The hottest Data Visualization Substack posts right now

And their main takeaways
Category
Top Technology Topics

Visualizing extrajudicial violence in Bangladesh

Alberto Cairo's The Art of Insight • 19 implied HN points • 25 Mar 24
🌍 World Politics International relations Human Rights Journalism Data Visualization
  1. Investigative journalism is still thriving worldwide, producing important work even in tough conditions. Journalists work hard to uncover truths, showcasing their dedication and creativity.
  2. In Bangladesh, extrajudicial killings by security forces have surged, especially around election times. Reports show over 2,500 cases of violence in recent years, emphasizing the seriousness of the issue.
  3. Innovative visual storytelling, like the project by Nazmul Ahasan, brings attention to these serious topics. Combining solid research with engaging graphics helps people understand and connect with the information.

Using LC0's WDL to Analyze Games

Chess Engine Lab • 19 implied HN points • 23 Mar 24
🕹 Technology Chess Analysis Artificial Intelligence Data Visualization Gaming
  1. Analyzing chess games using LC0's WDL can provide a more insightful overview of the game compared to centipawn graphs.
  2. Increasing the number of nodes per move in analysis results in spikier graphs, showing more extreme evaluations; finding a balance between accuracy and relevance to human play is important.
  3. Using WDL contempt values in LC0 analysis can adjust the winning probabilities based on player ratings, offering a new perspective on game outcomes.
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An interview in 'Significance' magazine

Alberto Cairo's The Art of Insight • 19 implied HN points • 11 Mar 24
🎨 Art & Illustration Data Visualization Graphic design Creative Process Visual Arts Communication
  1. Learning basic rules of data visualization helps you make better choices but it's also important to know that there aren't hard and fast rules. Understanding conventions allows you to decide how to present data effectively.
  2. Using a bar graph is often better than a pie chart for comparing numbers, but beyond that, your choices matter more than following strict rules.
  3. The key is to use the knowledge you've gained about perception and cognition to guide your decisions, creating a unique approach to data visualization.

Introducing Orchestra: rapidly Build and Monitor Data and AI Products

The Orchestra Data Leadership Newsletter • 19 implied HN points • 07 Mar 24
🕹 Technology Data Management Artificial Intelligence Software Engineering Data Visualization Cloud Computing
  1. Launching a free tier for Orchestra, a tool to build and monitor data and AI products, offering a lightweight approach to improving business value and AI integration.
  2. Addressing the challenges faced by data teams in balancing business value and software engineering best practices through tools like Nessie, dbt, and emerging 'as-code' BI platforms.
  3. Providing an end-to-end platform with features like declarative pipelines, data quality monitoring, granular alert control, and asset-based data lineage to empower data teams in accelerating their initiatives.

GroupBy #25: From Samza to Flink: A Decade of Stream Processing, DoorDash’s In-House Search Engine,Meta's DotSlash, Designing Metrics Trees

VuTrinh. • 19 implied HN points • 05 Mar 24
🕹 Technology Data Engineering Software Development Stream Processing Data Visualization
  1. Stream processing has evolved significantly over the years, with frameworks like Samza and Flink leading the way in handling real-time data streams.
  2. DoorDash developed its own search engine using Apache Lucene, achieving impressive performance improvements, like reduced latency and lower hardware costs.
  3. Understanding metrics trees is essential for businesses as they visually represent how different inputs contribute to outputs, helping in decision-making.

Does Reality Have a Surprising _Lack_ of Detail?

Breaking Smart • 45 implied HN points • 16 Feb 24
🕹 Technology Artificial Intelligence Coding Data Visualization
  1. The essay discussed contrasting viewpoints on the level of detail present in reality, questioning if there might actually be a surprising lack of detail.
  2. The post highlighted two major AI developments, Sora and Gemini 1.5, emphasizing the importance of boring inference advances over flashy training advances.
  3. The complexity of reality and the intricacies of AI advancements were juxtaposed with simple examples, prompting readers to reconsider their perceptions about reality's level of detail.

