Data Visualization Software

Visualizing Uncertainty in Machine Learning with Gaussian Process Regression I've been reflecting on how Gaussian Process Regression (GPR) visualizations provide one of the most intuitive ways to understand uncertainty in machine learning models. What makes these visualizations so powerful is how they transform abstract statistical concepts into immediate visual insight: 🔍 Uncertainty as space: The confidence interval (typically shown as a shaded region) visually represents where the model believes the true function might lie. It's uncertainty made tangible. 📊 Data-driven confidence: Watching how uncertainty narrows precisely at locations where data exists, while remaining wide in unexplored regions, creates an immediate "aha!" moment about how models learn. 📈 Correlation intuition: Seeing how adding a single point affects predictions in neighboring regions helps build intuition about the fundamental concept of correlation in probabilistic models. 🧠 Prior knowledge visualization: GPR visualizations elegantly show how prior assumptions about smoothness and variation influence predictions in regions with sparse data. I find these visualizations particularly valuable when explaining complex concepts like Bayesian reasoning, active learning, and the exploration-exploitation tradeoff to stakeholders without technical backgrounds. What I appreciate most is how a simple curve with a shaded region conveys a sophisticated mathematical concept: that our models aren't just making predictions; they're expressing degrees of confidence that systematically decrease as we gather more evidence. Have you found other visualization approaches that make complex ML concepts more intuitive? I'd love to hear your thoughts! #MachineLearning #DataScience #Visualization #UncertaintyQuantification #GaussianProcesses #BayesianML

Dimensionality Reduction (DR) simplifies complex, high-dimensional datasets into more manageable lower-dimensional forms for easier interpretation and better computational efficiency while preserving key information. Modern nonlinear DR techniques, such as t-SNE and UMAP, are popular for transforming complex datasets into simpler visual representations. However, they can produce results that are difficult to interpret due to the lack of inherent meaning in the shapes and clusters, suboptimal hyperparameters, and potential distortions. DimVis is a visualization tool developed by the authors of [1] which employs supervised EBM (Explainable Boosting Machine) models (trained on user-selected data of interest) as an interpretation assistant for DR projections. The DimVis tool facilitates high-dimensional data analysis by providing an interpretation of feature relevance in visual clusters through interactive exploration of UMAP projections. Specifically, the DimVis tool utilizes a contrastive EBM model that is trained in real-time to distinguish between data points inside and outside a cluster of interest. Leveraging the inherently explainable nature of the EBM, this model is then used to interpret the cluster through single and pairwise feature comparisons, ranked according to the EBM model’s feature importance. The applicability and effectiveness of DimVis are demonstrated through a use case and a scenario involving real-world data. Their paper [1] and the DimVis #Python code [2] are available through the links provided in the comments.

I reduced a Power BI dashboard load time from 45 seconds to 3. Not by buying better hardware. Not by rewriting every DAX formula. But by fixing how I built the model. Most people try to speed up dashboards at the visual layer. But the real slowdown usually hides in the data model. Here’s what worked for me 👇 ✅ 1. Removed unnecessary columns and tables If a field wasn’t used in visuals or relationships, it was gone. Smaller models run faster - every column adds weight. ✅ 2. Disabled auto date/time This tiny setting adds hidden overhead. Turn it off - especially with large date columns. ✅ 3. Aggregated data before import I summarized data in SQL and Power Query first. The row count dropped by 80%. Power BI isn’t meant to store raw transactions - it’s meant to analyze. ✅ 4. Replaced calculated columns with measures Calculated columns sit in memory. Measures calculate on demand. Same output - huge performance difference. ✅ 5. Optimized visuals Fewer slicers. Simpler visuals. Cards instead of massive tables. Cleaner design - faster queries. Result? From 45 seconds down to 3. Stakeholders noticed immediately. No more “is this dashboard broken?” messages. Speed builds trust. A slow dashboard feels like bad data - even when it’s not. Have you ever optimized a dashboard that suddenly became everyone’s favorite? What was your biggest Power BI performance win? #powerbi #dataanalytics #dax #businessintelligence #datamodeling #datavisualization

This is what a neural network actually learns, animated in real time. 💡 Watch the dots. That is the model building understanding from scratch. What you are seeing is a PCA visualization of the embedding space during training on CIFAR-10, 10 classes of images including airplanes, cats, dogs, frogs, and more. At the start, all the dots are clustered together in a random mess. The model has no idea what a cat is versus a dog versus an airplane. Everything looks the same to it. As training progresses and the loss drops, something remarkable happens. The dots start separating. Each color finds its own region in the space. The model is not memorizing images. It is learning the underlying structure of what makes a cat different from a truck. By the end, each class has carved out its own territory. The outliers on the right side are the interesting part. Those are real images from the dataset that do not look like typical examples of their class. A frog that looks like a bird. A cat with unusual lighting. An airplane shot at a weird angle. The model correctly identifies them as unusual because they sit far from the main cluster of their class. This is what people mean when they say a neural network learns representations. It is not storing a lookup table of images it has seen. It is building a geometry where similar things end up near each other and different things end up far apart. Every prediction the model makes is just asking "where does this new image fall in that geometry?" The loss curve at the bottom tells you when the learning is happening. The embeddings tell you what the learning actually looks like. If you have never visualized your model's embedding space during training, you are missing one of the most useful debugging tools available. What do you think happens to the embedding space when the model starts overfitting? #DeepLearning #MachineLearning #ComputerVision #NeuralNetworks #PyTorch #DataScience #AIEngineer

