#datamustread: Visualize This: The FlowingData Guide to Design, Visualization, and Statistics (2nd Edition) by Nathan Yau

A bookshelf neatly arranged with several books on data visualization and analytics: Displayed in the center is the 2nd edition of "Visualize This: The FlowingData Guide to Design, Visualization, and Statistics" by Nathan Yau. Surrounding this book are various other titles, including those by the Alexander Loth: "Decisively Digital", "Teach Yourself VISUALLY Power BI", "Visual Analytics with Tableau", "Datenvisualisierung mit Tableau", "Datenvisualisierung mit Power BI", and "KI für Content Creation." Other visible titles include "Rewired" and "Self-Service BI & Analytics." The arrangement highlights a strong focus on data visualization, analytics, and AI.
The 2nd edition of Visualize This by Nathan Yau, surrounded by several influential data and AI books, including my own works like Decisively Digital and Teach Yourself Visually Power BI.

While my latest book, KI für Content Creation, has just been reviewed by the renowned c’t magazine, I’m happy to continue reviewing books myself. Today, I’m reviewing the just-released second edition of a cornerstone of the data visualization community, Visualize This: The FlowingData Guide to Design, Visualization, and Statistics by Nathan Yau.

Visualize This: A Deep Dive into Data Visualization

Nathan Yau’s Visualize This has long been a staple for data enthusiasts, and the updated second edition brings fresh techniques, technologies, and examples that reflect the rapidly evolving landscape of data visualization.

Core Highlights of This Book

Data-First Approach: Yau emphasizes that effective visualizations start with a deep understanding of the data. This foundational principle ensures that the resulting graphics are not just visually appealing but also accurately convey the underlying information.

Diverse Toolkit: The book introduces a wide range of tools, including the latest R packages, Python libraries, JavaScript libraries, and illustration software. Yau’s pragmatic approach helps readers choose the right tool for the job without feeling overwhelmed by options.

Real-World Applications: With practical, hands-on examples using real-world datasets, readers learn to create meaningful visualizations. This experiential learning approach is particularly valuable for grasping the subtleties of data representation.

Comprehensive Tutorials: The step-by-step guides are a standout feature, covering statistical graphics, geographical maps, and information design. These tutorials provide clear, actionable instructions that make complex visualizations accessible.

Web and Print Design: Yau details how to create visuals suitable for various mediums, ensuring versatility in application whether for digital platforms or printed materials.

Personal Insights on Visualize This

Having taught data strategy and visualization for seven years, I find Visualize This to be an exceptional resource for a broad audience. Yau skillfully integrates scientific data visualization techniques with graphic design principles, providing practical advice along the way. The book’s toolkit is extensive, featuring R, Illustrator, XML, Python (with BeautifulSoup), JSON, and more, each with working code examples to demonstrate real-world applications.

The image shows an open page from the second edition of "Visualize This: The FlowingData Guide to Design, Visualization, and Statistics" by Nathan Yau. The page, from Chapter 5 titled "Visualizing Categories," features a colorful visualization titled "Cycle of Many," which depicts a 24-hour snapshot of daily activities based on data from the American Time Use Survey. This visual highlights how categories change over time and demonstrates the book's practical approach to data visualization.
Visualize This featuring a colorful 24-hour activity visualization based on data from the American Time Use Survey.

Even though I read the first edition years ago, I couldn’t put the second edition down all weekend. This book is a must-read for anyone who handles data or prepares data-based reports. Its beautiful presentation and careful consideration of every aspect—from typeface to page layout—make it a pleasure to read.

The book is user-friendly, offering a massive set of references and free tools for obtaining interesting datasets across various fields, from sports to politics to health. This breadth of resources is crucial for anyone looking to create impactful visualizations across different domains.

While the focus on Adobe Illustrator might be daunting due to its cost and learning curve, Yau’s examples show how Illustrator can enhance graphics created in other tools like SAS and R. I personally prefer the open-source Inkscape, but Yau’s insights helped me overcome my initial reluctance to use Illustrator, leading to more polished and professional visuals.

Yau uses R, Python, and Adobe Illustrator to demonstrate what can be achieved with imagination and creativity. Although some readers might desire more complex walkthroughs from raw data to final graphics, such material would require substantial foundational knowledge in R and Python. Including this would make the book significantly thicker and veer off from its focus on creating visually appealing graphics.

Conclusion: Visualize This is Essential Reading for Data Professionals

Visualize This (Amazon) is an indispensable guide for anyone serious about data visualization. Its methodical, data-first approach, combined with practical tutorials and a comprehensive toolkit, makes it a must-read for information designers, analysts, journalists, statisticians, and data scientists.

