How to Create Data Visualizations

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.

Curious about how to transform raw data into captivating visual narratives? Dive into Matplotlib in Python with our comprehensive beginner's guide! Here's what you'll learn: Introduction to Matplotlib: Understand the basics of Matplotlib and how it's used in Python for data visualization. 🔵Creating Plots: Learn how to effortlessly create stunning plots using Matplotlib's plot() function. You'll explore various customization options to add style and color to your plots. 🔵Adding Labels and Titles: Master the art of adding labels and titles to your plots for clarity and context. 🔵Formatting Fonts: Discover techniques to beautify fonts in your plots, enhancing the overall aesthetic appeal. 🔵Grid Lines for Precision: Understand the importance of grid lines in achieving precision in your plots, and learn how to effectively utilize them. 🔵Handling Multiple Plots: Learn how to handle multiple plots in a single figure, making complex visualizations simpler to create and understand. 🔵Enhancing Storytelling with Legends: Explore how to add legends to your plots to enhance storytelling and provide additional context to your data. 🔵Exploring Different Plot Types: Delve into various types of plots supported by Matplotlib, including bar graphs, scatter plots, pie charts, histograms, and 3D plots. 🔵Working with Images: Elevate your data visualization skills by learning how to work with images in Matplotlib, opening up new possibilities for visual storytelling. 🔵Takeaways and Next Steps: Wrap up your journey with key takeaways and pointers for further exploration, empowering you to continue honing your data visualization prowess. Are you ready to elevate your data visualization skills and unlock the full potential of Matplotlib in Python? ��� CC: Abhishek Mishra #DataVisualization #Python #Matplotlib #DataScience #Coding #DataAnalysis #VisualizeData #Programming #LinkedInLearning #TechSkills

In today’s data-driven world, the ability to quickly understand and act on data is more critical than ever. One of the most powerful tools to achieve this is data visualization, especially when using Excel. By transforming raw data into visual representations, we can not only identify trends and patterns but also communicate insights in a more digestible format. 𝐿𝑒𝑡’𝑠 𝑑𝑖𝑣𝑒 𝑖𝑛𝑡𝑜 ℎ𝑜𝑤 𝑦𝑜𝑢 𝑐𝑎𝑛 𝑙𝑒𝑣𝑒𝑟𝑎𝑔𝑒 𝐸𝑥𝑐𝑒𝑙’𝑠 𝑓𝑒𝑎𝑡𝑢𝑟𝑒𝑠 𝑡𝑜 𝑒𝑛ℎ𝑎𝑛𝑐𝑒 𝑦𝑜𝑢𝑟 𝑑𝑎𝑡𝑎 𝑎𝑛𝑎𝑙𝑦𝑠𝑖𝑠 𝑎𝑛𝑑 𝑑𝑒𝑐𝑖𝑠𝑖𝑜𝑛-𝑚𝑎𝑘𝑖𝑛𝑔 𝑝𝑟𝑜𝑐𝑒𝑠𝑠𝑒𝑠: 📈 Charts and Graphs: Visualizing data with charts and graphs helps highlight important trends and patterns at a glance. Whether it’s a bar chart, line graph, or pie chart, these visuals are perfect for simplifying complex data and making it easier to interpret. ℹ️ Conditional Formatting: Want to quickly spot outliers or key data points? Conditional formatting is your go-to tool. By applying color scales, data bars, or icon sets, you can instantly identify critical information without having to sift through every row of data. 📊 Pivot Charts: Pivot charts allow you to create dynamic visual summaries of your data, giving you the flexibility to explore different perspectives on the fly. With the ability to adjust and manipulate the data, you can uncover insights that might have been overlooked in static tables. 🌟 Sparklines: These mini-charts inside a cell are perfect for showcasing trends within a single row of data. Use sparklines to get a snapshot of trends without taking up too much space on your sheet. 〰️ Dashboard Integration: A dashboard consolidates multiple visualizations into one interactive view, making it easier to track key metrics and make informed decisions. With Excel, you can integrate different charts and graphs into a dashboard that provides a holistic view of your data. Data visualization isn’t just about creating pretty pictures—it’s about making data more accessible, understandable, and actionable. Whether you’re tracking business performance or analyzing trends, these tools can turn raw numbers into strategic insights that drive decisions. How do you currently use data visualization to inform your decision-making process, and which Excel feature do you find most effective? Share your thoughts in the comments below! #DataVisualization #ExcelTips #ExcelDashboards #DataInsights #DataDrivenDecisionMaking

