive , and prescriptive analysis can enable you to understand its full picture.
• Narrative: A verbal or written narrative, also called a storyline, is used to communicate insights gleaned from data, the context surrounding it, and actions you recommend and aim to inspire in your audience.
• Visualizations: Visual representations of your data and narrative can be useful for communicating its story clearly and memorably. These can be charts, graphs, diagrams, pictures, or videos.

Data storytelling can be used internally (for instance, to communicate the need for product improvements based on user data) or externally (for instance, to create a compelling case for buying your product to potential customers).

Related: The Advantages of Data-Driven Decision-Making

The Psychological Power of Storytelling

Humans have told stories since the Cro-Magnon era to communicate with others for survival and record accounts of daily life. While storytelling methods have come a long way since the days of cave paintings, its psychological power holds true tens of thousands of years later.

The brain’s preference for stories over pure data stems from the fact that it takes in so much information every day and needs to determine what’s important to process and remember and what can be discarded.

When someone hears a story, multiple parts of the brain are engaged, including:

• Wernicke’s area, which controls language comprehension
• The amygdala, which processes emotional response
• Mirror neurons, which play a role in empathizing with others

When multiple areas of the brain are engaged, the hippocampus—which stores short-term memories—is more likely to convert the experience of hearing a story into a long-term memory.

Rather than presenting your team with a spreadsheet of data and rattling off numbers, consider how you can engage multiple parts of their brains. Using data storytelling, you can evoke an emotional response on a neural level that can help your points be remembered and acted upon.

How to Craft a Compelling Data Narrative

Data storytelling uses the same narrative elements as any story you’ve read or heard before: characters, setting, conflict, and resolution.

To help illustrate this, imagine you’re a data analyst and just discovered your company’s recent decline in sales has been driven by customers of all genders between the ages of 14 and 23. You find that the drop was caused by a viral social media post highlighting your company’s negative impact on the environment, and craft a narrative using the four key story elements:

• Characters: The players and stakeholders include customers between the ages of 14 and 23, environmentally conscious consumers, and your internal team. This doesn’t need to be part of your presentation, but you should define the key players for yourself beforehand.
• Setting: Set the scene by explaining there’s been a recent drop in sales driven by customers of all genders ages 14 to 23. Use a data visualization to show the decline across audience types and highlight the largest drop in young users.
• Conflict: Describe the root issue: A viral social media post highlighted your company’s negative impact on the environment and caused tens of thousands of young customers to stop using your product. Incorporate research (such as this article in the Harvard Business Review ) about how consumers are more environmentally conscious than ever and how sustainably-marketed products can potentially drive more revenue than their unsustainable counterparts. Remind the team of your company’s current unsustainable manufacturing practices to clarify why customers stopped purchasing your product. Use visualizations here, too.
• Resolution: Propose your solution. Based on this data, you present a long-term goal to pivot to sustainable manufacturing practices. You also center marketing and public relations efforts on making this pivot visible across all audience segments. Use visualizations that show the investment required for sustainable manufacturing practices can pay off in the form of earning customers from the growing environmentally conscious market segment.

If there isn’t a conflict in your data story—for instance, if the data showed your current marketing campaign was driving traffic and exceeding your goal—you can skip that element and go straight to recommending that the current course of action be maintained.

Whatever story the data tells, you can communicate it effectively by formatting your narrative with these elements and walking your audience through each piece with the help of visualizations.

Communicating the Need for Action

Data storytelling can help turn data insights into action. Without effective communication, insights can go unnoticed or unremembered by your audience; both hard and soft skills are crucial for leveraging data to its fullest potential.

Harvard Business School Professor Jan Hammond speaks to this in the online course Business Analytics , one of three courses that make up the Credential of Readiness (CORe) program.

“Always remember that applying analytical techniques to managerial problems requires both art and science,” Hammond says. “Over my career, I’ve learned that it’s the soft skills that are the hardest to master, but they’re critically important.”

