Data visualization is a powerful field that transforms raw data into compelling visual stories. My journey into this fascinating world began with a curiosity about the universe, transitioning from astronomy to the art of visual data storytelling. Over the years, I’ve accumulated insights and practical tips that have significantly shaped my skills and career as a data visualization designer. While everyone’s path is unique, I hope sharing my experiences and advice can be a helpful guide as you Learn Data Visualization and navigate your own journey.
My Path into Data Visualization Design
For those interested in a deeper dive into my background, I’ve previously detailed my somewhat unconventional journey from studying astronomy to becoming a freelance data visualization designer. That blog post, “My Journey into Dataviz“, includes early projects and reflects the initial steps I took in this field.
My freelancing career officially started in 2017, and since then, I’ve gained invaluable experience navigating the world of self-employment in data visualization. If you’re curious about the lessons I’ve learned specifically from freelancing, you might find interviews I’ve given to be insightful resources. They offer a deeper look into the practicalities of building a career as a freelance data visualization professional.
Practical Tips for Mastering Data Visualization
One of the most frequent questions I receive is from individuals seeking advice on improving their data visualization skills and potentially forging a career in this domain. While I can only offer my perspective, here’s my comprehensive advice for anyone looking to learn data visualization and enhance their abilities.
1. Discover Your Data Visualization Niche
While data visualization itself is often considered a specialized field, the reality is that it encompasses a wide spectrum of specializations. From the precision of medical illustrations to the dynamic interactivity of dashboards, and the artistic expression of data art, the possibilities are vast.
When you’re new to data visualization, it’s crucial to explore and identify the type of data visualization that genuinely resonates with you. Which visuals capture your attention and inspire you to create? Once you pinpoint these areas, investigate the tools used to create them. This might involve exploring software like d3.js, Tableau, R, or Flourish. Understanding the tools behind your favorite visuals will provide crucial direction, as technology, whether it’s coding or traditional art supplies, is fundamental to bringing your visualizations to life.
When I decide to learn a new technology to create a specific visual style, I usually begin with beginner tutorials or books. Just enough to grasp the basics and create something simple. From there, I leverage online resources like Stack Overflow and community forums to learn progressively, working on personal projects to solidify my understanding. This approach has been effective for me with d3.js (D3.js in Depth), Three.js (Learn Three.js – Third Edition), Blender (Blender Donut Tutorial), and GLSL shaders (The Book of Shaders).
If you’re unsure about your niche, experiment with various tools and approaches. Trial and error is a valuable part of the learning process, helping you discover what truly aligns with your interests and strengths.
2. Master Data Visualization Best Practices
Human perception is not always intuitive, and effective data visualization requires understanding how we actually see and interpret visual information. Relying solely on gut feeling can lead to misleading or ineffective visuals. Therefore, learning established best practices is crucial – it’s about “knowing the rules before you break them.”
Books are invaluable resources for in-depth knowledge, research findings, and expert guidelines in data visualization. Well-edited books written by experts offer a wealth of insights that are hard to find elsewhere. For recommended reading, explore my curated list of data visualization books that I’ve reviewed and enjoyed here. These resources can significantly enhance your understanding of visual perception, design principles, and effective data storytelling.
3. Practice, Practice, Practice: Create a Lot of Visuals
If there’s one piece of advice that stands above all else, it’s this: create, create, create. Consistent practice is the most effective way to hone your skills and deepen your understanding of what works and what doesn’t in data visualization.
If you’re new to this field, start with smaller projects. Try replicating existing visuals that you admire. Then, substitute the original data with your own datasets and experiment with color palettes. With each new visualization, progressively modify more aspects, building upon your previous learning and experiences.
However, be prepared for a commitment of time and effort. Especially when starting out, you might find that your current job doesn’t offer much opportunity to practice data visualization. This often means dedicating personal time – evenings and weekends – to developing your skills. Perseverance and self-discipline are essential. Improving requires dedicated work, there’s no shortcut.