For those looking to refine their data visualization skills and create compelling, accurate graphics, this book offers invaluable insights and techniques.

Connect with me on LinkedIn and Twitter for more reviews and insights on the latest in data and AI! #datamustread

Unlocking the Power of Data Science with Excel: Discover the Book „Data Smart“

Exploring the depths of Data Science with Excel: A glimpse into 'Data Smart' by Jordan Goldmeier, a must-read for data enthusiasts.
Exploring the depths of Data Science with Excel: A glimpse into ‚Data Smart‘ by Jordan Goldmeier, a must-read for data enthusiasts.

Data Smart (Amazon) is an exceptional guide that creatively uses Microsoft Excel to teach data science, making complex concepts accessible to business professionals. This 2nd edition, masterfully updated by Jordan Goldmeier, arrives a decade after John Foreman’s highly acclaimed original version, bringing fresh perspectives and contemporary insights to the renowned first edition.

Whether you’re a novice or a seasoned analyst, this book provides valuable insight and skill enhancement without requiring extensive programming knowledge. The practical, problem-solving approach ensures that you not only understand the theory, but also how to apply it in real-world scenarios. That’s why I’ve chosen Data Smart as our latest pick for the #datamustread book club.

Why „Data Smart“ is a #datamustread

Data Smart stands out in the realm of data science literature. Its approachable and practical methodology is a breath of fresh air for business professionals and data enthusiasts alike. Here’s why this book is an indispensable resource:

1. Excel as Your Data Science Laboratory:
The use of Excel, a tool many of us are familiar with, to unravel data science concepts is nothing short of brilliant. This approach significantly flattens the learning curve, making complex techniques more digestible.

2. Practical Learning through Real Business Problems:
Each chapter of the book introduces a different data science technique via a relatable business scenario. This context-driven approach makes the learning experience tangible and immediately applicable.

3. No Programming, No Problem:
The author’s method of teaching data science without delving into programming languages makes the content accessible to a broader audience.

4. Excel Skills Elevated:
In addition to data science concepts, readers will enhance their Excel prowess with advanced tools like Power Query and Excel Tables.

5. A Spectrum of Techniques:
From cluster analysis to forecasting, the book covers a wide array of methods, making it a comprehensive toolkit for any aspiring data scientist.

6. Fresh Perspectives in the Second Edition:
Goldmeier’s updates are not just cosmetic; they incorporate the latest Excel features, ensuring the content remains relevant in today’s fast-paced tech landscape.

Bridging the Gap with „Teach Yourself VISUALLY Power BI“

While exploring Data Smart, you’ll find parallels with the insights shared in my own book, Teach Yourself VISUALLY Power BI. Both texts aim to make data analytics accessible and actionable, providing a solid foundation for anyone looking to make informed decisions based on data.

Your Journey into Data Science Awaits

Data Smart is a gateway to understanding data science through a familiar and powerful tool: Excel. Whether you’re a beginner or a seasoned analyst, this book will enhance your analytical skills and expand your understanding of data in the business world.

Order Data Smart today and support both the authors and my endeavors in bringing such valuable resources to our community. Let’s dive into this journey of discovery together, transforming data into actionable insights.

Join the Conversation

After delving into Data Smart, I’d love to hear your thoughts and takeaways. Share your insights and join the discussion in our vibrant #datamustread community on LinkedIn and Twitter:

„Unlocking the Power of Data Science with Excel: Discover the Book „Data Smart““ weiterlesen

Newsletter: Data & AI Digest #2

Generated with DALL-E
Generated with DALL-E

👋 Hello Data & AI Enthusiasts,

Welcome to another edition of the Data & AI Digest! We’re excited to bring you a curated selection of the week’s most compelling stories in the realm of data science, artificial intelligence, and more. Whether you’re a seasoned expert or a curious beginner, there’s something here for everyone.

