Data Visualization Techniques That Work

“Show me your data plot!” That was the first thing the professor said when I tried to explain my ML model in graduate school at MIT. Not the accuracy. Not the loss curve. Not the architecture. The plot. Over time, I realized, visualization is not the final step of machine learning. It is the first one. Before we build anything we need to understand what we are working with. And to understand it, we need to see it. This week, I taught a lecture on data visualization for ML using Matplotlib, Seaborn, and Plotly on Vizuara's YouTube channel: https://lnkd.in/dQTQYccT We walked through a complete exploratory data analysis (EDA) pipeline, starting with foundational charts and ending with interactive, dynamic visualizations. And through that, I was reminded of a principle I often forget: A good plot does not just summarize your data. A good plot changes what you believe about your data. You are not always building models for yourself. You are building for a client, reviewer or even a policymaker. They will not read your code. They may not understand your metrics. But they will look at your plots. Visualization is what makes machine learning interpretable - not only to others, but to you. And that matters more than ever. -A boxplot reveals whether a feature is skewed. -A scatterplot shows whether it separates your classes. -A correlation heatmap tells you what is redundant. -A violin plot raises questions about fairness. The stack: Matplotlib, Seaborn, Plotly Each of the three libraries plays a different role in the data visualization journey. 1) matplotlib: The bedrock. Sometimes verbose, but it gives you full control. Perfect for plotting model metrics, trends, and comparisons. Think of it as the NumPy of plotting. 2) seaborn: Statistical plotting done right. One-liner plots that look beautiful and convey distributions, relationships, and groups instantly. Use it for EDA - where every plot leads to a new hypothesis. 3) plotly: The bridge to interaction. If you want to share a story, demo a dataset, or explore it dynamically - this is the tool. Interactive histograms, 3D scatter plots, tooltips on hover. Especially powerful for explaining your work to non-technical stakeholders. Data visualization is not about being fancy. It is about being thoughtful. If you cannot explain your dataset visually, you are not ready to model it. If you cannot explain your model’s results visually, you are not ready to defend it. No one ever changed their mind because of an F1-score. But stunning plot? Those make people pause. Those change narratives. As ML gets more complex - with deeper models, larger datasets, and higher stakes - our ability to communicate clearly will matter more, not less. So if you are starting out in ML - start here. Learn to see before you try to predict. The plots will tell you where to go.

🔍 𝗥𝗲𝘁𝗵𝗶𝗻𝗸𝗶𝗻𝗴 𝗗𝗮𝘁𝗮 𝗣𝗿𝗲𝘀𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻: 𝗕𝗲𝘆𝗼𝗻𝗱 𝗣𝗶𝗲 𝗖𝗵𝗮𝗿𝘁𝘀 𝗮𝗻𝗱 𝗚𝗿𝗮𝗽𝗵𝘀! 🎨💡 We’ve all seen the same old pie charts, bar graphs, and line charts. But what if we could present technical information and data in more engaging, creative, and memorable ways? The world of data visualization is evolving, and it's time to break out of the traditional chart mindset! Here are some fresh approaches to presenting technical information through illustrations that will captivate and inform: 𝗜𝗻𝗳𝗼𝗴𝗿𝗮𝗽𝗵𝗶𝗰𝘀: Think of it as the storytelling of data! Infographics combine design, icons, and illustrations to visually guide the audience through complex concepts in a clear, compelling way. They’re perfect for summarizing large amounts of information at a glance. 🖼️📊 𝗗𝗮𝘁𝗮-𝗱𝗿𝗶𝘃𝗲𝗻 𝗜𝗹𝗹𝘂𝘀𝘁𝗿𝗮𝘁𝗶𝗼𝗻𝘀: Instead of a simple bar graph, why not use illustrated elements that represent the data? For instance, using icons, animated figures, or custom illustrations to show how data plays out in real-world scenarios. This method makes abstract numbers feel more tangible and human. 👩💻🌍 𝗜𝗻𝘁𝗲𝗿𝗮𝗰𝘁𝗶𝘃𝗲 𝗩𝗶𝘀𝘂𝗮𝗹𝘀: Make the data come to life with interactive illustrations! Whether it’s a clickable infographic or an interactive diagram, these visuals let the audience explore data points at their own pace, creating a more engaging experience. 🖱️✨ 𝗡𝗮𝗿𝗿𝗮𝘁𝗶𝘃𝗲 𝗗𝗶𝗮𝗴𝗿𝗮𝗺𝘀: Instead of static charts, use narrative diagrams to guide your audience through the data step by step, much like a journey. This method works great for processes, workflows, or any complex system that needs to be broken down into digestible parts. 🗺️🔄 𝗠𝗼𝘁𝗶𝗼𝗻 𝗚𝗿𝗮𝗽𝗵𝗶𝗰𝘀 & 𝗔𝗻𝗶𝗺𝗮𝘁𝗶𝗼𝗻: What better way to make data exciting than with motion? Animated charts or flowing data visualizations can help bring static information to life, drawing in the audience with movement and interactivity. 🎥⚡ By moving beyond traditional graphs, we’re embracing a new wave of creativity in technical communication. Data doesn’t have to be boring—it can be vibrant, insightful, and even fun! Have you experimented with new ways of presenting data? What methods do you think are the most effective? Let's discuss how we can transform technical information into visual masterpieces! ✨ #DataVisualization #TechCommunication #CreativeDesign #Infographics #Illustration #UXDesign #DataStorytelling #Innovation

