The hottest Data Visualization Substack posts right now

And their main takeaways
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Top Technology Topics

Data Science Weekly - Issue 378

Data Science Weekly Newsletter • 19 implied HN points • 18 Feb 21
🕹 Technology Data science Machine Learning Artificial Intelligence Data Visualization
  1. Creating morals in robots can be similar to parenting techniques, which raises interesting questions about how we teach values to machines.
  2. There is a growing collection of data science podcasts available, making it easy for enthusiasts to find quality content and stay updated in the field.
  3. Research is exploring better and more stable methods for training neural networks, which could improve how computers learn and function like human brains.

Segmentation Tooling winners + new $50,000 prize!

Vesuvius Challenge • 3 implied HN points • 27 Jun 23
🕹 Technology Software Development Open Source Data Visualization Programming
  1. There is a $50,000 Segmentation Tooling Prize to incentivize the building of open source software to help segment scrolls.
  2. The winners of the first prize developed innovative tools like Optical Flow Segmentation and Khartes.
  3. A new $50,000 Segmentation Tooling Prize 2 has been announced with a deadline of September 15, 2023.

Data Science Weekly - Issue 319

Data Science Weekly Newsletter • 19 implied HN points • 02 Jan 20
🕹 Technology Data science Machine Learning AI Research Data Visualization Programming
  1. AI can help detect cancer in mammograms better than humans, which shows the growing role of technology in healthcare.
  2. Working on data projects can help new data scientists stand out to employers and improve their skills.
  3. The AI research community needs to improve transparency by sharing their work, which can help advance the field.

Data Science Weekly - Issue 318

Data Science Weekly Newsletter • 19 implied HN points • 26 Dec 19
🕹 Technology Data science Machine Learning Artificial Intelligence Data Visualization Programming
  1. Visualizing data is important. Tools like MNIST and butterfly datasets help us see patterns and improve recognition using machine learning.
  2. AI is making strides in complex games, like poker. There are now AI that can beat expert players, showing how advanced it's become.
  3. Learning and understanding the math behind neural networks is crucial. It helps us grasp how these systems work and improve our data analysis skills.
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Data Science Weekly - Issue 316

Data Science Weekly Newsletter • 19 implied HN points • 12 Dec 19
🕹 Technology Data science Machine Learning Artificial Intelligence Programming Languages Data Visualization
  1. NeurIPS 2019 saw a huge increase in submissions, with over 6,700 entries and a 21.6% acceptance rate. This shows how popular and competitive the field of data science has become.
  2. Data Science teams often use both R and Python together, but merging them can be challenging. Finding ways to integrate these languages can help teams be more effective in their projects.
  3. A new method has been discovered for understanding quadratic equations, making it easier for students who struggled with the traditional formula. This could change how math concepts are taught.

Data Science Weekly - Issue 315

Data Science Weekly Newsletter • 19 implied HN points • 05 Dec 19
🕹 Technology AI Machine Learning Data science Data Visualization Neural Networks
  1. New technology is helping scientists study animals more effectively, but it's also creating a lot of data to handle.
  2. Machine learning tools are still complex and unique, making it tough for researchers to replicate their work easily.
  3. Recent advancements in machine learning are uncovering historical authorship details, like who wrote parts of Shakespeare's plays.

Seeing Centuries

Chartography • 1 HN point • 17 May 23
🎨 Art & Illustration Data Visualization History Information graphics
  1. Visualize history as a succession of 25-year generations with each generation represented by a single significant person.
  2. When creating historical data visualizations, identifying key dates, not just lifespans, is crucial for accurately representing each era.
  3. Leveraging technology, like AI, can help organize historical data and simplify the process of creating complex visual representations.

Data Science Weekly - Issue 267

Data Science Weekly Newsletter • 19 implied HN points • 03 Jan 19
🕹 Technology Data science Artificial Intelligence Machine Learning Statistics Data Visualization
  1. Understanding probability and statistics can be made easier with visual tools, like those offered by Seeing Theory.
  2. Machine learning has significant potential in healthcare, including improving diagnoses and assisting doctors with data.
  3. There's a strong link between social mobility and family background, suggesting our parents' status can greatly impact our own opportunities.

Data Science Weekly - Issue 257

Data Science Weekly Newsletter • 19 implied HN points • 25 Oct 18
🕹 Technology Data science Machine Learning Artificial Intelligence Data Visualization Big Data
  1. Neural networks can help create fun and unique Halloween costumes. Using AI for creative tasks can lead to new ideas we might not think of ourselves.
  2. Uber processes massive amounts of data very quickly, showing how big data can improve services and make operations smoother. Their platform manages over 100 petabytes of information.
  3. Learning data science can be made easier with mentorship and flexible payment options. Programs like Springboard's help you get job-ready skills while supporting your career journey.