A Brief History of Static

Cybernetic Forests • 59 implied HN points • 29 Jan 23
🎨 Art & Illustration AI Art Data Visualization Digital Art
  1. Refik Anadol's AI art piece, 'Unsupervised,' at MoMA uses AI to interpret and reimagine the history of modern art, creating a mesh of pixelated visuals.
  2. Interpolation in AI refers to filling in the gaps between data points or images, creating a smooth transition and possible new variations.
  3. The concept of interpolation extends to creating a connection and kinship between disparate entities in an artistic representation, showcasing the latent possibilities in the in-between spaces.

Pragmatic Prompting With Python Plotly and GPT-4: Telling A Story With UN Literacy Data

Data at Depth • 19 implied HN points • 29 Jan 24
🕹 Technology Data Visualization Artificial Intelligence Programming
  1. The post discusses using GPT-4 to streamline the creation of Python Plotly code for interactive data visualization.
  2. The author mentions being a computer science professor who also engages in using GPT-4 for data visualization code creation.
  3. GPT-4 has shown significant improvement in its ability to generate Python Plotly code for visualizing data interactively.

Visualise & Discover RAG Data

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 19 implied HN points • 23 Jan 24
🕹 Technology AI Data science Software Development Machine Learning Data Visualization
  1. RAGxplorer is a tool that helps visualize and explore data chunks, making it easier to understand how they relate to different topics.
  2. The process of Retrieval-Augmented Generation (RAG) involves breaking documents into smaller chunks to improve how data is retrieved and used with language models.
  3. Visualizing data can help identify problems like missing information or unexpected results, allowing users to refine their questions or understand their data better.

Three data visualisation tips

A Bit Gamey • 27 implied HN points • 17 Mar 24
🪑 Design Data Visualization Graphic design
  1. Maximize the data-ink ratio by minimizing non-informative ink like excessive grid lines and decorations, to enhance clarity and comprehension.
  2. Align graphic components to create stronger organization and cohesion in design, ensuring nothing is placed arbitrarily.
  3. Utilize small multiples technique to present series of similar graphics or charts in a grid format, enabling easy comparison and revealing patterns within the dataset.

High Impact Python Streamlit: Beautiful Interactive Maps and Charts

Data at Depth • 5 HN points • 15 May 24
🕹 Technology Data Visualization Python Web Development
  1. Creating an interactive Streamlit dashboard can be done step by step with a modular approach, allowing users to select a year, view a global choropleth map, and see a horizontal bar chart of top 10 countries.
  2. By using Python libraries like Streamlit, Pandas, and Plotly Express, you can efficiently build interactive data visualizations for a dashboard project.
  3. Data preprocessing steps, such as filtering, cleaning, and extracting necessary information, are essential before visualizing data on the dashboard using tools like Plotly Express for map and chart creation.

GPT-4 for No-code Data Visualization Reports: CSV to PDF in No Time Flat

Data at Depth • 19 implied HN points • 23 Nov 23
🕹 Technology AI Data Visualization GPT-4
  1. GPT-4 can create comprehensive PDF data visualization reports from CSV files on-the-fly, directly in its interface.
  2. Recent updates in the GPT-4 interface have introduced this new capability to generate PDF files quickly and efficiently.
  3. Readers can get a 7-day free trial to access more content and explore the full archive of posts on Data at Depth.

Interactive Mapping Tutorial With Python: Visualizing US Education Trends

Data at Depth • 19 implied HN points • 08 Jun 23
🕹 Technology Data Analysis Data Visualization Python
  1. Data visualization skills are crucial for modern data analysis, and mapping skills are a valuable addition to visualization abilities.
  2. Python libraries like Folium, Plotly, and Dash can be used for effective display of data.
  3. Interactive mapping tutorials using Python can help in visualizing US education trends with tools like Folium, Plotly, and Dash.

Must Learn KQL Part 13: The Extend Operator

Rod’s Blog • 19 implied HN points • 31 May 23
🕹 Technology Data Analysis Programming Data Visualization
  1. Custom data views in KQL are crucial for tailoring information to each environment's unique requirements for security and operations.
  2. The Extend operator in KQL allows users to create custom columns in real-time for query results, enhancing data analysis and presentation.
  3. By using the Extend operator, it's possible to generate calculated columns, append them to results, and combine existing data to display meaningful information in KQL queries.