Most people don’t need more charts. They need the right chart. This graphic shows 50 ways to visualize data — and that’s exactly why many dashboards are confusing. Too many choices, not enough thinking. Here’s how I’d use this: Start with the question, not the chart. Comparison? Use column/bar. Trend? Line, area, or sparkline. Distribution? Histogram or box/violin (not 12 pie charts…). Choose by relationship, not aesthetics. Correlation → scatter, correlogram. Composition → stacked bar/area, not donut overload. Flow or structure → Sankey, org chart, network. One insight per visual. If your audience can’t say, “This chart shows X,” in 5 seconds, it’s decoration, not communication. Reduce cognitive load. Fewer colors. Clear labels. No 3D anything. Ever. Build your “go-to 10.” From these 50, pick 10 charts you’ll master. Use them 90% of the time. The pros look “simple” because they obsess over clarity, not complexity. Save this as a checklist for your next report or dashboard. And if you want to go deeper into data storytelling and visualization, Corporate Finance Institute® (CFI)'s resources are a great place to start.

I've been doing analytics for 13+ years. Here's how I would learn data visualization fast if I started again from zero. (The second thing might surprise you) 1) I would focus on data analysis. I've learned that the best data visualizations help the viewer understand what's going on: For myself. For my data story audience. For executives using my dashboards. This is way more important than the technology. Which leads to... 2) I would start with Microsoft Excel. Here's why: - Just about every professional has it. - Excel supports many visualizations. - PivotCharts are fantastic. - Python in Excel. Even in 2025, you can't go wrong learning to analyze data with Microsoft Excel visually. So what to learn? 3) Start with histograms. If you're like me, you first learned histograms in a statistics course. And then promptly forgot about them. It took me years to realize that histograms are wildly useful for analyzing columns of numbers. Oh, and Excel can make histograms. 4) Box and whisker plots. Commonly called box plots, this visualization allows you to analyze a column of numbers by category. For example, how do the amounts of sales orders vary across company geographies? Combining histograms and box plots is powerful. And Excel supports both. 5) Use multiple dimensions. Visualizations are more powerful when you use multiple columns (dimensions) at the same time. Excel PivotCharts can create these visualizations. Also, Python in Excel has plotnine, the best way to make these visualizations. 6) Multidimensional bar charts. Bar charts are the go-to visual for categorical data. But, most professionals don't create them with multiple columns. Excel PivotCharts are great for this. Plotnine with Python in Excel is even better. Be sure to explore related columns and see what pops. 7) Fall in love with line charts. Line charts are the best visualization in business analytics. Because every business process has a time element. Line charts allow you to see: Trends Variability Cycles Rate of change Exceptions This is what executives care about! 8) Use stacked area line charts. Stacked area line charts add the power of seeing relative proportions over time. For example, sales over time by product line or geography. Stacked area line charts are a go-to in my data story PowerPoint decks. They're easily understood and powerful. 9) Get some good resources. Here are two of my favorite books to get you started: To learn visual analysis, "Now You See It" by Stephen Few. To learn how to make your visuals look good, "The Wall Street Journal Guide to Information Graphics" by Dona Wong.