Data Science Weekly - Issue 254

Data Science Weekly Newsletter • 19 implied HN points • 04 Oct 18
🕹 Technology Data science Machine Learning AI Data Visualization Deep Learning
  1. You can calculate the age of the universe using SQL to analyze data from various databases. It's easier than it sounds and can lead to interesting insights.
  2. Training deep learning models on phones and other small devices is now possible but still challenging. There are teams making it work, but the tools available aren't very user-friendly yet.
  3. Big data is starting to change genetic research a lot. New techniques are creating huge amounts of data, which helps scientists discover new things but also keeps them busy trying to catch up.

Data Science Weekly - Issue 238

Data Science Weekly Newsletter • 19 implied HN points • 14 Jun 18
🕹 Technology Machine Learning Data science AI Development Statistical Analysis Data Visualization
  1. Neural networks can struggle to tell jokes if they don't have enough examples to learn from. Giving them more data might help improve their humor.
  2. Machine learning is becoming more efficient with smaller, low-power chips, which could solve many current problems. This trend is expected to grow in the future.
  3. Data cleaning takes a lot of time in data science, with up to 80% of the effort spent on it. Learning tools like Python's Pandas can really help with this task.

Data Science Weekly - Issue 228

Data Science Weekly Newsletter • 19 implied HN points • 05 Apr 18
🕹 Technology Data science Machine Learning Artificial Intelligence Software Development Data Visualization
  1. Using just $1 of hardware, you can turn a MacBook into a touchscreen with some clever computer vision. It shows how innovative ideas can come from simple solutions.
  2. There's a debate about whether we need new programming languages specifically for machine learning. Current languages are being adapted, but new ones might be better suited for future AI developments.
  3. The NIH is pushing to use data science and AI to improve healthcare initiatives. They’re looking for public input to create a strategy around data science in health and research.

Data Science Weekly - Issue 221

Data Science Weekly Newsletter • 19 implied HN points • 15 Feb 18
🕹 Technology Data science Artificial Intelligence Machine Learning Data Visualization Software Development
  1. Deep learning can be implemented in simple tools like Google Sheets, making it more accessible for everyone.
  2. Reinforcement learning in trading could be a valuable research area, similar to training AI for multiplayer games.
  3. The use of AI tools is growing rapidly, impacting fields like data visualization and criminal justice decision-making.

Data Science Weekly - Issue 199

Data Science Weekly Newsletter • 19 implied HN points • 14 Sep 17
🕹 Technology Data science Machine Learning Artificial Intelligence Automation Data Visualization
  1. Deep learning can help diagnose heart disease with fewer parameters, making it more efficient.
  2. Autonomous robots are being used to plant and harvest crops, showcasing the future of farming technology.
  3. AI is transforming music production, allowing artists to create albums without traditional tools.

Data Science Weekly - Issue 197

Data Science Weekly Newsletter • 19 implied HN points • 31 Aug 17
🕹 Technology AI Data science Machine Learning Software Development Data Visualization
  1. Amazon's AI can help you find styles that suit you by using machine learning. It can even make new styles from scratch!
  2. Being a non-traditional data scientist is possible with interest and a willingness to learn. Many paths can lead you to a successful career in data science, even from diverse backgrounds.
  3. AI and machine learning are becoming essential tools in data science, expected to drive future economic growth just like past innovations such as electricity.

Data Science Weekly - Issue 194

Data Science Weekly Newsletter • 19 implied HN points • 10 Aug 17
🕹 Technology Data science Machine Learning AI Software Development Data Visualization
  1. Computers can predict successful startups using AI, and they performed surprisingly well in identifying companies like Evernote and Spotify.
  2. Choosing the right data visualization style can help viewers understand information more easily, whether it's showing geographic variations or busy activity areas.
  3. Understanding different deep learning frameworks like PyTorch and TensorFlow is important for effective model building and analysis in data science.

Data Science Weekly - Issue 192

Data Science Weekly Newsletter • 19 implied HN points • 27 Jul 17
🕹 Technology Data science Artificial Intelligence Machine Learning Data Visualization Computer Science
  1. We need to consider the entire system when discussing data, not just the algorithms or models. This helps us understand the bigger picture and ask meaningful questions about how things work.
  2. There are many guidelines for figuring out if something causes another thing. It can be helpful to look at these through creative ways, like using comics to explain complex ideas.
  3. Robots are getting better at imitating humans, which can be a threat to democratic societies. It's important to stay aware of how these technologies can be misused.