Prompt Engineering For Rapid Data Visualization with GPT-4 and Python

Data at Depth • 19 implied HN points • 11 Jun 23
🕹 Technology Data Visualization Artificial Intelligence Coding
  1. Using GPT-4 for prompt engineering simplifies Python coding for complex data visualizations by providing concise instructions and reducing troubleshooting time.
  2. GPT-4 allows focusing on implementing solutions rather than dealing with lower-level coding details.
  3. Integration of GPT-4 with Python streamlines the process of creating interactive data visualizations, making it faster and more efficient.

✋ Newsletter #38: Me Too

Women On Rails Newsletter - International Version • 19 implied HN points • 03 Nov 22
🕹 Technology Web Development Software Development Ruby on Rails Algorithms Data Visualization
  1. The newsletter discusses a case of justice served in a #MeToo context, emphasizing the importance of identifying and addressing abnormal situations in professional environments.
  2. The community encourages creating safe spaces, advocating for victims of sexual violence, and providing support for legal processes.
  3. Recommendations are offered for joining women-centered Ruby communities, along with resources for building sustainable digital products and insights on improving team workflows.

Data Science Weekly - Issue 462

Data Science Weekly Newsletter • 19 implied HN points • 29 Sep 22
🕹 Technology Data science Artificial Intelligence Machine Learning Programming Data Visualization
  1. Teaching students about scientific failure helps them build resilience. It prepares them for real-world challenges in research.
  2. Understanding uncertainty in deep learning models is crucial for effective use. It helps in making better predictions and decisions.
  3. Increasing data maturity in organizations leads to more strategic use of data. Assessing data maturity can guide teams in improving their data practices.

Data Science Weekly - Issue 460

Data Science Weekly Newsletter • 19 implied HN points • 15 Sep 22
🕹 Technology Data science Machine Learning Artificial Intelligence Software Engineering Data Visualization
  1. Soft skills are super important for data scientists. Being able to communicate well and work in a team can make a big difference in their effectiveness.
  2. There are great resources available online for learning data science, including live streams on platforms like Twitch. It’s a fun way to learn and engage with others.
  3. Use the right fonts and designs in data visualizations. They can greatly affect how your data is understood and appreciated.

Data Science Weekly - Issue 454

Data Science Weekly Newsletter • 19 implied HN points • 04 Aug 22
🕹 Technology Data science Machine Learning Artificial Intelligence Remote Sensing Data Visualization Big Data
  1. NASA is using machine learning to organize millions of astronaut photos of Earth. This technology helps scientists access and study these images more effectively.
  2. Data-driven companies can have a competitive edge in the market. The right expertise and data strategy can influence investors' decisions.
  3. There are many resources and discussions available online about using machine learning and data science effectively. Engaging with these can help keep skills and knowledge up to date.

Data Science Weekly - Issue 443

Data Science Weekly Newsletter • 19 implied HN points • 19 May 22
🕹 Technology Data science Artificial Intelligence Machine Learning Software Development Data Visualization
  1. Data scientists should improve their software development skills by learning about project structure, testing, reproducibility, and version control.
  2. AI-generated artwork may not be considered true art because it lacks the communication and consciousness involved in traditional art creation.
  3. Using optimized tools like DuckDB can enhance the data processing experience by making it faster and easier to work with large datasets.

Data Science Weekly - Issue 442

Data Science Weekly Newsletter • 19 implied HN points • 12 May 22
🕹 Technology Data science Machine Learning Artificial Intelligence Data Visualization NLP
  1. Splitting data into training, testing, and validation sets is crucial for building effective machine learning models. It helps ensure that we evaluate our models properly.
  2. Bandit algorithms can improve recommender systems by balancing exploration of new items and exploitation of known user preferences. This way, they can discover hidden gems instead of just repeating popular choices.
  3. Protecting machine learning models and their intellectual property is important, and best practices are still evolving. It's useful to stay updated on strategies to safeguard your work in this fast-changing field.

[In case you missed it] Data Science Weekly - Issue 439

Data Science Weekly Newsletter • 19 implied HN points • 24 Apr 22
🕹 Technology Data science Machine Learning Artificial Intelligence Data Visualization Software Development
  1. Building a recommendation system is challenging. It requires careful planning and execution to serve users quickly and efficiently.
  2. Understanding different probability distributions is essential in data science. They help us make better predictions and understand the variability in our data.
  3. Contrastive learning is an important method for training machine learning models. Recent advances in this area can improve how we represent data and solve complex problems.