In my first year of seriously pursuing data visualization, I dedicated nearly all my free time to it. From reading books and writing tutorials to working on personal projects, data visualization became my primary focus. While the intensity lessened after about a year, it wasn’t until I transitioned to freelancing, dedicating my workdays to visualizations, that I regained more balance and could pursue other hobbies in the evenings. This isn’t to say this is the only path, but it illustrates that mastering data visualization demands significant dedication.
Initially, your work might be more imitative than original. That’s a natural part of the learning process. However, aim to evolve from being a copycat to a remixer as quickly as possible. Strive to create visuals that, while potentially inspired by others, clearly reflect your own design choices and unique style. Only share your work publicly and claim it as your own once you reach this level of creative remixing and personal expression.
4. Choose Topics That Truly Captivate You
Personal interest is a powerful motivator. For me, the ability to spend hours on personal projects stems from choosing topics that I am genuinely passionate about. Whether it’s exoplanets, the Lord of the Rings, fantasy literature, or Dragon Ball Z, my projects are fueled by genuine enthusiasm. I avoid readily available, but often less engaging, statistical datasets from government websites.
Being a fan of my chosen topics gives me an insider perspective. I often incorporate “easter eggs” or subtle details that fellow enthusiasts will appreciate.
Finding data for niche topics might require more effort, but the payoff is always worth it. My enthusiasm for the subject fuels my work, making the visualization process a joy. I invariably learn new things about the topic itself, and my pre-existing knowledge allows me to identify compelling stories and highlight relevant aspects within the data.
You might initially believe that “there’s no data for the topics I like,” but through numerous personal projects on niche subjects, I’ve consistently found valuable data. The key is to be open to data in various forms, not just neatly packaged spreadsheets.
For example, unsurprisingly, websites dedicated to exoplanets offer comprehensive datasets. I discovered a hidden gem in a GitHub repository: a dataset of all words spoken in the Lord of the Rings movies (found by simply Googling “data Lord of the Rings”). Goodreads’ API provides access to a wealth of information about fantasy books (which I used with R scripts and a list of top fantasy authors scraped from Amazon). And for Dragon Ball, fan wikis meticulously list every fight sequence from the anime, which I copy-pasted into Excel and manually cleaned.
5. Build a Strong Portfolio (and Your Own Website)
Creating personal projects not only enhances your skills but also builds a portfolio – a crucial asset in a visual field like data visualization. Showcasing your capabilities and style through a portfolio is invaluable for attracting clients.
While platforms like Behance are useful, I strongly recommend establishing your own website as your primary portfolio.
Your website doesn’t need to be elaborate at the start (my first website was a free Google blog). The important thing is to have a dedicated space to display your work. After completing each project, take the time to add it to your portfolio page. You don’t need to be a coding expert to create a website; services like Squarespace, Webflow, or even a well-curated Behance page are excellent starting points.
In 2016/2017, I dedicated my evenings to a major personal project, Data Sketches, a collaborative endeavor. While I already had a decent portfolio, Data Sketches significantly expanded my online presence and became a pivotal stepping stone in launching my freelancing career and attracting clients.
As your skills evolve and your portfolio grows, remember to curate it regularly. Remove older projects that no longer represent your current abilities or the type of work you want to pursue. Avoid showcasing skills you no longer wish to utilize professionally! Your portfolio should be a dynamic reflection of your best and most desired work.
6. Decide: Design Skills or Data Skills First?
The ideal starting point depends on your existing skill set.
Individuals with a strong design background should prioritize learning data handling and preparation. If you discover a passion for the data side, consider pursuing data science courses. I personally recommend MOOCs (Massive Open Online Courses) from reputable universities. The Harvard edX Data Science course was particularly impactful for me. If data still feels daunting, “The Truthful Art” by Alberto Cairo (The Truthful Art) is an excellent resource, covering essential data and basic statistics relevant to data visualization.
Regardless of your chosen visualization tools, learning either R or Python is highly recommended for statistical analysis and data cleaning. While manual data preparation in Excel might seem convenient initially, it becomes unsustainable as you grow as a data visualization creator. While exceptions exist (some excellent visualizers don’t code), programming skills, especially in R or Python, are incredibly beneficial for data manipulation and analysis.