Do you want to become a data-driven professional? Explore our eight-week Business Analytics course and our three-course Credential of Readiness (CORe) program to deepen your analytical skills and apply them to real-world business problems.

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Top 9 Presentation Design Books (2023)

Hey there, slide slayer! 🦸‍♂️ Looking for the perfect resource to level up your presentation design game? Look no further! I've compiled a list of the best (and my favorite) presentation design books that will turn your slides from 'meh' to 'WOW.' Say goodbye to boring presentations and hello to captivating, engaging visuals. Let's dive in!

What Are the Best Presentation Design Books?

I've handpicked these amazing books, packed with insights, tips, and techniques to help you create stunning presentations. Whether you're a new grad or a working professional, these books have got you covered. Let's explore them one by one.

Slide:ology: The Art and Science of Creating Great Presentations

Written by presentation expert Nancy Duarte, Slide:ology is a must-read for anyone looking to improve their slide design skills. This comprehensive guide covers everything from storytelling to slide layout and design principles.

What I Liked

• Step-by-step guidance on designing effective presentations
• Beautiful visuals and examples from well-known companies
• Great case studies highlighting the people
• Clear authority in the field since she designed Al Gore's famous climate change slide deck that was the basis for an Inconvenient Truth film
• Clear explanations of design concepts
• Focus on storytelling and audience engagement
• Good focus on timeless ideas
• The print book is beautifully designed
• More hands-on exercises and activities

Bottom Line

This book is perfect for anyone looking to master the art of creating visually stunning and impactful presentations, regardless of their design background.

Resonate: Present Visual Stories that Transform Audiences

Another gem from Nancy Duarte, Resonate focuses on the power of storytelling to create memorable and persuasive presentations. Learn how to connect with your audience and leave a lasting impact.

• Unique approach to visual storytelling
• Incorporation of narrative structure in presentations
• Emphasis on audience engagement and connection
• Variety of case studies from different fields
• Tips for effective slide design and layout
• Expanded section on slide design tools and resources

If you want to learn the art of storytelling in presentations and captivate your audience, this book is a fantastic choice.

DataStory: Explain Data and Inspire Action Through Story

DataStory, also by Nancy Duarte, is a valuable guide for those looking to effectively communicate complex data through storytelling. Discover how to turn numbers into compelling narratives that drive action.

What I Liked:

• Clear explanations of data visualization concepts
• Practical tips for transforming data into stories
• Examples from various industries
• Focus on audience understanding and decision-making
• Techniques for creating persuasive data-driven presentations
• More detailed guidance on data visualization tools
• Expanded coverage of data analysis techniques

Ideal for professionals working with data, this book will help you present complex information in a clear, engaging, and persuasive manner.

Everyday Business Storytelling: Create, Simplify, and Adapt A Visual Narrative for Any Audience

Authors Janine Kurnoff and Lee Lazarus provide practical guidance on using visual storytelling to create effective business presentations. Learn how to simplify complex ideas and adapt your message for any audience.

• Step-by-step process for crafting visual narratives
• Emphasis on audience-centered communication
• Real-world examples and case studies
• Tips for adapting messages to different audiences and contexts
• Techniques for simplifying complex information
• Plenty of examples with before and afters
• The section about email communication because not everything has to be a presentation
• Even more examples at the end

This book is perfect for business professionals who want to create engaging and persuasive visual narratives that resonate with any audience.

Storytelling with Data: A Data Visualization Guide for Business Professionals

Written by Cole Nussbaumer Knaflic, Storytelling with Data teaches readers how to present data in a clear and compelling way. Gain valuable insights on data visualization techniques and best practices for business professionals.

• In-depth coverage of data visualization best practices
• Focus on audience-centric presentation design
• Strategies for simplifying complex data
• Tips for selecting the most effective chart types
• Guidance on using color and design elements effectively
• Additional exercises and activities for hands-on practice

This book is ideal for business professionals who want to master data visualization and deliver clear, compelling data-driven presentations.