Data Science Weekly - Issue 191

Data Science Weekly Newsletter • 19 implied HN points • 20 Jul 17
🕹 Technology Data science Machine Learning Artificial Intelligence Data Visualization Algorithm Design
  1. Understanding your data is crucial in machine learning. Using visualization tools can help you make sense of large datasets and reveal important insights.
  2. AI can unintentionally learn biases from data, leading to unfair outcomes. It's important to know how these biases can occur and take steps to avoid them.
  3. Machine learning models require careful tuning to avoid overfitting or underfitting. Balancing complexity and performance is key to building effective models.

Data Science Weekly - Issue 175

Data Science Weekly Newsletter • 19 implied HN points • 30 Mar 17
🕹 Technology Data science Artificial Intelligence Machine Learning Big Data Data Visualization
  1. Deep learning is becoming important for various parts of companies like Facebook. It's not just a special skill; it's useful everywhere from messaging to ads.
  2. Nvidia is focusing on making chips that can help improve healthcare through AI. They see medicine as a big chance to apply their technology.
  3. Data visualization is crucial for understanding information. Tools like Pandas and Seaborn help people make sense of data easily.

Axial Discovery - Clinical trial statistical analysis

Discovery by Axial • 1 implied HN point • 08 Sep 23
🔬 Science Clinical Trials Statistical Analysis Software Development Data Integration Data Visualization
  1. Clinical trial statistical analysis involves collecting and interpreting data to evaluate new treatments.
  2. Startups have opportunities to develop software for automating and streamlining statistical analysis processes due to increasing data complexity.
  3. Software development for data integration, visualization, and communication can improve efficiency in clinical trial statistical analysis.

Data Science Weekly - Issue 163

Data Science Weekly Newsletter • 19 implied HN points • 05 Jan 17
🕹 Technology Data science Machine Learning Data Visualization Automation Programming
  1. Data visualization projects can be really impressive and help understand complex information. It's interesting to see what creative ways people use to present data.
  2. AI is making its way into the pharmaceutical industry, helping to analyze data and find insights. This shows how important data scientists are becoming in various fields.
  3. Learning about machine learning, like creating algorithms from scratch, can give you a deeper understanding of technology. It's a great way to see how these tools actually work.

Data Science Weekly - Issue 158

Data Science Weekly Newsletter • 19 implied HN points • 01 Dec 16
🕹 Technology Data science Machine Learning Artificial Intelligence Programming Analytics Data Visualization
  1. Machine intelligence is making predictions cheaper, which can create big economic changes. This technology is becoming essential in many fields.
  2. Retailers can use machine learning to manage fresh food stock better, avoiding waste and shortages. This helps them save money and serve customers better.
  3. AI is starting to impact medicine, like an AI that can detect eye diseases as well as human doctors. This could change how we approach healthcare.

Data Science Weekly - Issue 150

Data Science Weekly Newsletter • 19 implied HN points • 06 Oct 16
🕹 Technology Data science Machine Learning Artificial Intelligence Data Visualization Big Data
  1. Python has great tools for data visualization, like Altair. It's worth exploring these options to make your data stories clearer.
  2. Machine learning can be likened to deep frying; it might sound exciting but requires careful consideration about what you're working with. Understanding the underlying processes is key.
  3. Data analysis is an evolving field that aims to improve decision-making through experience and tools. We should keep learning to make better conclusions from our data.

Data Science Weekly - Issue 144

Data Science Weekly Newsletter • 19 implied HN points • 25 Aug 16
🕹 Technology Data science Machine Learning Artificial Intelligence Programming Data Visualization Statistical Analysis
  1. Neural networks are inspired by how our brain's neurons work and help simulate intelligent behavior. They have a long history and have evolved significantly over time.
  2. Counting can be surprisingly difficult in data science, often requiring more effort than expected. Even experienced data scientists face challenges with counting tasks.
  3. Data-driven decision making is important, but we must be cautious. Ignoring the nuances can lead to pitfalls, so it's crucial to stay aware and informed.

Data Science Weekly - Issue 142

Data Science Weekly Newsletter • 19 implied HN points • 11 Aug 16
🕹 Technology Data science Artificial Intelligence Machine Learning Data Analysis Data Visualization
  1. Data analysis can be used to understand patterns, like analyzing tweets to see how they reflect someone's personality.
  2. Artificial intelligence is developing, but there are still limitations in how machines understand human language.
  3. Using technology like NASA imagery and machine learning can help improve agricultural predictions and trading.

Data Science Weekly - Issue 134

Data Science Weekly Newsletter • 19 implied HN points • 16 Jun 16
🕹 Technology Data science Machine Learning Artificial Intelligence Statistics Data Visualization
  1. Neural networks are becoming essential for solving many tough computer problems, like identifying faces or understanding speech.
  2. Netflix believes its recommendation system is incredibly valuable, saving the company over $1 billion a year by personalizing user suggestions.
  3. R programming has recently surpassed SAS in scholarly use, showing a shift in data analysis preferences among researchers.

