The hottest Visualization Substack posts right now

And their main takeaways
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Song Pong

Victor Tao • 273 HN points • 28 Aug 24
🕹 Technology Gaming Optimization Music Visualization Programming
  1. You can make a pong game more exciting by syncing the ball's movements to music. This allows paddles to dance to the beat as they hit the ball.
  2. Using math and optimization techniques can help you decide where the paddles should hit the ball. It ensures that the game looks good while still following all the rules.
  3. Changing the physics of the game doesn't have to be hard. You just update the rules in your math model, making it easy to test new ideas and keep improving the game.

Data Science Weekly - Issue 561

Data Science Weekly Newsletter • 139 implied HN points • 22 Aug 24
🕹 Technology Data science AI Machine Learning Data Engineering Visualization
  1. When building web applications, using Postgres for data storage is a good default choice. It's reliable and widely used.
  2. A new study shows that agents can learn useful skills without rewards or guidance. They can explore and develop abilities just from observing a goal.
  3. The list of important books and resources in Bayesian statistics is being compiled. It's a way to recognize influential ideas in this field.

Data Science Weekly - Issue 554

Data Science Weekly Newsletter • 119 implied HN points • 04 Jul 24
🕹 Technology Data science AI Machine Learning Data Engineering Visualization
  1. Staying updated in data science, AI, and machine learning is essential for improving skills and knowledge. Weekly newsletters provide curated articles and resources that help you keep up with the latest trends.
  2. Effective structuring of data science teams can greatly enhance productivity. Learning from past experiences on team reorganizations can help in clarifying roles and increasing effectiveness.
  3. Building interactive dashboards in Python can make data more accessible. Using tools like PostgreSQL and specific libraries can simplify the process and enhance data visualization.

Better Data Science: How To Design Visualizations that Work

Data at Depth • 79 implied HN points • 05 May 24
🕹 Technology Data science Visualization Accessibility Aesthetics Design principles
  1. Start with defining the function you want the audience to perform with the presented data before creating visualizations that support it
  2. Implement aspects like affordances, accessibility, and aesthetics to ensure your visualizations are clear, usable, and visually appealing for the audience
  3. Achieving acceptance of your data visualization involves following established design principles like direct labeling, thoughtful use of color, alignment, and the data-ink principle
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Kahneman's System 1 and 2 Thinking for Data Storytelling: An Example

Data at Depth • 79 implied HN points • 08 Apr 24
🕹 Technology Data storytelling Visualization Color Theory
  1. Create impactful data stories by hitting your audience's senses first, then backing it up with solid data and an interesting narrative
  2. Understand Kahneman's System 1 (intuitive) and System 2 (thoughtful) thinking to effectively engage your audience by appealing to both ways of thinking
  3. Utilize color effectively in data visualization to enhance communication, emphasize key points, and leverage pre-attentive attributes to grab and direct viewer attention

Using Matplotlib To Create The 50th Article Cover Mosaic Animation (#50 Part 2)

The Python Coding Stack • by Stephen Gruppetta • 119 implied HN points • 10 Feb 24
🕹 Technology Programming Animation Visualization
  1. You can use Matplotlib to create animations, like a mosaic of previous article cover images, by following a step-by-step tutorial.
  2. Before starting the animation, ensure you have images ready and the necessary libraries installed like Matplotlib, NumPy, and Pillow.
  3. You can control how images are plotted, resize images in the animation frames, and save the animation as a movie file like an mp4 or an animated GIF using libraries like Matplotlib or PillowWriter.

Correlation Can Ruin Interpretability

Mindful Modeler • 479 implied HN points • 20 Sep 22
🔬 Science Data interpretation Correlation Visualization
  1. Correlation between features can significantly impact the interpretability of machine learning models, both technically and philosophically.
  2. Identifying and addressing correlation issues is crucial for accurate model interpretation. Techniques include grouping correlated features, decorrelation methods like PCA, feature selection, causal modeling, and conditional interpretation.
  3. Entanglement of interpretation due to correlation makes it challenging to isolate the impact of individual features in machine learning models.

