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

📊💡 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

Useful data science post Friday. I know you want to use cool visualizations. Whether you build dashboards, perform exploratory data analysis, or present evidence-based recommendations to the C-Suite via Powerpoint, the temptation to use complex visualizations is strong. This is simply because you work with data a lot, and your brain has acclimated to seeing data in 3-dimensional ways that can be difficult to visualize with bar graphs, line charts, and yes, even the much-maligned pie charts. Resist this temptation at all costs! Sankey diagrams, box plots, violin plots, radial charts, network graphs, tree maps, parallel coordinate plots and the like are impossible for most people to understand. Even dual-Axis line charts, waterfall charts, histograms, normalized (100%) stacked area charts and scatterplots should be used sparingly, as most end users will require a training and repeat exposure to understand them effectively. So, what should you use? What they already know and what's super intuitive. Single-axis line charts, vertical and horizontal bar graphs, and pie charts. Line charts should have a single axis and no more than five-to-seven lines, the fewer the better. Anything more than seven and most people will run into working memory issues. Less than five works for the majority of people. Bar graphs should not be stacked, but they should be grouped together. Use vertical bars for time series, and horizontal bars for comparing categories. When using horizontal bars, order them intuitively from greatest-to-least or vice versa. Pie charts should likewise have a limited amount of categories. Aim for a maximum of 5-6 with anything over that being labeled as "other." Because they are hard to read, ensure you have hover-over functionality that shows users the exact number on the pie chart (both absolute and as a percentage). That will help avoid misinterpretations. So, it's not sexy advice, but it's the best advice for delivering usable content to end users. Enjoy your day! #datascience #datavisualization #powerbi #tableau #qlik

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

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!

Python in Excel: Creating layered and faceted visualizations with Plotnine With Python’s Plotnine package integrated into Excel, you can go beyond basic charts and create layered and faceted plots that bring clarity and depth to your data analysis. This blog post walks you through step-by-step examples, complete with a downloadable exercise file, so you can follow along and build advanced visualizations right in Excel. You'll learn how to build layered regression plots, small multiples, overlaid histogram/density plots and more. Follow along at https://lnkd.in/gEYV8Q4i These techniques showcase how the Grammar of Graphics framework in Plotnine can help you create meaningful, impactful visualizations that go far beyond Excel’s built-in charting tools. What questions do you have about layered and faceted plots in Plotnine specifically, or about data visualization with Python in Excel more broadly? Let me know below.

Top 3 data visualization tips you need to know. 📈 Which is your favorite? Early in my career, I created charts that were technically correct but completely failed to communicate insights effectively. My visualizations were cluttered, confusing, and often missed the point entirely. After years of trial and error, these three principles became the foundation of every successful data visualization I create. The three essential tips: 1️⃣ Sparklines for Trend Context – Add mini trend charts within cells to show data patterns alongside the actual numbers. Perfect for showing monthly sales trends or performance trajectories without cluttering your main display. 2️⃣ REPT Bar Charts – Create in-cell bar charts using the REPT function to visualize relative values instantly. Change the font to Stencil for solid bars that make comparisons obvious at a glance. 3️⃣ Heat Map Visualization – Use conditional formatting to create color-coded heat maps that highlight patterns, outliers, and trends across large datasets. Essential for geographic data, performance matrices, and correlation analysis. These aren't just visualization tricks - they're communication tools that transform how stakeholders interact with and understand your data. Save your seat now for the FREE Excel Dashboard Class: https://lnkd.in/eXRbPD4Q #excel #exceltips #exceltricks #spreadsheets #corporate #accounting #finance #workhacks #tutorial #sheets

💡Line, bar, and pie chart design: tips & tricks Effective data viz makes it easy to communicate complex information without requiring people to read and interpret a lot of text. People tend to be familiar with common chart types, such as line and bar charts, so using one of these types in your app can make it more likely that people will already know how to read your chart. 🍎 General recommendations ✔ Ensure chart placement and size aligns with the visual hierarchy of the page. When using multiple charts at the same level of importance, they should have consistent sizing. ✔ Design charts to adapt to different screen sizes and resolutions without losing clarity or detail. ✔ Use colorblind-friendly palettes and ensure charts are legible in both dark and light mode. 🍏 Line chart Line charts connect a series of data points using a line, and are commonly used to show data trends over time. ✔ Simplify the lines: Use distinct, contrasting colors for each line. Avoid using more than four to six lines to prevent clutter. ✔ Axis labels and gridlines: Use subtle gridlines and clearly label the axes. ✔ Data points: Highlight data points with small markers (circles, squares) to make it easier to read exact values. ✔ Interactivity: Consider adding tooltips that show exact data values when users hover over specific points on the line. ✔ Avoid using just color to communicate meaning in your data visualization. Incorporate other visual indicators such as shapes, line texture, patterns, or direct labels to help users make sense of the data. How to create line chart in Figma: https://lnkd.in/edPVhsih 🍏 Bar chart Bar charts are used to compare categorical or discrete data. ✔ Baseline number: Don't set the baseline to any number other than zero. Doing so misrepresents the data. ✔ Color: Use a single color for all bars unless comparing different categories; in that case, use distinct colors for each category. ✔ Labels: Place value labels directly on top of or inside the bars to make the data easily accessible without needing to reference the axis. ✔ Stacked vs. grouped bars: Use stacked bars for comparing parts of a whole and grouped bars for comparing multiple categories across the same scale. 🍏 Pie chart Pie chart helps users visualize portions of a whole at a glance. ✔ Segment clarity: Limit the number of slices to 5; more slices make the chart difficult to read. Consider grouping smaller slices into an "Others" category. ✔ Pie order: show data in descending order, starting at the 12 o'clock point and moving clockwise. ✔ Minimalist design: Keep the design clean by avoiding 3D effects, shadows, or excessive borders, which can distort perception. How to create bar chart in Figma: https://lnkd.in/eAaV3KBs #dataviz #datavisualization #ui #uidesign #productdesign #userinterface #uxdesign