Power BI has levels to it: - level 1 Building reports and basic DAX Create visuals and dashboards with drag-and-drop ease. Understand data modeling basics: tables, relationships, and star schemas. Learn core DAX functions like SUM, COUNT, FILTER, and CALCULATE. Grasp context (row vs. filter) to avoid rookie mistakes. Master these, and you’ll never confuse Power BI with “just Excel on steroids.” - level 2 Advanced DAX & data modeling Write reliable DAX with variables and DIVIDE for safe calculations. Use ALL and ALLEXCEPT to manipulate filter context like a pro. Schema evolution: build flexible star schemas with bridge tables for many-to-many relationships. Normalize or denormalize based on query patterns. Optimize model size with proper data types and cardinality reduction—because bloated models kill performance. - level 3 Incremental refresh & advanced visuals Set up incremental refresh to process only new data—query RANGESTART and RANGEEND for massive datasets. Use custom visuals or Deneb (Vega-Lite) for bespoke charts. Implement drillthroughs, bookmarks, and dynamic titles for interactive storytelling. Row-level security (RLS): enforce data access by user or role. These make compliance and user-specific reporting painless. - level 4 Performance tuning & query optimization Use Performance Analyzer to spot slow visuals. Rewrite DAX to minimize iterator functions like SUMX when SUM + CALCULATE will do. Aggregate tables: pre-summarize data to speed up reports. Manage VertiPaq engine: reduce column cardinality and remove unused fields. Check Query Diagnostics to ensure DirectQuery or composite models aren’t hammering your source. A sneaky FORMAT in DAX can tank performance—keep it lean! - level 5 Ecosystem mastery & automation Query metadata via DMVs (e.g., TMSCHEMA_TABLES) for instant model insights or documentation. Automate with Power Automate: trigger refreshes, send alerts, or sync data flows. Use Power BI REST APIs to programmatically manage datasets or embed reports. Integrate with Azure Synapse, Databricks, or Fabric for end-to-end analytics. Build CI/CD pipelines with Tabular Editor and ALM Toolkit for versioned deployments. Treat your Power BI environment like a governed, scalable data platform—not a collection of random PBIX files. What else did I miss for mastering Power BI?

“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.

At the end of every quarter, Sandra has the same challenge. As the Commercial Analytics Lead at Nestlé Nigeria, she's responsible for preparing performance reports for management. The problem isn't the reporting itself. The problem is that the data comes from different regions. For Q1, sales reports arrived from the North, South-West, and South-East regions as separate CSV files. Each region maintained its own report, which meant slight differences in formatting, naming conventions, and data quality. Before management could answer questions like: • Which region generated the most revenue? • Which products performed best? • Who were the top-performing sales representatives? • How did revenue trend throughout the quarter? The data first had to be prepared. This project started with three regional sales datasets. Using Power Query, I imported the files, standardized the structure, corrected data types, cleaned inconsistencies, and appended all three datasets into a single master table. Once the foundation was in place, I created additional fields to support analysis, including: • Revenue • Profit • Profit Margin • Week Number • Month Name • Performance Indicators With the transformed dataset ready, the next step was analysis. Before building any visuals, I summarized the data using PivotTables to identify the key insights and answer the business questions that mattered most. Only after the analysis phase did I move into dashboard design. I wireframed the layout, defined the KPI structure, selected the appropriate visualizations, and built an interactive dashboard featuring: • Revenue, Profit, Quantity, and Transaction KPIs • Revenue by Channel and Category • Top Performing Sales Representatives • Best Selling Products • Weekly Revenue Trends • Interactive filters for Region, Month, and Sales Representative What began as three separate CSV files became a centralized reporting solution capable of delivering insights in seconds. One lesson I keep reinforcing in my classes: Most people think dashboards start with charts. They don't. Dashboards start with clean data, a structured process, and the right business questions. Tools Used: Excel, Power Query, PivotTables, PivotCharts, Slicers, Dashboard Design #DataAnalytics #Excel #PowerQuery #BusinessIntelligence