My personal preference is R, although I’ve used Python when necessary.
If programming is new to you, Python might be a good starting point due to its versatility and wider applications beyond data visualization. However, if you’re completely new to programming, R is generally considered easier to learn and use, particularly for statistical tasks.
Think “steal like an artist,” but always aim for originality, not mere imitation.
Conversely, if you have a data-oriented background, like mine in astronomy and data science, focus on developing your design skills. Here, my advice is less prescriptive. My design approach largely stems from striving for aesthetics that I personally find appealing and professional. This is coupled with a developed sense of visual discernment – knowing when something looks good and when it doesn’t. I draw inspiration from others’ work, deconstructing what elements resonate with me – is it the color palette? The shapes? – and then try to incorporate those elements into my future projects.
Just like mastering data visualization tools and data handling, improving design skills requires dedicated effort. The design phase often consumes a significant portion of my project time, involving numerous iterations as I refine the visual until I’m satisfied with its appearance. This iterative process draws upon my experience from hundreds of previous visualizations. My early visuals were far from polished, but each project has been a learning experience, progressively guiding me towards creating more effective and aesthetically pleasing visualizations.
If you lack both design and data skills, and are unsure where to begin, it can feel overwhelming. In this case, I believe a foundational understanding of data handling is more crucial. We are data visualization designers, not solely graphic designers. Without the ability to handle data, you’re not fully equipped. However, while you’re developing data skills, integrate data visualization practice to keep things engaging. If a course or tutorial asks you to simply clean a dataset, challenge yourself to also create a visualization from some aspect of that data. Don’t just use the default chart options; strive for a professional look, something you’d be proud to showcase. Balance your learning by focusing primarily on data skills while incorporating design practice to maintain interest and develop a holistic skillset.
7. Join the Data Visualization Society
The Data Visualization Society is an invaluable resource that didn’t exist when I started. Membership is free, connecting you with thousands of data visualization enthusiasts worldwide. Join their Slack channel for ongoing conversations, ask questions, or simply observe and learn. Subscribe to their newsletter for free access to Nightingale articles. Participate in challenges, consider speaking at the Outlier conference, and explore the many other opportunities they offer. Connecting with fellow data visualization professionals, even virtually, and becoming part of this community will undoubtedly amplify your passion for the field.
Bonus Tip: Curate Your Inspiration
This is a highly personal but effective technique for me. I meticulously curate my Pinterest boards with any visual inspiration I encounter throughout the year. Anything I find aesthetically pleasing, whether data visualization related or from completely different domains like space or spirographs, finds its place on my boards.
When starting the design phase of a new project, I create a secret “client mood board.” I select relevant Pinterest boards, browse through them, and pin anything that I feel might offer design inspiration. With this mood board open during my sketching and design process, I have a constant source of visual ideas and starting points.
Data Visualization Book Recommendations
During my initial years exploring data visualization, books were instrumental in my learning journey. They significantly helped me grasp best practices for creating effective visualizations, particularly in areas like visual perception. In my experience, well-written books offer a depth of knowledge that surpasses online resources. On this page, you’ll find my reviews and reflections on various data visualization books. I hope these reviews help you identify books that will further your learning in data visualization.
My Data Visualization Design and Creation Process
While each project is unique, influenced by diverse clients and data topics, my overall process generally follows a consistent flow: from data understanding to initial design to iterative creation, with back-and-forth communication throughout.
1. Understand the Project Goals
I always begin by clarifying the client’s objectives for the visualization. What key insights need to be revealed? What should the audience learn or be persuaded of? Many clients have a clear vision. Some have an idea but need help articulating it concisely. We then collaborate to refine their goals into specific questions or insights. Other clients have rich datasets but need assistance in uncovering potential narratives. In these cases, a thorough data preparation & analysis phase is crucial for me to explore the data and identify compelling angles for visualization.
For example, Physics Today commissioned me to create a visualization for the Hubble Space Telescope’s 30th anniversary. They provided access to a database of 500,000 scientific observations. The data was undoubtedly rich, but the specific story needed to be unearthed. I delved into the dataset, exploring various perspectives to eventually distill it into four potential story angles, from which we selected two to develop further.