Five Lessons from Walmart's Innovative Approach to Network Design

Mike Talks AI • 157 implied HN points • 09 May 23
💼 Business Supply Chain Innovation Visualization Project management
  1. Walmart's innovative approach to network design involved building a second model for transition planning.
  2. Linking strategic design to load planning and using simulation for execution is crucial in network design.
  3. Visualization is important in gaining approval and support for large-scale network design projects.

Data Science Weekly - Issue 487

Data Science Weekly Newsletter • 199 implied HN points • 23 Mar 23
🕹 Technology Data science Machine Learning AI Software Development Visualization
  1. This week's newsletter shares useful links in data science, machine learning, and AI. It's a great way to stay updated in these fields.
  2. One highlighted article discusses the importance of prompt engineering in interacting with language models. It's about how to communicate effectively with AI for desired results.
  3. There's also a report on how generative models like GPT might impact jobs. It shows that many workers could see changes in their tasks due to AI advancements.

S&T - Bret Victor, Realtalk and the ‘laboratory of the future’ vision [updated with proposal]

James W. Phillips' Newsletter • 78 implied HN points • 14 May 23
🕹 Technology Innovation Computing Visualization Collaboration
  1. Bret Victor envisions a future where the laboratory is a communal computational system.
  2. Personal computing history, led by figures like Alan Kay, envisioned computers as 'intellectual amplifiers'.
  3. Realtalk is a system where physical spaces are transformed into computational systems, allowing collaborative work without screens.

The Top Reasons High-Performing Teams Leverage User Stories

PeopleStorming • 59 implied HN points • 26 Oct 23
💼 Business Communication Prioritization Visualization Validation
  1. User stories are concise descriptions of value from the perspective of the person who desires that value, helping to keep focus on end user needs and goals.
  2. Leveraging user stories can lead to improved communication and collaboration within teams, customer-centricity, and easier prioritization of workloads.
  3. An effective user story typically consists of three parts: the role, the output, and the benefit, enabling teams to articulate the purpose of their work and prioritize effectively.

Introduction to R for Data Science (Part Seven Final)

The Software & Data Spectrum • 39 implied HN points • 06 Apr 23
🕹 Technology Data science Visualization Themes
  1. Boxplots are common for visualizing data like stock pricing, and you can customize them with colors and flips.
  2. Variable plotting can include heat maps to show occurrences, and you can adjust the appearance with features like scale_fill_gradient().
  3. Coordinate your graphs using functions like coord_cartesian() and facet them based on specific variables for more detailed insights.

Angels in the Architecture, pt IV

Premium Grind • 19 implied HN points • 19 Jan 24
🕹 Technology Research Visualization Data Analysis Software
  1. Interpreting VAS heatmaps is challenging due to lack of established guidelines and overlaps in definitions.
  2. Studies have shown that traditional civic architecture consistently draws more viewer attention than modern styles.
  3. Discrepancies exist between VAS results and actual human-subject eye-tracking studies, raising questions about accuracy and interpretation.

A Generative Week

Cybernetic Forests • 39 implied HN points • 19 Feb 23
🎨 Art & Illustration AI Art Generative Art Artificial Intelligence Visualization Experimental Art
  1. Eryk Salvaggio will be a guest on BBC 4's _Digital Humans_ radio show discussing creativity and automation in AI art
  2. Sensitive Noise is an AI-generated artwork exploring content moderation and human sensuality through censored images
  3. Apply for STORY x CODE, a residency program focusing on AI and Machine Learning in storytelling, filmmaking, and animation

Must Learn KQL Part 12: The Render Operator

Rod’s Blog • 19 implied HN points • 31 May 23
🕹 Technology Data Analysis Visualization Learning
  1. The Render operator in KQL allows you to turn data into visualizations like area graphs, bar charts, and pie charts among others.
  2. Using KQL to create visualizations is crucial for tasks like developing dashboards in Microsoft Sentinel, providing real-time insights to security teams.
  3. Learning to transform data into graphs and charts can make information more engaging and appealing, especially for visual or hands-on learners.