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

One of the biggest challenges in data visualization is deciding 𝘸𝘩𝘪𝘤𝘩 chart to use for your data. Here’s a breakdown to guide you through choosing the perfect chart to fit your data’s story: 🟦 𝗖𝗼𝗺𝗽𝗮𝗿𝗶𝘀𝗼𝗻 𝗖𝗵𝗮𝗿𝘁𝘀 If you’re comparing different categories, consider these options: - Embedded Charts – Ideal for comparing across 𝘮𝘢𝘯𝘺 𝘤𝘢𝘵𝘦𝘨𝘰𝘳𝘪𝘦𝘴, giving you a comprehensive view of your data. - Bar Charts – Best for fewer categories where you want a clear, side-by-side comparison. - Spider Charts – Great for showing multivariate data across a few categories; perfect for visualizing strengths and weaknesses in radar-style. 📈 𝗖𝗵𝗮𝗿𝘁𝘀 𝗳𝗼𝗿 𝗗𝗮𝘁𝗮 𝗢𝘃𝗲𝗿 𝗧𝗶𝗺𝗲 When tracking changes or trends over time, pick these charts based on your data structure: - Line Charts – Effective for showing trends across 𝘮𝘢𝘯𝘺 𝘤𝘢𝘵𝘦𝘨𝘰𝘳𝘪𝘦𝘴 over time. Line charts give a sense of continuity. - Vertical Bar Charts – Useful for tracking data over fewer categories, especially when visualizing individual data points within a time frame.    🟩 𝗥𝗲𝗹𝗮𝘁𝗶𝗼𝗻𝘀𝗵𝗶𝗽 𝗖𝗵𝗮𝗿𝘁𝘀 To reveal correlations or relationships between variables: - Scatterplot – Best for displaying the relationship between 𝘵𝘸𝘰 𝘷𝘢𝘳𝘪𝘢𝘣𝘭𝘦𝘴. Perfect for exploring potential patterns and correlations. - Bubble Chart – A go-to choice for three or more variables, giving you an extra dimension for analysis. 🟨 𝗗𝗶𝘀𝘁𝗿𝗶𝗯𝘂𝘁𝗶𝗼𝗻 𝗖𝗵𝗮𝗿𝘁𝘀 Understanding data distribution is essential for statistical analysis. Use these to visualize distribution effectively: - Histogram – Best for a 𝘴𝘪𝘯𝘨𝘭𝘦 𝘷𝘢𝘳𝘪𝘢𝘣𝘭𝘦 with a few data points, ideal for showing the frequency distribution within a dataset. - Line Histogram – Works well when there are many data points to assess distribution over a range. - Scatterplot – Can also illustrate distribution across two variables, especially for seeing clusters or outliers. 🟪 𝗖𝗼𝗺𝗽𝗼𝘀𝗶𝘁𝗶𝗼𝗻 𝗖𝗵𝗮𝗿𝘁𝘀 Show parts of a whole and breakdowns with these: - Tree Map – Ideal for illustrating hierarchical structures or showing the composition of categories as part of a total. - Waterfall Chart – Perfect for showing how individual elements contribute to a cumulative total, with additions and subtractions clearly represented. - Pie Chart – Suitable when you need to show a single share of the total; use sparingly for clarity. - Stacked Bar Chart & Area Chart – Both work well for visualizing composition over time, whether you’re tracking a few or many periods. 💡 Key Takeaways - Comparing across categories? Go for bar charts, embedded charts, or spider charts. - Tracking trends over time? Line or bar charts help capture time-based patterns. - Revealing relationships? Scatter and bubble charts make variable correlations clear. - Exploring distribution? Histograms or scatter plots can showcase data spread. - Showing composition? Use tree maps, waterfall charts, or pie charts for parts of a whole.