  1. [AI] Understanding AI Performance: Discover how modern AI models often match or exceed human capabilities in tests, yet struggle in real-world applications. Read more
  2. [AI] Generative AI Strategy for Tech Leaders: CIOs and CTOs need to integrate generative AI into their tech architecture effectively. Explore 5 key elements for successful implementation. Read more
  3. [Statistics] Mastering the Central Limit Theorem in R: Understand the Central Limit Theorem, a cornerstone in statistics, and learn how to simulate it using R in this step-by-step tutorial. Read more
  4. [Graph Theory] Comprehensive Introduction to Graph Theory: This quarter-long course covers everything from simple graphs to Eulerian circuits and spanning trees. Read more
  5. [SQL] SQL Konferenz Highlights on Microsoft Fabric: Get an in-depth look at Microsoft Fabric and its role as a Data Platform for the Era of AI. Read more
  6. [Microsoft] Forbes Insights on Microsoft’s Copilots: Learn six critical things every business owner should know about Microsoft Copilot. Read more
  7. [GitHub] How GitHub’s Copilot is Being Used: GitHub’s Copilot remains the most popular AI-based code completion service. Find out the latest usage trends. Read more
  8. [Apple] iPhone 15 Pro’s Spatial Videos: Teased at Apple’s latest keynote, learn about the new spatial video capabilities of the iPhone 15 Pro. Read more
  9. [Geopolitics] China’s AI Influence Campaign: Researchers from Microsoft and other organizations discuss Beijing’s rapid change in disinformation tactics through AI. Read more

That’s a wrap for this week’s Data & AI Digest! We hope you found these articles insightful and thought-provoking. If you enjoyed this issue, help us make it bigger and better by sharing it with colleagues and friends. 🚀

Don’t forget, for real-time updates and discussions, join our LinkedIn Data & AI User Group. We look forward to your active participation and valuable insights.

Get the Data & AI Digest newsletter delivered to your email weekly.

Until next week, happy reading and exploring!

Newsletter: Data & AI Digest #1

Generated with DALL-E
Generated with DALL-E

Hello and welcome to the first issue of Data & AI Digest! We’ve curated an exceptional list of articles that delve into a wide array of topics—from the triumphs of data visualization in public health to the ethical dilemmas surrounding AI-generated images. Are you curious about running Python directly in Excel? We’ve got you covered!

  • [dataviz] The Triumph Over Tobacco: A Public Health Milestone: Explore how a blend of regulation, taxation, and education led to a significant decline in cigarette sales and lung cancer deaths in the U.S. Read more
  • [dataviz] Master the Art of Data Visualization with These Must-Read Books: Whether you’re a novice or a pro, discover five essential books that guide you to data visualization mastery. Read more
  • [analytics] Python Meets Excel: A New Era in Data Analysis: Announcing Python support in Microsoft Excel, enabling data analysis directly within the Excel grid—no separate Python installation needed. Read more
  • [powerbi] Unlock Power BI’s Full Potential with DAX: Discover 20 essential DAX tricks to enhance your Power BI reports, suitable for both beginners and experts. Read more
  • [ethics] Controversy Over ‚SmashOrPassAI‘ Site: A new site that allows users to rate AI-generated women sparks backlash, raising ethical concerns. Read more
  • [public opinion] Rising Concerns Over AI’s Role in Daily Life: A new Pew Research survey reveals growing apprehension among Americans about the role of AI in daily life, with views varying by age and use cases. Read more
  • [privacy] GDPR Complaint Against OpenAI Over ChatGPT: OpenAI faces allegations of GDPR violations regarding its ChatGPT model, as filed by a privacy researcher. Read more
  • [coding] Meet Code Llama: A New Large Language Model for Coding: Meta is introducing Code Llama, a state-of-the-art large language model designed to assist with coding tasks. Read more

We hope you find this week’s digest both informative and inspiring. Enjoy the newsletter? Help us make it bigger and better by sharing it with colleagues and friends. For even more curated content, discussions, and networking opportunities, don’t forget to join our LinkedIn Data & AI User Group.

Get the Data & AI Digest newsletter delivered to your email weekly.

Stay curious and keep innovating!

Introducing „Data & AI Digest“: Your Essential AI Newsletter for Weekly Insights on Data Science and Artificial Intelligence

Data & AI Digest - the AI newsletter (image generated with DALL-E)
Data & AI Digest – the AI newsletter (image generated with DALL-E)

Why You Can’t Afford to Miss the „Data & AI Digest“

In today’s fast-paced digital landscape, keeping up with the ever-changing worlds of Data Science and Artificial Intelligence (AI) can be challenging. From breakthroughs in machine learning algorithms to ethical debates surrounding AI, the scope of what you need to know is vast and continuously expanding.

Enter the Data & AI Digest, a weekly newsletter curated to keep you updated on the most pertinent news, articles, and discussions in the realms of data and AI. Whether you are a seasoned professional, a student, or simply an enthusiast, this newsletter is designed with you in mind.

„Introducing „Data & AI Digest“: Your Essential AI Newsletter for Weekly Insights on Data Science and Artificial Intelligence“ weiterlesen