Good Charts: The HBR Guide to Making Smarter, More Persuasive Data Visualizations

Authored by Scott Berinato, Good Charts is a comprehensive guide to creating data visualizations that effectively communicate complex information. Learn best practices and techniques for creating persuasive and informative charts and graphs.

• Practical guidance for creating effective charts and graphs
• Harvard Business Review's credibility and expertise
• Tips for improving existing data visualizations
• Strategies for engaging and persuading audiences with data
• Expanded coverage of advanced visualization techniques

This book is perfect for professionals seeking to improve their data visualization skills and create more persuasive presentations using data.

Good Charts Workbook: Tips, Tools, and Exercises for Making Better Data Visualizations

A companion to Good Charts, the Good Charts Workbook by Scott Berinato offers hands-on exercises and practical tools to help you create better data visualizations. Improve your skills with engaging activities and real-world examples.

• Interactive exercises and activities
• Focus on hands-on learning and skill development
• Complements the concepts covered in Good Charts
• Tips for refining and improving existing visualizations
• Greater variety of data visualization tools covered
• Inclusion of digital resources and templates

Ideal for those looking to practice and improve their data visualization skills, this workbook is a valuable resource for creating more effective and persuasive data-driven presentations.

The Elegant Pitch: Create a Compelling Recommendation, Build Broad Support, and Get It Approved

Written by Mike Figliuolo, The Elegant Pitch provides a structured approach to crafting persuasive business recommendations. Learn how to build support and get your ideas approved with a clear, compelling pitch.

• Step-by-step process for creating persuasive pitches
• Focus on clarity and simplicity in communication
• Tips for overcoming objections and building support
• Techniques for tailoring your pitch to different audiences
• Well written book with very actionable advice
• More of a hidden gem that isn't talked about as much
• Greater emphasis on visual presentation techniques

This book is perfect for professionals who want to learn how to create compelling recommendations and get their ideas approved in a business setting.

Presentation Zen Design: Simple Design Principles and Techniques to Enhance Your Presentations

In Presentation Zen Design, Garr Reynolds shares his expertise on creating visually stunning and engaging presentations. Discover simple design principles and techniques to transform your slides and captivate your audience.

• Focus on clean, minimalist design principles
• Practical tips for improving slide design
• Real-world examples from various industries
• Strategies for incorporating visuals effectively
• Techniques for engaging and connecting with audiences
• Very culturally sensitive
• Eastern cultural themes and analogies
• An updated edition is needed because, visually, some of the examples feel very out of date

This book is ideal for those looking to enhance their presentation design skills and create visually engaging slides that resonate with their audience.

So there you have it, my top picks for the best presentation design books. Whether you're just starting or want to polish your skills, these books will help you create captivating, engaging slides that leave a lasting impression.

Ready to get started? Grab one of these books and watch your presentations transform! 🚀

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1st Edition

Presenting Your Data with SPSS Explained

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Description

Data Presentation with SPSS Explained provides students with all the information they need to conduct small scale analysis of research projects using SPSS and present their results appropriately in their reports. Quantitative data can be collected in the form of a questionnaire, survey or experimental study. This book focuses on presenting this data clearly, in the form of tables and graphs, along with creating basic summary statistics. Data Presentation with SPSS Explained uses an example survey that is clearly explained step-by-step throughout the book. This allows readers to follow the procedures, and easily apply each step in the process to their own research and findings. No prior knowledge of statistics or SPSS is assumed, and everything in the book is carefully explained in a helpful and user-friendly way using worked examples. This book is the perfect companion for students from a range of disciplines including psychology, business, communication, education, health, humanities, marketing and nursing – many of whom are unaware that this extremely helpful program is available at their institution for their use.