2. Understand the Data Itself
Concurrently with understanding the client’s needs, I focus on gaining a comprehensive understanding of the data. What variables are available? How complete is the data? What is the data volume? I always request to see the data upfront to get a feel for what I’ll be working with. I never work with dummy data, as it has consistently proven to be detrimental. Therefore, I require either a representative sample or, ideally, the complete dataset before commencing any project. Since my visualizations are data-driven and not reliant on pre-designed graphical elements, a substantial and diverse dataset is essential for creating compelling visuals. The data itself is my primary creative tool.
3. Develop Initial Rough Designs
With a clear understanding of the project goals and the data, I move into the design phase. I always start with sketching my designs, using pen and paper or my iPad Pro with Apple Pencil. On the iPad, I prefer the Tayasui Sketches app, which offers a balance of features without being overwhelming like more complex applications.
This design phase often overlaps with initial data exploration (if not already done), to better understand the range of values for each variable and visualize potential insights. I generate numerous simple plots – bar charts, histograms, line charts, scatterplots – to build a mental model of the dataset. I primarily use R for these analyses and visualizations, leveraging packages like tidyverse and ggplot2.
My sketches are intentionally rough – far from pixel-perfect designs. This is because data visualization design is intrinsically linked to the data. At this stage, I focus minimally on aesthetics like colors or layout. The primary focus is on how to translate numerical data into visual encodings. Should I use a radial layout? Connecting lines? Size-based visual marks?
Once I’ve exhausted my visualization ideas, I present these sketches to the client, outlining my rationale, pros, and cons for each design concept. We discuss and decide on a direction to proceed. I then restructure the data in R to align with the chosen design and move to the next phase.
4. Create the Data Visualization
This phase is typically the most time-consuming part of any project: building the actual data visualization. I always program my visuals to seamlessly link data attributes to visual elements, like connecting a data variable to the size of a shape. Programming also allows for easy modifications once the foundation is built. Changing data mappings – for instance, switching variables for size and color – can be done in minutes, regardless of dataset size. Furthermore, programming ensures effortless updates when data changes occur; simply update the data file, and the visualization automatically reflects the changes.
I heavily utilize d3.js functionalities and work in Visual Studio Code with Chrome or Firefox devTools. D3.js provides the flexibility to precisely shape my visuals. While it might not be the fastest setup, the enhanced creative freedom is paramount for me.
Although d3.js is my core tool for preparing visuals, I might use SVGs, HTML5 Canvas, or webGL for rendering on screen, depending on dataset size and technical requirements. Each rendering option has distinct strengths and weaknesses.
Regardless of the rendering method, my first step is always to structure the data to match the chosen design. If the data aligns well with the design, I then focus on enhancing the visual’s effectiveness, engagement, and aesthetics. If the initial data integration reveals design-data incompatibility, I explore compromises or inform the client that an alternative approach is necessary.
Once the abstract data shapes are on screen and functional, I enter what I call the “endless iteration cycle.” At this stage, the visual is usually rudimentary, and transforming it into something captivating and attention-grabbing is rarely straightforward. It’s an iterative process of trial and error until I achieve a satisfactory outcome. While I can’t fully articulate or structure this chaotic process, it’s fundamental to my creative workflow.
During this iteration phase, I establish a hidden URL to showcase the latest visual iteration and share it with the client for continuous feedback. This iterative feedback loop allows for early course correction, client-driven adjustments, and ensures that the final visualization aligns with expectations and receives client approval throughout the development process.
Upon finalizing the visualization, I either package it as a reusable function for client-side integration or, for static visuals, export it from the browser to Illustrator for final refinements like legends and annotations, before delivering the final image files.
5. Personal Projects: A Slightly Different Approach
The process for personal projects largely mirrors the client-driven workflow, with a key addition at the beginning: idea generation and data sourcing (typically online). As my own client, I have complete autonomy – no approvals needed, no style guides to adhere to. This freedom allows for more experimental, out-of-the-box concepts and the opportunity to explore new tools and techniques.
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