The hottest Visualization Substack posts right now

And their main takeaways
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Top Health & Wellness Topics

Data Science Weekly - Issue 201

Data Science Weekly Newsletter • 19 implied HN points • 28 Sep 17
🕹 Technology Data science Machine Learning AI Programming Visualization
  1. Linear programming can help optimize diets for better health. It's about finding the best balance of food for weight loss and longevity.
  2. Understanding the risk of extreme weather events, like floods, can help cities prepare better. It's important to question outdated models when they don't match recent data.
  3. AI and machine learning are changing design fields, like web design, by enabling automated creation. This could make building websites easier and more efficient.

Data Science Weekly - Issue 143

Data Science Weekly Newsletter • 19 implied HN points • 18 Aug 16
🕹 Technology Data science Machine Learning Artificial Intelligence Programming Visualization
  1. Machine learning can help analyze personal health data, like weight, by tracking various factors that affect it. Keeping a simple record, like a CSV file, can make this process easier.
  2. There are creative ways to visualize data, like global shipping traffic or Olympic medals, which can make insights more engaging. Using tools like GIFs can bring data to life.
  3. Combining different programming languages, like Python and R, can enhance data science work instead of arguing about which one is better. Each has its strengths and can be used together effectively.

Data Science Weekly - Issue 132

Data Science Weekly Newsletter • 19 implied HN points • 02 Jun 16
🕹 Technology Data science Machine Learning AI Visualization Software Engineering
  1. There's a new visual search engine for scientific diagrams that helps analyze and categorize images. This can make researching easier for scientists.
  2. Using emojis can help create a fun and memorable cheatsheet for machine learning concepts. Combining personal interests with learning tools can enhance retention.
  3. Data-driven storytelling is important for making impactful narratives. Workshops on this topic can help people learn the best practices for sharing data stories.

Data Science Weekly - Issue 119

Data Science Weekly Newsletter • 19 implied HN points • 03 Mar 16
🕹 Technology Data science Artificial Intelligence Machine Learning Natural Language Processing Visualization
  1. Data science can reveal hidden insights, like analyzing the language used in presidential debates to understand candidates better.
  2. AI is becoming more creative, as seen when Google's AI sold art for charity, showing its ability to create valuable pieces.
  3. Social media data can tell interesting stories, like an interactive map of Instagram posts in Hong Kong which shows the city's life based on user activity.
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Data Science Weekly - Issue 50

Data Science Weekly Newsletter • 19 implied HN points • 06 Nov 14
🕹 Technology Data science Machine Learning Artificial Intelligence Statistical Analysis Visualization
  1. Learning about neural networks can start from the basics before diving into complex topics. It's helpful to understand the core concepts first.
  2. Visualizing data is important for understanding text data better. There are interactive tools available that can help with this.
  3. Choosing the right statistical analysis method is crucial for data science. There are guides that can help you figure out which analysis to use based on your data.

Data Science Weekly - Issue 32

Data Science Weekly Newsletter • 19 implied HN points • 03 Jul 14
🕹 Technology Data science Machine Learning Algorithms Visualization Big Data
  1. Visualization helps explain algorithms better. It's not just about graphs; it's about showing how logical rules work.
  2. Research shows there are ideal lengths for online content, like tweets and titles. Keeping things concise can improve engagement.
  3. Big data can have problems like inaccuracies and outdated info. This makes it challenging for companies and researchers to get reliable insights.

Data Science Weekly - Issue 11

Data Science Weekly Newsletter • 19 implied HN points • 06 Feb 14
🕹 Technology Data science Machine Learning Big Data Visualization Analytics Artificial Intelligence
  1. Data visualization is important in data science, especially for large-scale projects. It helps people understand data flows and make better decisions.
  2. Bringing machine learning models from a lab to real-world applications is crucial for impact. This requires integrating tools and strategies to analyze data in production.
  3. Learning about user experience and changing tastes is key for making good product recommendations. It's important to consider what users will enjoy now and in the future.

[in case you missed it] Data Science Weekly - Issue 384

Data Science Weekly Newsletter • 0 implied HN points • 04 Apr 21
🕹 Technology AI Data science Machine Learning Visualization NLP
  1. AI is improving tools like Google Maps, making them smarter and more helpful with real-time updates.
  2. It's important to focus on building effective machine learning systems that provide real value, instead of just labeling everything as AI.
  3. Data can be powerful for decision-making, but relying too heavily on numbers can lead to mistakes and misinterpretation.

[in case you missed it] Data Science Weekly - Issue 382

Data Science Weekly Newsletter • 0 implied HN points • 21 Mar 21
🕹 Technology Data science AI Machine Learning Statistics Visualization
  1. Computers can't write good stories. It's a big claim, but they really don't understand literature like humans do.
  2. Using color scales is important when showing data visually. Choosing the right colors can make your data easier to understand.
  3. Data science can help fight illegal fishing with satellite data. By tracking boats, experts can prevent unlawful activities in our oceans.