Table of Contents

Perry Hinton is a psychologist and has taught the use of SPSS to a wide range of students from disciplines including communication and media, education, linguistics, nursing, psychology and social work. He is joint author of SPSS Explained , also published by Routledge. His own research is on interpersonal perception and he has written three books on the topic. Isabella McMurray is a senior lecturer in psychology. She has taught SPSS to students in psychology, applied social studies and nursing and is joint author of SPSS Explained , also published by Routledge. Isabella undertakes consultancy, training and research with local authorities and charities, including working with social workers, probation services, parenting programmes, education and road safety teams.　 　

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Introducing Apple Intelligence, the personal intelligence system that puts powerful generative models at the core of iPhone, iPad, and Mac

New Capabilities for Understanding and Creating Language

Image Playground Makes Communication and Self‑Expression Even More Fun

Genmoji Creation to Fit Any Moment

New Features in Photos Give Users More Control

Siri Enters a New Era

A New Standard for Privacy in AI

ChatGPT Gets Integrated Across Apple Platforms

Text of this article

June 10, 2024

PRESS RELEASE

Setting a new standard for privacy in AI, Apple Intelligence understands personal context to deliver intelligence that is helpful and relevant

CUPERTINO, CALIFORNIA Apple today introduced Apple Intelligence , the personal intelligence system for iPhone, iPad, and Mac that combines the power of generative models with personal context to deliver intelligence that’s incredibly useful and relevant. Apple Intelligence is deeply integrated into iOS 18, iPadOS 18, and macOS Sequoia. It harnesses the power of Apple silicon to understand and create language and images, take action across apps, and draw from personal context to simplify and accelerate everyday tasks. With Private Cloud Compute, Apple sets a new standard for privacy in AI, with the ability to flex and scale computational capacity between on-device processing and larger, server-based models that run on dedicated Apple silicon servers.

“We’re thrilled to introduce a new chapter in Apple innovation. Apple Intelligence will transform what users can do with our products — and what our products can do for our users,” said Tim Cook, Apple’s CEO. “Our unique approach combines generative AI with a user’s personal context to deliver truly helpful intelligence. And it can access that information in a completely private and secure way to help users do the things that matter most to them. This is AI as only Apple can deliver it, and we can’t wait for users to experience what it can do.”

Apple Intelligence unlocks new ways for users to enhance their writing and communicate more effectively. With brand-new systemwide Writing Tools built into iOS 18, iPadOS 18, and macOS Sequoia, users can rewrite, proofread, and summarize text nearly everywhere they write, including Mail, Notes, Pages, and third-party apps.

Whether tidying up class notes, ensuring a blog post reads just right, or making sure an email is perfectly crafted, Writing Tools help users feel more confident in their writing. With Rewrite, Apple Intelligence allows users to choose from different versions of what they have written, adjusting the tone to suit the audience and task at hand. From finessing a cover letter, to adding humor and creativity to a party invitation, Rewrite helps deliver the right words to meet the occasion. Proofread checks grammar, word choice, and sentence structure while also suggesting edits — along with explanations of the edits — that users can review or quickly accept. With Summarize, users can select text and have it recapped in the form of a digestible paragraph, bulleted key points, a table, or a list.

In Mail, staying on top of emails has never been easier. With Priority Messages, a new section at the top of the inbox shows the most urgent emails, like a same-day dinner invitation or boarding pass. Across a user’s inbox, instead of previewing the first few lines of each email, they can see summaries without needing to open a message. For long threads, users can view pertinent details with just a tap. Smart Reply provides suggestions for a quick response, and will identify questions in an email to ensure everything is answered.

Deep understanding of language also extends to Notifications. Priority Notifications appear at the top of the stack to surface what’s most important, and summaries help users scan long or stacked notifications to show key details right on the Lock Screen, such as when a group chat is particularly active. And to help users stay present in what they’re doing, Reduce Interruptions is a new Focus that surfaces only the notifications that might need immediate attention, like a text about an early pickup from daycare.

