Best Practices for Visual Data Representation in Science

Bad data visualization is everywhere — here’s how to fix it. Understanding the essentials of effective data visualization is one thing, but witnessing poor data visualization in practice offers the real lessons. Take a look at this chart, “How Baby Boomers Describe Themselves,” which had some fundamental errors. The major problem? It disregards the rule of relativity. The design implies the data forms a complete whole, yet the percentages total 243%. This clearly indicates the wrong visual format was selected. If respondents could choose multiple answers, the data should be shown as a grouped bar chart rather than being forced into a single human figure. Additionally, contrast is mishandled: • Size contrast is deceptive – Larger sections don’t correlate with larger values. • Color contrast is excessive – Every section demands attention, causing nothing to stand out. • Shape contrast is absent – The chart depends solely on color to distinguish categories, reducing clarity. • Annotations cause confusion – Instead of providing clarity, extra design elements divert attention from the main insights. So, how to fix it? Opt for the correct visual structure, use proportional sizes, apply color contrast wisely, introduce meaningful shape variations, and ensure annotations are purposeful. Bad data visualization doesn’t just appear cluttered. It misleads. Correcting it involves directing the audience to the right insights without making it a struggle. 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

Data science or data analytics without storytelling is void. You can do all the SQL, all the Python, all the modeling — but if the final insight is not communicated in the right visual form, the value is lost. This cheat sheet is a perfect reminder that choosing the right chart is not decoration — it is part of analysis. It breaks the decision down by purpose of insight: 1) Composition Waterfall, Progress bar, Pie, Gauge — great when you want to show parts contributing to a whole or target progress. 2) Comparison Bar charts, Row charts, Line charts, Combo charts — useful when comparing categories or trends over time. 3) Distribution & Relationship Histogram and Scatter plot — when you want to show how values are spread or how two variables interact. 4) Stage Analysis Sankey and Funnel — ideal for visualizing drop-offs or flow across process stages. 5) Single Value KPIs Number & Trend cards — best for dashboards where one metric needs to stand out with context. The skill is not in plotting a chart — the skill is in selecting the correct one for the question being asked. Your analysis is only as powerful as the clarity of how you present it. cc Metabase #DataAnalytics #DataScience #DataVisualization #StorytellingWithData #BI #Metabase #DashboardDesign #DecisionMaking

𝐈 𝐮𝐬𝐞𝐝 𝐭𝐨 𝐭𝐡𝐢𝐧𝐤 𝐝𝐚𝐭𝐚 𝐯𝐢𝐬𝐮𝐚𝐥𝐢𝐳𝐚𝐭𝐢𝐨𝐧 𝐰𝐚𝐬 𝐣𝐮𝐬𝐭 𝐚𝐛𝐨𝐮𝐭 𝐦𝐚𝐤𝐢𝐧𝐠 𝐜𝐡𝐚𝐫𝐭𝐬… 𝐮𝐧𝐭𝐢𝐥 𝐈 𝐫𝐞𝐚𝐥𝐢𝐳𝐞𝐝 𝐈 𝐰𝐚𝐬 𝐝𝐨𝐢𝐧𝐠 𝐢𝐭 𝐚𝐥𝐥 𝐰𝐫𝐨𝐧𝐠. When I first started with data visualization, I thought it was just about making pretty charts. But I quickly realized that true mastery lies in telling a story with data turning raw numbers into insights that drive real decisions. If you’re looking to level up your data visualization skills, here’s the structured path I followed (and continue refining every day): 1️⃣ Build a Strong Foundation 🔹Understand why we visualize data - clarity and decision-making over aesthetics. 🔹Learn chart selection - when to use bar charts, line graphs, heatmaps, or scatter plots. 🔹Master the basics of color theory, contrast, and accessibility to make visuals effective for all audiences. 2️⃣ Get Hands-On with the Right Tools 🔹 Beginner: Excel, Google Sheets (Great for understanding core visualization concepts) 🔹 Intermediate: Tableau, Power BI (Essential for dashboards and interactivity) 🔹 Advanced: Python (Matplotlib, Seaborn, Plotly) & R (ggplot2) for full customization and automation 3️⃣ Learn to Tell a Story 🔹A great visualization isn’t just about good design, it’s about answering the right questions. 🔹Focus on context: Who is your audience? What action should they take? 🔹Follow frameworks like “Who, What, Why, How” to structure your storytelling. 4️⃣ Practice, Share, Get Feedback 🔹Recreate visualizations from reports and dashboards you admire. Join communities like #DataVizChallenge, or share your work on LinkedIn. 🔹Get feedback and iterate your first draft is never your best! 5️⃣ Stay Inspired & Keep Learning 🔹Read books like Storytelling with Data and The Truthful Art. 🔹Explore real-world dashboards and case studies to see how pros do it. Data visualization is both an art and a science. The more you practice, the more intuitive it becomes. I’d love to hear what’s your biggest challenge in mastering data visualization? Let’s discuss in the comments! 🚀 #DataVisualization #DataStorytelling #BusinessIntelligence #Analytics #LearnWithMe #CareerGrowth #StorytellingWithData #DashboardDesign #PowerBI #Tableau #Python #DataDriven