The “perfect” daily production dashboard doesn’t exist. But after building hundreds, these are the principles I’ve seen work on the factory floor. 1/ Start with the KPIs that matter. OEE, Availability, Performance, Quality (or the KPI important to you) all color-coded against clear daily targets. It should answer one question: How are we doing today? 2/ Drill down to what’s driving the gap. → Sort machines or lines by worst performance → Use Pareto charts to highlight what’s worth fixing → Filter out the noise: show the top 3–5 loss reasons 3/ Add real context. → Show labor efficiency next to production → Only show what someone can act on today → Use clear labels: avoid hidden meanings or long legends 4/ Include actions taken. A dashboard is not just for display it’s a tool to drive decisions. Example: 1st shift → Coil change issues flagged in Packaging 1 Countermeasure: Applied new checklist Result: Setup time dropped 40% in 2nd shift 5/ Always show the trend. One good day doesn’t mean improvement. One bad day doesn’t mean failure. Use trends to stay focused on what’s working (or not). Dashboards don’t fix problems, people do. But a well-designed one helps them fix the right ones, faster. PS: What would you add to your dashboards? Can you help me improve my checklist? *** <--> Repost to help more factory teams build dashboards that actually help them improve.

What if you could see the exact point where your model starts overfitting as you tune hyperparameters? Hyperparameter tuning requires finding the sweet spot between underfitting (model too simple) and overfitting (model memorizes training data). You could write the loop, run cross-validation for each value, collect scores, and format the plot yourself. But that's boilerplate you'll repeat across projects. Yellowbrick is a machine learning visualization library built for exactly this. Its ValidationCurve shows you what's working, what's not, and what to fix next without the boilerplate or inconsistent formatting. How to read the plot in this example: • Training score (blue) stays high as max_depth increases • Validation score (green) drops after depth 4 • The growing gap means the model memorizes training data but fails on new data Action: Pick max_depth around 3-4 where validation score peaks before the gap widens. 🚀 Full article: https://bit.ly/4qn5Qeq ☕️ Run this code: https://bit.ly/48RanQp #Python #MachineLearning #DataScience #Yellowbrick

📌 KPI Overload Is Real (And It's Slowing Down Your Decision Making) Let’s be honest. A lot of dashboards today are cluttered with metrics that look impressive… But don’t actually help anyone take action. You’ve probably seen it: → Revenue trends sliced by every possible dimension → Dozens of KPIs on performance, efficiency, and growth → Filters for every variable, but no clear direction At first glance, it feels comprehensive. But when it’s time to make a decision, there’s no clear signal in the noise. Here’s the real problem: Too many KPIs don’t accelerate decisions. They paralyze them. Stakeholders hesitate because they don’t know what to focus on. And eventually, no one opens the dashboard unless they have to. But the truth is: Most of the time you don’t need 20 KPIs to run a business unit. You just need a few metrics that guide action. Let’s take a real example. A regional sales manager logs into their dashboard. What do they need? → Are we on track to hit quota this month? → Which territories are underperforming? → Which reps need coaching or support? That’s it. They don’t need a 12-metric KPI card cluster. They need clarity and speed. Anything else can be put in a drill-through or secondary page. Here’s a 5-step framework I use when designing decision-first dashboards: 1️⃣ 𝐒𝐭𝐚𝐫𝐭 𝐰𝐢𝐭𝐡 𝐭𝐡𝐞 𝐞𝐧𝐝 𝐮𝐬𝐞𝐫 𝐢𝐧 𝐦𝐢𝐧𝐝 What decision will this dashboard influence and who’s going to be the end user? I wrote a post last week about the 4 types of dashboard users. I highly recommend you to check it out: https://lnkd.in/ex4W47F2 2️⃣ 𝐋𝐢𝐧𝐤 𝐞𝐚𝐜𝐡 𝐊𝐏𝐈 𝐭𝐨 𝐚 𝐬𝐩𝐞𝐜𝐢𝐟𝐢𝐜 𝐚𝐜𝐭𝐢𝐨𝐧 Avoid “nice-to-know” metrics. Prioritize “need-to-act” indicators. 3️⃣ 𝐊𝐞𝐞𝐩 𝐢𝐭 𝐟𝐨𝐜𝐮𝐬𝐞𝐝 Stick to 3-5 core KPIs on the main page. Save the rest for deeper analysis. 4️⃣ 𝐃𝐞𝐬𝐢𝐠𝐧 𝐟𝐨𝐫 𝐢𝐧𝐭𝐞𝐫𝐩𝐫𝐞𝐭𝐚𝐭𝐢𝐨𝐧 Use visuals that are instantly understandable. Every second spent decoding a chart is a second wasted. 5️⃣ 𝐁𝐮𝐢𝐥𝐝 𝐟𝐨𝐫 𝐢𝐭𝐞𝐫𝐚𝐭𝐢𝐨𝐧 Check in with users regularly. Ask: What are you actually using? What’s useful? What’s not? The bottom line is: Your dashboards should empower people. If someone needs a 10-minute walkthrough to understand your dashboard… It’s not a dashboard. It’s a presentation. The best dashboards are quiet operators: → They surface the most important signals → They remove decision friction → They let people act fast and with confidence Cut the noise and prioritize impact :) #BusinessIntelligence #DataAnalytics #DashboardDesign