In the Notes and Phone apps, users can now record, transcribe, and summarize audio. When a recording is initiated while on a call, participants are automatically notified, and once the call ends, Apple Intelligence generates a summary to help recall key points.

Apple Intelligence powers exciting image creation capabilities to help users communicate and express themselves in new ways. With Image Playground, users can create fun images in seconds, choosing from three styles: Animation, Illustration, or Sketch. Image Playground is easy to use and built right into apps including Messages. It’s also available in a dedicated app, perfect for experimenting with different concepts and styles. All images are created on device, giving users the freedom to experiment with as many images as they want.

With Image Playground, users can choose from a range of concepts from categories like themes, costumes, accessories, and places; type a description to define an image; choose someone from their personal photo library to include in their image; and pick their favorite style.

With the Image Playground experience in Messages, users can quickly create fun images for their friends, and even see personalized suggested concepts related to their conversations. For example, if a user is messaging a group about going hiking, they’ll see suggested concepts related to their friends, their destination, and their activity, making image creation even faster and more relevant.

In Notes, users can access Image Playground through the new Image Wand in the Apple Pencil tool palette, making notes more visually engaging. Rough sketches can be turned into delightful images, and users can even select empty space to create an image using context from the surrounding area. Image Playground is also available in apps like Keynote, Freeform, and Pages, as well as in third-party apps that adopt the new Image Playground API.

Taking emoji to an entirely new level, users can create an original Genmoji to express themselves. By simply typing a description, their Genmoji appears, along with additional options. Users can even create Genmoji of friends and family based on their photos. Just like emoji, Genmoji can be added inline to messages, or shared as a sticker or reaction in a Tapback.

Searching for photos and videos becomes even more convenient with Apple Intelligence. Natural language can be used to search for specific photos, such as “Maya skateboarding in a tie-dye shirt,” or “Katie with stickers on her face.” Search in videos also becomes more powerful with the ability to find specific moments in clips so users can go right to the relevant segment. Additionally, the new Clean Up tool can identify and remove distracting objects in the background of a photo — without accidentally altering the subject.

With Memories, users can create the story they want to see by simply typing a description. Using language and image understanding, Apple Intelligence will pick out the best photos and videos based on the description, craft a storyline with chapters based on themes identified from the photos, and arrange them into a movie with its own narrative arc. Users will even get song suggestions to match their memory from Apple Music. As with all Apple Intelligence features, user photos and videos are kept private on device and are not shared with Apple or anyone else.

Powered by Apple Intelligence, Siri becomes more deeply integrated into the system experience. With richer language-understanding capabilities, Siri is more natural, more contextually relevant, and more personal, with the ability to simplify and accelerate everyday tasks. It can follow along if users stumble over words and maintain context from one request to the next. Additionally, users can type to Siri, and switch between text and voice to communicate with Siri in whatever way feels right for the moment. Siri also has a brand-new design with an elegant glowing light that wraps around the edge of the screen when Siri is active.

Siri can now give users device support everywhere they go, and answer thousands of questions about how to do something on iPhone, iPad, and Mac. Users can learn everything from how to schedule an email in the Mail app, to how to switch from Light to Dark Mode.

With onscreen awareness, Siri will be able to understand and take action with users’ content in more apps over time. For example, if a friend texts a user their new address in Messages, the receiver can say, “Add this address to his contact card.”

With Apple Intelligence, Siri will be able to take hundreds of new actions in and across Apple and third-party apps. For example, a user could say, “Bring up that article about cicadas from my Reading List,” or “Send the photos from the barbecue on Saturday to Malia,” and Siri will take care of it.

Siri will be able to deliver intelligence that’s tailored to the user and their on-device information. For example, a user can say, “Play that podcast that Jamie recommended,” and Siri will locate and play the episode, without the user having to remember whether it was mentioned in a text or an email. Or they could ask, “When is Mom’s flight landing?” and Siri will find the flight details and cross-reference them with real-time flight tracking to give an arrival time.

