Best Practices for Visual Data Representation in Science

I used to think colorful charts helped display information. Now I know they do exactly the opposite. When it comes to data visualization, color *is* crucial. But not in the way you’ve likely been taught. The general rule of thumb is that you should use color sparingly and strategically. In other words, never use color for the sake of being color*ful*. Here’s how: First, identify your core colors (I recommend 1-2 max): Option 1 ↳ Use your company’s (or client’s) brand colors. This is often the easiest and best choice. (But remember, you don’t have to use *all* the brand colors.) Option 2 ↳ Use an online color palette (check out the resources linked in the comments to get started). I’ve also searched Pinterest for things like “blue and green color palettes.” Second, follow best practices: Use grey as your default.  ↳ Create all your charts in greyscale first. Then, incorporate color to draw your audience’s eyes to the most important takeaways or data points. Use 1-2 core colors throughout your presentation.  ↳ Use your core colors to highlight the specific trends, categories, or insights you want your audience to pay attention to. Be aware of cultural associations.  ↳ Color symbolism varies across the globe - for example, red often carries a negative connotation in Western cultures, but represents luck and prosperity in Eastern/Asian cultures. Be mindful of color blindness.  ↳ Approximately 8% of men and 0.5% of women are colorblind (red-green being the most common). In general, less is more. Imagine someone were to look at your chart and say “Why is THAT particular bar blue? Why is THAT one green?” If you can’t give a clear answer, it's time to go back to the drawing board. —-— 👋🏼 I’m Morgan. I share my favorite data viz and data storytelling tips to help other analysts (and academics) better communicate their work.

Choosing the right chart is half the battle in data storytelling. This one visual helped me go from “𝐖𝐡𝐢𝐜𝐡 𝐜𝐡𝐚𝐫𝐭 𝐝𝐨 𝐈 𝐮𝐬𝐞?” → “𝐆𝐨𝐭 𝐢𝐭 𝐢𝐧 10 𝐬𝐞𝐜𝐨𝐧𝐝𝐬.”👇 𝐇𝐞𝐫𝐞’𝐬 𝐚 𝐪𝐮𝐢𝐜𝐤 𝐛𝐫𝐞𝐚𝐤𝐝𝐨𝐰𝐧 𝐨𝐟 𝐡𝐨𝐰 𝐭𝐨 𝐜𝐡𝐨𝐨𝐬𝐞 𝐭𝐡𝐞 𝐫𝐢𝐠𝐡𝐭 𝐜𝐡𝐚𝐫𝐭 𝐛𝐚𝐬𝐞𝐝 𝐨𝐧 𝐲𝐨𝐮𝐫 𝐝𝐚𝐭𝐚: 🔹 𝐂𝐨𝐦𝐩𝐚𝐫𝐢𝐬𝐨𝐧? • Few categories → Bar Chart • Over time → Line Chart • Multivariate → Spider Chart • Non-cyclical → Vertical Bar Chart 🔹 𝐑𝐞𝐥𝐚𝐭𝐢𝐨𝐧𝐬𝐡𝐢𝐩? • 2 variables → Scatterplot • 3+ variables → Bubble Chart 🔹 𝐃𝐢𝐬𝐭𝐫𝐢𝐛𝐮𝐭𝐢𝐨𝐧? • Single variable → Histogram • Many points → Line Histogram • 2 variables → Violin Plot 🔹 𝐂𝐨𝐦𝐩𝐨𝐬𝐢𝐭𝐢𝐨𝐧? • Show part of a total → Pie Chart / Tree Map • Over time → Stacked Bar / Area Chart • Add/Subtract → Waterfall Chart 𝐐𝐮𝐢𝐜𝐤 𝐓𝐢𝐩𝐬: • Don’t overload charts; less is more. • Always label axes clearly. • Use color intentionally, not decoratively. • 𝐀𝐬𝐤: What insight should this chart unlock in 5 seconds or less? 𝐑𝐞𝐦𝐞𝐦𝐛𝐞𝐫: • Charts don’t just show data, they tell a story • In storytelling, clarity beats complexity • Don’t aim to impress with fancy visuals, aim to express the insight simply, that’s where the real impact is 💡 ♻️ Save it for later or share it with someone who might find it helpful! 𝐏.𝐒. I share job search tips and insights on data analytics & data science in my free newsletter. Join 14,000+ readers here → https://lnkd.in/dUfe4Ac6

Do you want your data to make a difference? Transform your numbers into narratives that drive action—follow these five key steps: 📌 STEP 1: understand the context Before creating any visual, ask: - Who is your audience? - What do they need to know? - How will they use this information? Getting the context right ensures your message resonates. 📊 STEP 2: choose an appropriate graph Different visuals serve different purposes: - Want to compare values? Try a bar chart. - Showing trends? Use a line graph. - Need part-to-whole context? A stacked bar may work. Pick the right tool for the job! 🧹 STEP 3: declutter your graphs & slides More isn’t better. Remove unnecessary elements (gridlines, redundant labels, clutter) to let your data breathe. Less distraction = clearer communication. 🎯 STEP 4: focus attention Not all elements on your graphs and slides are equal. Use: ✔️ Color ✔️ Annotations ✔️ Positioning …to guide your audience’s eyes to what matters most. Help them know where to look and what to see. 📖 STEP 5: tell a story Numbers alone don’t inspire action—stories do. Structure your communication like a narrative: 1️⃣ Set the scene 2️⃣ Introduce the conflict (tension) 3️⃣ Lead to resolution (insight or action) Make it memorable! THAT'S the *storytelling with data* process! ✨ Following these five steps will help you create clear, compelling data stories. What's your favorite tip or strategy for great graphs and powerful presentations? Let us know in the comments!

Want to create better dataviz? Before you call your next data visualization complete, make sure its passes these three tests: 1. The Spartan Test: Strip it down. Ruthlessly assess every element in your chart. If removing something doesn’t change the message, it’s clutter. Clear visuals build trust — give your audience only what they need. 2. The Peek Test: Look away for 5 seconds, then glance back at your visual. Where does your eye go first? Chances are, that’s where your audience will focus too. Adjust until attention is drawn to the key insight. 3. The Colleague Test: Think it’s perfect? Share it with a colleague who hasn’t seen the analysis. Provide minimal context and give them 10-15 seconds to interpret. Ask what they take away — does it match your intent? Nail these three, and your data visualization will not just look good — it will communicate clearly and effectively. Three passing grades means it's ready to be presented. Art+Science Analytics Institute | University of Notre Dame | University of Notre Dame - Mendoza College of Business | University of Illinois Urbana-Champaign | University of Chicago | D'Amore-McKim School of Business at Northeastern University | ELVTR | Grow with Google - Data Analytics #Analytics #DataStorytelling