𝐄𝐱𝐩𝐥𝐨𝐫𝐢𝐧𝐠 𝐃𝐚𝐭𝐚 𝐓𝐡𝐫𝐨𝐮𝐠𝐡 𝐂𝐡𝐚𝐫𝐭𝐬: 𝐀 𝐆𝐮𝐢𝐝𝐞 𝐭𝐨 𝐕𝐢𝐬𝐮𝐚𝐥𝐢𝐳𝐚𝐭𝐢𝐨𝐧 Data visualization is a powerful tool for 𝐮𝐧𝐝𝐞𝐫𝐬𝐭𝐚𝐧𝐝𝐢𝐧𝐠 and 𝐜𝐨𝐦𝐦𝐮𝐧𝐢𝐜𝐚𝐭𝐢𝐧𝐠 insights from data. Different types of charts serve different purposes. Let's explore some common types of charts and their applications: 1️⃣ 𝐁𝐚𝐫 𝐂𝐡𝐚𝐫𝐭 📊: 𝐏𝐮𝐫𝐩𝐨𝐬𝐞: Comparing categorical data or showing changes over time. 𝐒𝐮𝐢𝐭𝐚𝐛𝐥𝐞 𝐟𝐨𝐫: Comparing values of different categories, such as sales by product category or revenue by month. 𝐄𝐱𝐚𝐦𝐩𝐥𝐞: Bar chart comparing monthly sales for different products. 2️⃣ 𝐋𝐢𝐧𝐞 𝐂𝐡𝐚𝐫𝐭 📈: 𝐏𝐮𝐫𝐩𝐨𝐬𝐞: Showing trends and changes over time. 𝐒𝐮𝐢𝐭𝐚𝐛𝐥𝐞 𝐟𝐨𝐫: Visualizing continuous data over a period, such as stock prices over months or temperature variations over days. 𝐄𝐱𝐚𝐦𝐩𝐥𝐞: Line chart showing the trend of website traffic over a year. 3️⃣ 𝐏𝐢𝐞 𝐂𝐡𝐚𝐫𝐭 🥧: 𝐏𝐮𝐫𝐩𝐨𝐬𝐞: Displaying parts of a whole and illustrating proportions. 𝐒𝐮𝐢𝐭𝐚𝐛𝐥𝐞 𝐟𝐨𝐫: Showing the composition of a categorical variable, such as market share by product or distribution of expenses by category. 𝐄𝐱𝐚𝐦𝐩𝐥𝐞: Pie chart illustrating the distribution of budget allocation for different departments. 4️⃣ 𝐇𝐢𝐬𝐭𝐨𝐠𝐫𝐚𝐦 📊: 𝐏𝐮𝐫𝐩𝐨𝐬𝐞: Representing the distribution of continuous data. 𝐒𝐮𝐢𝐭𝐚𝐛𝐥𝐞 𝐟𝐨𝐫: Visualizing the frequency distribution of numerical data, such as age distribution of survey respondents or distribution of exam scores. 𝐄𝐱𝐚𝐦𝐩𝐥𝐞: Histogram showing the distribution of heights among a sample population. 5️⃣ 𝐒𝐜𝐚𝐭𝐭𝐞𝐫 𝐏𝐥𝐨𝐭 📈: 𝐏𝐮𝐫𝐩𝐨𝐬𝐞: Examining relationships between two continuous variables. 𝐒𝐮𝐢𝐭𝐚𝐛𝐥𝐞 𝐟𝐨𝐫: Identifying patterns and correlations between variables, such as the relationship between temperature and ice cream sales or the correlation between advertising spending and sales revenue. 𝐄𝐱𝐚𝐦𝐩𝐥𝐞: Scatter plot depicting the relationship between hours studied and exam scores for students. 6️⃣ 𝐁𝐨𝐱 𝐏𝐥𝐨𝐭 📊: 𝐏𝐮𝐫𝐩𝐨𝐬𝐞: Summarizing the distribution of numerical data and identifying outliers. 𝐒𝐮𝐢𝐭𝐚𝐛𝐥𝐞 𝐟𝐨𝐫: Visualizing the spread and skewness of data, comparing distributions, and identifying anomalies. 𝐄𝐱𝐚𝐦𝐩𝐥𝐞: Box plot comparing the distribution of salaries for different job roles within a company. 7️⃣ 𝐇𝐞𝐚𝐭𝐦𝐚𝐩 🔥: 𝐏𝐮𝐫𝐩𝐨𝐬𝐞: Displaying the magnitude of a variable in a matrix format. 𝐒𝐮𝐢𝐭𝐚𝐛𝐥𝐞 𝐟𝐨𝐫: Visualizing relationships and patterns in large datasets, such as correlation matrices or user engagement matrices. 𝐄𝐱𝐚𝐦𝐩𝐥𝐞: Heatmap showing customer engagement levels across different demographics and products. Remember to choose the appropriate chart type based on the nature of your data and the insights you want to convey. #dataanalysis #visualization #charts #insights #analysis #eda Follow Sneha Vijaykumar for more... 😊

📊 Demystifying Data Visualization: A Comprehensive Guide Navigating the world of data can be like trying to find your way through a maze. But with the right map, suddenly the path becomes clear. That's what this chart is—a visual guide that matches your data storytelling needs with the perfect chart type. Whether you're looking to compare variables, demonstrate relationships, or show compositions, this guide distills complex information into a straightforward format. It's like having a data visualization expert by your side! Key takeaways from this guide: To compare multiple variables? Consider bar charts and scatter plots. To show how parts make up a whole? Pie or donut charts might be what you need. To illustrate data that changes over time? Line charts and area charts can track the trends. To reveal distribution patterns? Histograms provide a clear picture at a glance. Are you ready to enhance your reports, presentations, and dashboards? With this chart as your guide, you’ll always pick the right visual for your data. Let's make information beautiful and accessible, one chart at a time! You can explore my Tableau dashboards here: https://lnkd.in/ghR-KQba Image Credits: Damola Ladipo #DataVisualization #Infographics #StorytellingWithData #Analytics #BusinessIntelligence #DataScience