📊💡 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

📚Day-3 of Tableau Learning by Visualizing – Exploring Advanced Charts in Tableau! Yesterday, I took a deep dive into some of Tableau’s most powerful and visually rich chart types. To better understand them, I built simple visualizations for each, exploring how and when to use them effectively. Here’s a quick walkthrough of what I explored: 🔹 1. Histogram Helps visualize the distribution of continuous data by grouping it into bins. I used this to identify how frequently values fall into each range — great for spotting skew, gaps, or clusters. 🔹 2. Box and Whisker Plot Perfect for showing data spread and outliers. It displays the median, quartiles, and extremes clearly — especially helpful in comparing distributions across categories. 🔹 3. Area Chart These show how values change over time, with the area under the line filled in. It’s great for highlighting volume or cumulative trends, especially for stacked comparisons. 🔹 4. Dual Combination Chart Combines two chart types (like bar + line) with dual axes. I used this to compare two different measures (like sales & profit) even when they have different scales. 🔹 5. Gantt Chart A go-to for project timelines. It shows task durations and overlaps along a timeline — perfect for visualizing project phases or schedules. 🔹 6. Treemap Displays hierarchical data as nested rectangles. Size and color indicate values, helping visualize part-to-whole relationships at a glance. 🔹 7. Packed Bubble Chart Uses bubbles to represent categories with size based on value. It’s a fun and interactive way to compare proportions without needing axes. 🎯 Goal: Not just to create the visuals, but to understand why and when to use each chart, how they tell different stories, and how they can bring clarity to complex datasets. 📊 You can explore all these charts in my Tableau Public profile here: 👉 [https://lnkd.in/gbE-WVEp] This learning session gave me deeper insight into visual communication, not just displaying data, but making it actionable. #Tableau #DataVisualization #Analytics #LearningInPublic #StorytellingWithData #BusinessIntelligence #TableauTips #DashboardDesign #ContinuousLearning

Turn Data into Visual Stories with Just Pandas! When we think of data visualization, tools like Matplotlib or Seaborn often come to mind. But did you know Pandas alone is powerful enough to handle most basic to intermediate visualizations? Here’s how Pandas simplifies data visualization for analysts and data scientists: Key Pandas Visualization Methods (built on Matplotlib): • df.plot() – the universal method for most chart types • df.plot.line() – visualize trends over time • df.plot.bar() / df.plot.barh() – compare categorical data • df.plot.hist() – understand frequency distributions • df.plot.box() – explore statistical summaries and outliers • df.plot.area() – show cumulative data visually • df.plot.scatter(x, y) – examine correlations between variables • df.plot.pie() – display part-to-whole relationships (great for series) Why use Pandas for Visualization? • Quick and intuitive • Great for EDA (exploratory data analysis) • Seamlessly integrated with your DataFrame • Ideal for prototypes and dashboards You don’t always need a complex setup—sometimes, clean and clear insights begin with just df.plot(). Have you used Pandas for visualization? What’s your favorite use case? #DataAnalytics #Pandas #DataVisualization #PythonForDataScience #EDA #LinkedInLearning #DataScienceTools

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

How to use AI and Python for FP&A Data Visualization? Many #finance and FP&A teams asked me this. So I created a 5 steps framework to help you get started. With an LLM (ChatGPT, Copilot, Gemini, etc)+ Python, you can transform data into powerful visual stories. Here’s the 5-step approach I use: 1. Show ChatGPT your data Paste a few rows of your dataset and ask for visualization suggestions. This step is super important to understand. You do not need to GIVE your data to an "AI Company". You just need to show how your data LOOKS LIKE. Use this prompt: “I'm a FP&A analyst (replace this with your role) working with a dataset and I'd like your help picking the three most effective visualizations for it. Below is a sample of the data (including column names and a few rows). Based on the structure, types of variables, and any potential insights you notice, recommend three visualizations that would best highlight trends, patterns, or relationships in the data. Here is the data: (Paste a few rows of your "dummy" data here, ideally 5–10 rows, including the header. You don't need to add real data but the format of the data is important [e.g. date, number, percentage[)” 2. Get the 3 best examples Let AI recommend the most impactful charts for your dataset. 3. Ask for Python Code Get the code from the LLM and then run it in G. Colab, VS or even Excel. My recommendation: If you want the easiest to start → Google Colab If your company prefers Microsoft Products → Visual Studio If you want to stay in an environment you know → Python in Excel! If you need help with choosing, let me know and I can suggest or send you some courses to start! 4. Execute and visualize Generate dynamic charts that highlight key financial insights. 5. Improve and Customize! 🎨 This is where you take it to the next level: ✅ Refine Styling – Customize colors, fonts, and labels for readability. ✅ Add More Insights – Overlay trend lines, percentage changes, or KPIs. ✅ Make it Interactive – Use Plotly for drill-down capabilities. ✅ Automate Everything – Schedule updates and integrate into workflows. ✅ Leverage AI Further – Use predictive modeling to forecast trends. Hope this is useful and if you want the data and code I used for the belows examples just message me or comment and I can send!