To be truly helpful, Apple Intelligence relies on understanding deep personal context while also protecting user privacy. A cornerstone of Apple Intelligence is on-device processing, and many of the models that power it run entirely on device. To run more complex requests that require more processing power, Private Cloud Compute extends the privacy and security of Apple devices into the cloud to unlock even more intelligence.

With Private Cloud Compute, Apple Intelligence can flex and scale its computational capacity and draw on larger, server-based models for more complex requests. These models run on servers powered by Apple silicon, providing a foundation that allows Apple to ensure that data is never retained or exposed.

Independent experts can inspect the code that runs on Apple silicon servers to verify privacy, and Private Cloud Compute cryptographically ensures that iPhone, iPad, and Mac do not talk to a server unless its software has been publicly logged for inspection. Apple Intelligence with Private Cloud Compute sets a new standard for privacy in AI, unlocking intelligence users can trust.

Apple is integrating ChatGPT access into experiences within iOS 18, iPadOS 18, and macOS Sequoia, allowing users to access its expertise — as well as its image- and document-understanding capabilities — without needing to jump between tools.

Siri can tap into ChatGPT’s expertise when helpful. Users are asked before any questions are sent to ChatGPT, along with any documents or photos, and Siri then presents the answer directly.

Additionally, ChatGPT will be available in Apple’s systemwide Writing Tools, which help users generate content for anything they are writing about. With Compose, users can also access ChatGPT image tools to generate images in a wide variety of styles to complement what they are writing.

Privacy protections are built in for users who access ChatGPT — their IP addresses are obscured, and OpenAI won’t store requests. ChatGPT’s data-use policies apply for users who choose to connect their account.

ChatGPT will come to iOS 18, iPadOS 18, and macOS Sequoia later this year, powered by GPT-4o. Users can access it for free without creating an account, and ChatGPT subscribers can connect their accounts and access paid features right from these experiences.

Availability

Apple Intelligence is free for users, and will be available in beta as part of iOS 18 , iPadOS 18 , and macOS Sequoia  this fall in U.S. English. Some features, software platforms, and additional languages will come over the course of the next year. Apple Intelligence will be available on iPhone 15 Pro, iPhone 15 Pro Max, and iPad and Mac with M1 and later, with Siri and device language set to U.S. English. For more information, visit apple.com/apple-intelligence .

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The Visual Display of Quantitative Information, 2nd Ed. 2nd Edition

• ISBN-10 9780961392147
• ISBN-13 978-0961392147
• Edition 2nd
• Publisher Graphics Pr
• Publication date January 14, 1997
• Language English
• Dimensions 11 x 9 x 1 inches
• Print length 197 pages
• See all details

Product details

• ASIN ‏ : ‎ 0961392142
• Publisher ‏ : ‎ Graphics Pr; 2nd edition (January 14, 1997)
• Language ‏ : ‎ English
• Hardcover ‏ : ‎ 197 pages
• ISBN-10 ‏ : ‎ 9780961392147
• ISBN-13 ‏ : ‎ 978-0961392147
• Item Weight ‏ : ‎ 2.1 pounds
• Dimensions ‏ : ‎ 11 x 9 x 1 inches
• #2 in Mathematical Analysis (Books)
• #12 in Statistics (Books)
• #14 in Probability & Statistics (Books)

About the author

Edward r. tufte.

Statistician/visualizer/artist Edward Tufte is Professor Emeritus of Political Science, Statistics, and Computer Science at Yale University. He wrote, designed, and self-published 5 classic books on data visualization.

The New York Times described Tufte as the "Leonardo da Vinci of data," and Bloomberg as the "Galileo of graphics."

Having completed his book Seeing With Fresh Eyes: Meaning, Space, Data, Truth, ET is now constructing a 234-acre tree farm and sculpture park in northwest Connecticut, which will show his artworks and remain open space in perpetuity.

He founded Graphics Press, ET Modern Gallery/Studio, and Hogpen Hill Farms.

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