[in case you missed it] Data Science Weekly - Issue 318

Data Science Weekly Newsletter • 0 implied HN points • 28 Dec 19
🕹 Technology Data science Machine Learning AI Visualization Programming
  1. Data visualization tools help us understand complex data better. New projects like VisualizeMnist and butterfly datasets show exciting ways to use these tools.
  2. AI is becoming powerful in games, as seen with Pluribus, an AI that beats professional poker players. This success highlights the advancements in AI competition.
  3. Learning the math behind neural networks is important. Resources are available to help demystify the concepts, making it easier for beginners to grasp.

Unlocking Insights from 11 Million Game Moves: Data Engineering and Visualization

Encyclopedia Autonomica • 0 implied HN points • 31 Oct 24
🕹 Technology Data Engineering Visualization Machine Learning Artificial Intelligence Game Theory
  1. Data engineering is super important for AI systems. If we want AI agents to work well, they need structured data so they can learn and make decisions.
  2. Different data storage formats have their pros and cons. Formats like JSON and Parquet can help manage large datasets effectively, while CSVs can be limiting.
  3. Visualizing data can help us understand it better. Using tools like heatmaps and graphs makes it easier to see patterns and insights from complex game data.

Prédire le futur ? La technique étrangement efficace de Debbie Millman

Mind Fooled • 0 implied HN points • 03 Dec 23
🏥 Health & Wellness Personal Development Visualization Product Design
  1. You can predict and shape your own future by following a specific technique.
  2. The technique involves writing a detailed description of a day in your life 5 to 15 years in the future, focusing on details and success.
  3. Clarity and intention setting are powerful tools in creating a future you desire, whether personally or in product development.

Must Learn KQL Part 6: Interface Intimacy

Rod’s Blog • 0 implied HN points • 31 May 23
🕹 Technology Data Analysis User Interface Query Language Visualization
  1. Understanding the User Interface (UI) is crucial when starting with Kusto Query Language (KQL) as it provides a visual way to interact with the data.
  2. Filtering, sorting, grouping, selecting columns, and setting time ranges are important functions within the UI for manipulating and viewing data effectively.
  3. The UI also offers features like saving queries, sharing queries, formatting queries, exporting query results, creating alert rules, pinning visualizations, and utilizing keyboard shortcuts for efficient query development.

Professor’s Pick: 5 Essential Books for Python Data Analysis and Visualization

Data at Depth • 0 implied HN points • 29 May 23
🚌 Education Python Data Analysis Visualization
  1. Being proficient in Python, data analysis, and storytelling can give you a competitive edge in today's data-driven world.
  2. Having guidance from experienced professionals can help you identify the most impactful resources for mastering Python data analysis and visualization.
  3. Consider exploring the recommended essential books and leveraging a 7-day free trial to delve deeper into the topic.

6 𝐰𝐚𝐲𝐬 to turn code into beautiful Architectural Diagrams

Better Engineers • 0 implied HN points • 01 Aug 22
🕹 Technology Software Development Engineering Design Visualization
  1. You can turn your code into diagrams using different tools. This helps visualize and understand system architectures better.
  2. Tools like Diagrams, Mermaid, and PlantUML allow you to create diagrams with simple text and code. They make it easier to track changes and modify designs.
  3. Using diagramming tools can improve communication in tech teams by providing clear visual representations of complex systems.

[in case you missed it] Data Science Weekly - Issue 393

Data Science Weekly Newsletter • 0 implied HN points • 06 Jun 21
🕹 Technology Data science Machine Learning Artificial Intelligence Cyber Security Visualization
  1. Fourier transforms can create 3D terrain or noise, and understanding how this works can be a fun and interesting challenge.
  2. Interactive tools are available to visualize complex data concepts like Gaussian processes, making it easier to grasp difficult ideas.
  3. Machine learning has potential issues in healthcare; it's important to approach this field carefully and thoughtfully.

[in case you missed it] Data Science Weekly - Issue 389

Data Science Weekly Newsletter • 0 implied HN points • 09 May 21
🕹 Technology Data science AI Machine Learning Visualization Ethics
  1. Artificial intelligence is changing healthcare but raises important ethical questions, like the risk of bias and loss of doctors' decision-making power.
  2. Observable Plot is a new library designed to make data visualization easier and more enjoyable, built on the foundations of D3.
  3. Using SQL for data analysis can be very efficient, and it's worth remembering its capabilities compared to popular tools like Pandas.

[in case you missed it] Data Science Weekly - Issue 386

Data Science Weekly Newsletter • 0 implied HN points • 18 Apr 21
🕹 Technology Data science Machine Learning AI Accessibility Visualization
  1. Chartability focuses on making data visuals more accessible for people with disabilities. It's about ensuring everyone can understand the information presented.
  2. Data observability is important as companies handle more data, helping them maintain data quality. This can prevent issues like missing or stale data from affecting business decisions.
  3. Using advanced learning techniques like Graph Neural Networks can improve how we process complex data structures. These techniques can reveal deeper insights into various systems.