Data Science Weekly - Issue 123

Data Science Weekly Newsletter • 19 implied HN points • 31 Mar 16
🕹 Technology Data science Artificial Intelligence Machine Learning Data Visualization Computer Vision
  1. Stories can help us understand the world, but not all stories are true. It's important to know when to trust our explanations and when to question them.
  2. Data science is vital for companies like Airbnb because it helps integrate analytics into leadership decisions. This shows how data can shape business strategies.
  3. Predictive data can enhance safety, like how Baidu uses map searches to forecast crowd behavior. It demonstrates how technology can help manage real-world situations.

Data Science Weekly - Issue 120

Data Science Weekly Newsletter • 19 implied HN points • 10 Mar 16
🕹 Technology Data science Machine Learning Artificial Intelligence Data Visualization Big Data
  1. Understanding what makes content go viral on platforms like Reddit can be tricky. People often know great posts when they see them, but predicting them is another story.
  2. The American Statistical Association has made a big change by saying no to p-values. This is a significant shift in the statistical community.
  3. Robots are getting better at learning through interaction, but they still have a long way to go to match human skills. Continuous learning and feedback can help them improve.

Data Science Weekly - Issue 108

Data Science Weekly Newsletter • 19 implied HN points • 17 Dec 15
🕹 Technology Data science Machine Learning Artificial Intelligence Data Visualization Programming
  1. Data science is being applied in creative ways, like analyzing rap lyrics to see what makes a hit song. It's cool to see data being used to explore music trends!
  2. Recent advances in AI are allowing machines to perform vision tasks better than humans, showing how fast technology is evolving.
  3. Understanding the differences between jobs in data science, like data scientists and machine learning engineers, can help people find the best fit for their skills.

Data Science Weekly - Issue 102

Data Science Weekly Newsletter • 19 implied HN points • 05 Nov 15
🕹 Technology Data science Machine Learning Big Data Artificial Intelligence Data Visualization
  1. There are many ghost cities in China due to overdevelopment, which became clear through data mining techniques.
  2. The debate between programming languages like Python and R can distract from more important issues in the data science community.
  3. Research using social media data, like Instagram, can uncover trends such as teenage drinking that traditional surveys might miss.

Data Science Weekly - Issue 86

Data Science Weekly Newsletter • 19 implied HN points • 16 Jul 15
🕹 Technology Data science Machine Learning Artificial Intelligence Software Development Data Visualization
  1. A simple neural network can be built in just 11 lines of Python code, showcasing how backpropagation works in machine learning.
  2. There's interesting data visualization in sports that shows how team performance changes over time, affecting how we view their success.
  3. Data science can be used for social good, and there are many ways to get involved in projects that make a positive impact on the world.

Data Science Weekly - Issue 72

Data Science Weekly Newsletter • 19 implied HN points • 09 Apr 15
🕹 Technology Data science Machine Learning Artificial Intelligence Big Data Data Visualization
  1. Creating a data-driven organization can take time and requires dedication, as seen in Warby Parker's journey.
  2. Machine learning is being used effectively in large companies like American Express to improve their services and handle big data.
  3. Visual tools and tutorials can help people learn how to analyze large data sets more easily, like using Excel.

Data Science Weekly - Issue 67

Data Science Weekly Newsletter • 19 implied HN points • 05 Mar 15
🕹 Technology Data science Machine Learning Data Visualization AI Statistics
  1. Flickr uses deep learning to automatically label images, which helps with the huge number of daily uploads. This shows how technology can improve organization and accessibility of visual data.
  2. Data visualization is becoming essential in journalism, as it helps tell stories more effectively than traditional text and images. This shift is changing the way information is communicated to the public.
  3. Machine learning is being applied in drug discovery, showing its potential to find effective treatments for various diseases. This highlights how data science can make a significant impact on health and medicine.

Data Science Weekly - Issue 47

Data Science Weekly Newsletter • 19 implied HN points • 16 Oct 14
🕹 Technology Data science Machine Learning Artificial Intelligence Big Data Data Visualization
  1. Data science can help improve services, like reducing fraud in microfinance, showing its real-world impact.
  2. Mathematical models can predict disease outbreaks, but it's challenging to get them perfectly accurate.
  3. Machine learning tools, like those in Google Sheets, are making it easier to analyze data and make predictions.

Data Science Weekly - Issue 33

Data Science Weekly Newsletter • 19 implied HN points • 10 Jul 14
🕹 Technology Data science Machine Learning Big Data Statistical Analysis Data Visualization
  1. Random forests are a powerful tool in data science that can help understand how different parts of the algorithm work and improve its use.
  2. There are two main approaches to statistics: frequentism and Bayesianism, and they can lead to different solutions for data analysis problems.
  3. Data visualization is important for making complex information easier to understand, and there are many great tools available to help with this.