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

10 reasons why your dashboard lacks clarity. A - Don't put everything in one dashboard. => A dashboard made for everyone, is a dashboard used by no one. B - Help users see, not read. => "Good data visualization takes the burden of effort off the brain and puts it on the eyes." Stephen Few's C - Don’t use maps if they’re not relevant. => Even if your colleague worked so hard to get these ZIP codes. Ask yourself : Does the map add value to the business? D - Zoom in when necessary. => Sometimes (for specific reason) you'll need to truncate your axis. Because Usain Bolt has no intention of running the 100m in under 7 seconds. E - Declutter your charts. => It's a constant balance between space optimization and chart comprehension. F - Use double encoding on purpose. => Displaying the same KPI twice in the same chart may raise questions you don't want to hear during the kick-off meeting. Keep it clear. G - Rotate your charts to see full labels. => "My neck has been hurting lately, but I'm not sure why." H - Clean your pie chart. => Pie charts are hard enough to understand quickly, so let's not make them even trickier. I - Use aggregation to your advantage. => If your message is clear with 36 bars, why use 156? J - Use color to your advantage => The purpose of color is not to make your dashboard funky, but to attract the eye, to alert and to assist readability... Find this High Resolution visual + 50 other in the Dataviz Clarity Gallery here : https://lnkd.in/eThSWtWv #Businessintelligence #Datavisualization #DataAnalytics

📊💡 Mastering Data Visualization: Tips for Clear and Compelling Presentation In today's data-driven world, effective data visualization is key to conveying insights and driving decision-making. As data analysts, we understand the power of information. But presenting that data in a way that is not only clear but also compelling is an art form in itself. Here are some tips and best practices for mastering data visualization: 1. **Know Your Audience**: Before diving into visualization, understand who you're presenting to and what they care about. Tailor your visualizations to their level of expertise and interests. 2. **Simplify Complex Data**: Complexity can overwhelm and obscure your message. Simplify your visualizations by focusing on the most important insights. 3. **Choose the Right Visualization Type**: Different types of data lend themselves to different visualization formats. Choose the visualization type that best conveys your message and makes it easy for your audience to understand. 4. **Emphasize Key Insights**: Use visual cues to draw attention to the most important insights in your data. 5. **Tell a Story with Your Data**: Structure your visualizations in a logical sequence that leads your audience from problem to insight to action. 6. **Iterate and Solicit Feedback**: Data visualization is an iterative process. Continuous refinement based on feedback will help you create more effective and impactful visualizations over time. Tools such as Tableau, Power BI, and Python libraries like Matplotlib and Seaborn can be incredibly useful in creating visually stunning and informative visualizations. The real magic happens when you combine technical expertise with a keen eye for design and storytelling. Let's continue to harness the power of data visualization to unlock insights, tell compelling stories, and drive decision-making in our organizations. 🚀💻 #datavisualization #analytics