Using Visualization to create better solutions [Technique Tuesdays]

Technology Made Simple • 39 implied HN points • 16 Aug 22
🕹 Technology Visualization Math Computer Science Algorithms Learning
  1. Visualization helps in learning and problem-solving by making connections and identifying patterns.
  2. When visualizing complex ideas, start small by breaking down components and building up from there.
  3. Developing visualization skills requires a strong understanding of the concepts and practicing visualization techniques regularly.

Data Science Weekly - Issue 468

Data Science Weekly Newsletter • 19 implied HN points • 10 Nov 22
🕹 Technology Data science Machine Learning Artificial Intelligence Analytics Visualization
  1. If you're thinking about leaving Twitter, it's a good idea to save your data first. You can use it to find trends and insights that might be really useful later.
  2. Learning command-line data analytics can make your data processing much easier. There's a new tool called SPyQL that makes it simpler to work with and understand data on the command line.
  3. Federated learning allows us to train models using data from many users without needing to see the actual data. This means we can protect privacy while still making progress in AI.

Data Science Weekly - Issue 438

Data Science Weekly Newsletter • 19 implied HN points • 14 Apr 22
🕹 Technology Data science Machine Learning Artificial Intelligence Data Engineering Visualization
  1. The Modern Data Stack is becoming crucial for handling data, with many tools available to improve the way businesses work with data. It helps users understand how to start using these tools effectively.
  2. DeepMind's AlphaFold is revolutionizing biology by accurately predicting protein shapes. This technology is changing how researchers approach biological problems.
  3. There are better ways to visualize SQL joins than using Venn diagrams. New methods like the checkered flag diagram can make understanding joins easier and clearer.

Data Science Weekly - Issue 431

Data Science Weekly Newsletter • 19 implied HN points • 24 Feb 22
🕹 Technology Data science Machine Learning AI Statistics Visualization
  1. Vector databases are important for storing and searching data in various applications like image search and drug discovery.
  2. Statistics may not be the best path to becoming a data scientist; other fields could be more relevant and useful.
  3. Teaching and practicing reproducible workflows in data science helps ensure that research and findings can be verified and built upon.

Data Science Weekly - Issue 421

Data Science Weekly Newsletter • 19 implied HN points • 16 Dec 21
🕹 Technology Data science Machine Learning AI Visualization Programming
  1. Lee Wilkinson made a big impact in the field of interactive visualization. His work helped people better understand and create statistical graphics.
  2. A new journal for machine learning research is starting, aiming for quick and fair reviews. This will help share cutting-edge research in a transparent way.
  3. Feature engineering is still important in machine learning, despite the rise of deep learning. It turns out that creating good features can really boost model performance.

Data Science Weekly - Issue 417

Data Science Weekly Newsletter • 19 implied HN points • 18 Nov 21
🕹 Technology Data science Machine Learning AI Programming Visualization
  1. Brains are like prediction machines which help save energy. They do this by predicting what they will perceive in the world around them.
  2. AI is being used to help scientists study chimpanzee behavior in the wild. It can find important clips in hours of footage much faster than humans.
  3. Different approaches to AI governance exist between the EU and the US. This may affect how they collaborate on AI in the future.

Data Science Weekly - Issue 416

Data Science Weekly Newsletter • 19 implied HN points • 11 Nov 21
🕹 Technology Data science Machine Learning AI Visualization Computer Vision
  1. Mature machine learning systems can be tough to improve. Even with cutting-edge technology, you might find that new models don't perform better than old ones.
  2. Data drift and outlier detection are important for monitoring ML models. They help identify issues when you lack ground truth labels to compare against.
  3. Language models score how 'human' a sentence sounds. To train these models, you can analyze and convert language into probabilities.