Data Science Weekly - Issue 439

Data Science Weekly Newsletter • 19 implied HN points • 21 Apr 22
🕹 Technology Data science Machine Learning AI Data Visualization Statistical Analysis
  1. Building recommendation systems requires careful planning and quick processing to handle live requests effectively. It's not just about creating a model but also about deploying it at scale.
  2. Contrastive learning is a powerful technique in machine learning that helps in improving model performance. New insights in this area can lead to better model training and application.
  3. Understanding different probability distributions is crucial in data science. It helps in modeling data accurately and predicting outcomes better.

Waterfall Chart

Visualization For Science • 13 implied HN points • 26 Feb 23
💰 Finance Economics Charts Data Visualization
  1. Waterfall charts are floating column diagrams that emphasize increases and decreases of a quantity.
  2. Gross Domestic Product is a common measure of the economic health, representing the value of goods and services produced and sold.
  3. Complex metrics like GDP are composed of many individual components, each making its contribution.

Data Science Weekly - Issue 420

Data Science Weekly Newsletter • 19 implied HN points • 09 Dec 21
🕹 Technology Data science Machine Learning Artificial Intelligence Data Visualization Open Source
  1. D3 is a powerful tool for data visualization that has lasted over a decade. Its success is attributed to its flexibility and the community support it receives.
  2. Building AI models like open-source software can make these models better and more collaborative. This means involving a wider community in their development.
  3. Automated decision-making systems can still reflect human biases, which shows that technology doesn't always solve fairness issues.

Data Science Weekly - Issue 402

Data Science Weekly Newsletter • 19 implied HN points • 05 Aug 21
🕹 Technology Data science Machine Learning Open Source Data Visualization AI Ethics
  1. Visualizing your code can help you understand its structure easily. It's a useful way to see what's happening in a GitHub repository at a glance.
  2. AI ethics should be understood by everyone in an organization, not just data scientists. This awareness can help prevent risks and guide better decisions.
  3. If you want to build a successful AI project, learn from those who have done it. They often share important lessons that can help others achieve similar success.

The problem with isochrones: overcoming false precision in drive time maps

Expand Mapping with Mike Morrow • 3 HN points • 15 Dec 23
🔬 Science Mapping Accuracy Uncertainty Transport Data Visualization
  1. Drive time isochrones can give a false sense of precision because they are based on average traffic conditions, which can vary greatly.
  2. Improving isochrone accuracy can be done by increasing the number of trips, testing different conditions, and varied departure angles.
  3. To better communicate uncertainty, consider simplifying isochrone shapes and creating bands to show the range of possible outcomes.

Data Science Weekly - Issue 386

Data Science Weekly Newsletter • 19 implied HN points • 15 Apr 21
🕹 Technology Data science Machine Learning Artificial Intelligence Data Visualization Programming
  1. Accessibility in data visualization is important. Tools like Chartability help ensure that everyone can understand data, especially people with disabilities.
  2. Graph Neural Networks (GNNs) are a powerful tool for analyzing data, but their effectiveness can vary depending on how they use features and edges.
  3. There's a growing need for data observability. Companies must ensure data quality and avoid issues like missing or duplicate data as they handle more complex data pipelines.

Data Science Weekly - Issue 382

Data Science Weekly Newsletter • 19 implied HN points • 18 Mar 21
🕹 Technology Data science Machine Learning Artificial Intelligence Statistical Modeling Data Visualization
  1. Computers will never truly understand or create good literature. They lack the ability to appreciate and express the complexities of human writing.
  2. Color scales are important in data visualization. Choosing the right color can make your data easier to understand and communicate.
  3. Data documentation and organization are crucial for effective data management. Having a clear framework helps teams work better and ensures everyone understands the data.

Data Science Weekly - Issue 381

Data Science Weekly Newsletter • 19 implied HN points • 11 Mar 21
🕹 Technology Data science Machine Learning Artificial Intelligence Data Visualization Data Sharing
  1. COVID-19 skeptics use data and social media to promote their views. A study analyzed tweets and visual data to uncover their strategies.
  2. New reports on AI development show that the COVID-19 pandemic has impacted research and hiring in this field. It highlights how AI technology is being utilized in health-related areas.
  3. Machine learning can struggle with new data it wasn't trained on. Research is ongoing to improve its reliability and performance in real-world situations.