The hottest Data Visualization Substack posts right now

And their main takeaways
Category
Top Technology Topics

Modular Prompting GPT-4 For Interactive Multi-visual Python Dashboards: A Detailed Guide

Data at Depth • 39 implied HN points • 04 Jan 24
🕹 Technology Data Visualization
  1. The article discusses using GPT-4 to generate Python Plotly code for interactive data visuals in Python dashboards.
  2. The author shares their experience of how GPT-4 has significantly improved over 8 months in creating Python Plotly dashboard code.
  3. There's an opportunity to access the full post archives with a 7-day free trial subscription to 'Data at Depth.'

Interactive Maps and Plots With GPT-4 & Plotly Dash: A Story about UN Population Projections

Data at Depth • 39 implied HN points • 31 Dec 23
🕹 Technology Data Visualization
  1. Interactive maps and plots can now be created using GPT-4 and Plotly Dash, enhancing data visualization capabilities in Python.
  2. GPT-4's capacity to generate interactive Python Plotly dashboards has significantly improved in recent months, showcasing advancements in AI technology.
  3. Computer science professors have utilized GPT-4 to explore its Python data visualization code creation abilities, pushing the boundaries of AI in this field.

ASA lecture

Chartography • 58 implied HN points • 18 Jul 23
🚌 Education Data Visualization
  1. A seminar by RJ Andrews on data visualization is happening this Thursday at the American Statistical Association
  2. Join the virtual tour of spectacular information graphics by registering for the ASA seminar
  3. The American Statistical Association has a rich history in data visualization, featuring leaders like Florence Nightingale
Get a weekly roundup of the best Substack posts, by hacker news affinity:

Visualizing extrajudicial violence in Bangladesh

Alberto Cairo's The Art of Insight • 19 implied HN points • 25 Mar 24
🌍 World Politics 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 Data Visualization
  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.

An interview in 'Significance' magazine

Alberto Cairo's The Art of Insight • 19 implied HN points • 11 Mar 24
🎨 Art & Illustration Data Visualization
  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 Visualization
  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 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.

A Brief History of Static

Cybernetic Forests • 59 implied HN points • 29 Jan 23
🎨 Art & Illustration Data Visualization
  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
  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 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.

Job listing: Assistant Sports Analyst

Silver Bulletin • 30 implied HN points • 26 Feb 25
🎾 Sports Data Visualization
  1. An Assistant Sports Analyst position is open, mostly focusing on improving sports models for NFL, NBA, and college basketball. It's part-time and could turn into full-time.
  2. Candidates need skills in Stata, Python, and data analysis, along with a strong interest in sports. Pay ranges from $40-50 per hour, depending on work done.
  3. To apply, email with your materials and be prepared for interviews in early April. The deadline to apply is March 25, 2024.

High Impact Python Streamlit: Beautiful Interactive Maps and Charts

Data at Depth • 5 HN points • 15 May 24
🕹 Technology Data Visualization
  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 Data Visualization
  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.

A Song of Junk and Value

davidj.substack • 95 implied HN points • 03 Jan 24
🕹 Technology Data Visualization
  1. Data dashboards can become like old, unused bookmarks, cluttering up space.
  2. Having standard data models and a semantic layer could lead to a more efficient data analysis experience.
  3. It's important to focus on creating value in data analysis by asking complex questions and optimizing processes.

($) Tracking Global AI Talent Flow

Interconnected • 77 implied HN points • 08 Mar 24
🕹 Technology Data Visualization
  1. China is producing a significant amount of AI talent at the undergraduate level, with many choosing to stay in the country for graduate studies and work.
  2. Tracking AI talent flow through conferences like NeurIPS provides valuable insights into global trends and migration patterns.
  3. Understanding the definition and limitations of how AI talent is measured is crucial when interpreting and drawing conclusions from talent tracking analyses.

Interactive Mapping Tutorial With Python: Visualizing US Education Trends

Data at Depth • 19 implied HN points • 08 Jun 23
🕹 Technology Data Visualization
  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 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
  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.

Does Reality Have a Surprising _Lack_ of Detail?

Breaking Smart • 45 implied HN points • 16 Feb 24
🕹 Technology 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.

✋ Newsletter #38: Me Too

Women On Rails Newsletter - International Version • 19 implied HN points • 03 Nov 22
🕹 Technology 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.

Three data visualisation tips

A Bit Gamey • 27 implied HN points • 17 Mar 24
🪑 Design Data Visualization
  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.

Product Digest: Latch Plots, Protein Engineering and GPU-enabled Bioinformatics

LatchBio • 12 implied HN points • 13 Nov 24
🕹 Technology Data Visualization
  1. Latch Bio offers a new Protein Engineering Toolkit with over 16 tools that help create and analyze proteins. This means scientists can now design better drugs and enzymes more easily.
  2. The new software called Latch Plots makes it easier for scientists to visualize biological data. It allows them to create dynamic graphs and analyze data from various sources without much hassle.
  3. Using GPU technology in bioinformatics speeds up data processing significantly. This upgrade allows researchers to analyze large datasets quickly, which is essential for drug discovery and many research projects.

Data Science Weekly - Issue 462

Data Science Weekly Newsletter • 19 implied HN points • 29 Sep 22
🕹 Technology 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 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 Visualization
  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 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 Visualization
  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 Visualization
  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 Visualization
  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.