Data Science Weekly - Issue 415

Data Science Weekly Newsletter • 19 implied HN points • 04 Nov 21
🕹 Technology Data science Machine Learning Artificial Intelligence Programming Visualization
  1. Audio signal processing is important for machine learning projects that involve sound. To analyze sound effectively, you need to convert it into spectrograms first.
  2. Algorithmic efficiency in deep learning has improved greatly, requiring much less computing power than before. This means we can train complex neural networks faster and more efficiently now.
  3. Understanding Gaussian processes can be complicated, but looking at them in different ways can help. Each perspective gives new insights and makes the concept easier to grasp.

The Lure of Graphical Programming

Bzogramming • 7 implied HN points • 13 Mar 23
🕹 Technology Programming Visualization Coding Tools Innovation
  1. Visual programming languages with colored boxes and lines may not necessarily make code easier to understand.
  2. Human vision focuses on categorizing small pieces of images at a time, similar to how code should be structured.
  3. Text-based programming already utilizes spatial conveyance of meaning through features like indentation, highlighting the importance of enhancing visual tools in coding.

Data Science Weekly - Issue 393

Data Science Weekly Newsletter • 19 implied HN points • 03 Jun 21
🕹 Technology Data science Machine Learning Algorithms Visualization Artificial Intelligence
  1. Generating coherent noise using Fourier transforms can create impressive 3D terrain effects. It's interesting to see how a complex math concept can produce realistic visuals.
  2. Deepfake technology can alter maps, which raises concerns about misinformation. It's a reminder to be cautious about what we see online.
  3. Learning data science should start with foundational knowledge, not just jumping into deep learning. Understanding basic concepts is key to building effective models.

Data Science Weekly - Issue 389

Data Science Weekly Newsletter • 19 implied HN points • 06 May 21
🕹 Technology Data science AI Machine Learning Visualization Ethics
  1. The San Pellegrino label creates a wavy pattern called the Moiré effect. It happens when two repeating patterns overlap in a way that makes them look interesting and dynamic.
  2. AI in healthcare is changing how we make medical decisions, but it's also raising important moral questions. These include concerns about losing the role of doctors and the potential for bias in AI systems.
  3. Observable Plot is a new tool that helps visualize data better and easier. It's built on D3 and is designed for those who want a smoother experience in exploring data.

Data Science Weekly - Issue 388

Data Science Weekly Newsletter • 19 implied HN points • 29 Apr 21
🕹 Technology Data science Machine Learning AI Visualization Statistics
  1. Cluster analysis can help identify groups in data, but knowing how many clusters to use is often tricky. A new method called a clustergram provides a better view of how observations flow between classes as you add more clusters.
  2. Bayesian and frequentist methods provide different types of statistical results that can't be directly compared. Each method answers different questions, so understanding their unique outputs is important.
  3. Netflix is tackling decision fatigue by developing a feature that automatically plays a show or movie when users open the app. This change aims to simplify the user experience.

Data Science Weekly - Issue 384

Data Science Weekly Newsletter • 19 implied HN points • 01 Apr 21
🕹 Technology Data science Machine Learning AI Ethics Optimization Visualization
  1. Maps are getting smarter with AI, offering real-time updates for traffic and information. This makes navigation easier and more efficient than ever before.
  2. It's important to stop labeling everything as AI. We need to focus more on creating useful machine learning systems that actually help people.
  3. Using data effectively can be tricky. Numbers can greatly influence policy, but relying solely on them can lead to problems.

Data Science Weekly - Issue 336

Data Science Weekly Newsletter • 19 implied HN points • 30 Apr 20
🕹 Technology Data science Artificial Intelligence Machine Learning Software Development Visualization
  1. Tornado plots are a unique way to visualize time series data, showing how values change over time. They help us understand trends in a different way than regular graphs.
  2. Categorizing diverse products efficiently is crucial for platforms like Shopify. Proper categorization helps users find similar products faster, making shopping easier.
  3. Blender is an open-source chatbot by Facebook AI that feels more human and engages users better. It's a leap forward for conversational AI technology.