The hottest Data Visualization Substack posts right now

And their main takeaways
Category
Top Technology Topics

Job listing: Assistant Sports Analyst

Silver Bulletin • 30 implied HN points • 26 Feb 25
🎾 Sports Sports Analysis Job Listings Data Visualization Statistical Modeling Data Management
  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.

Worldbuilding with data

Data People Etc. • 231 implied HN points • 11 Feb 25
🕹 Technology Data Engineering Data science Big Data Data Visualization Analytics
  1. Data is more powerful when it has a purpose. It should tell a clear story, otherwise it's just clutter.
  2. Building a strong data system is like creating a world. A good structure connects different pieces and helps everyone understand the bigger picture.
  3. Data engineering is important because it helps manage and present large amounts of information, making sure everything works smoothly and accurately.

Data Science Weekly - Issue 564

Data Science Weekly Newsletter • 119 implied HN points • 12 Sep 24
🕹 Technology Data science Machine Learning Artificial Intelligence Data Visualization Engineering
  1. Understanding AI interpretability is important for building resilient systems. We need to focus on why interpretability matters and how it relates to AI's resilience.
  2. Testing machine learning systems can be challenging, but starting with basic best practices like CI pipelines and E2E testing can help. This ensures the models work well in real-world scenarios.
  3. Visualizing machine learning models is crucial for better understanding and analysis. Tools like Mycelium can help create clear visual representations of complex data structures.

Data Science Weekly - Issue 563

Data Science Weekly Newsletter • 139 implied HN points • 05 Sep 24
🕹 Technology Data science Artificial Intelligence Machine Learning Data Engineering Data Visualization
  1. AI prompt engineering is becoming more important, and experts share helpful tips on how to improve your skill in this area.
  2. Researchers in AI should focus on making an impact through their work by creating open-source resources and better benchmarks.
  3. Data quality is a common concern in many organizations, yet many leaders struggle to prioritize it properly and invest in solutions.

Mentions of Political Extremism in English Wikipedia

Rozado’s Visual Analytics • 350 implied HN points • 13 Dec 24
🇺🇸 U.S. Politics Political Analysis Media Studies Data Visualization Extremism
  1. English Wikipedia mentions far-right political extremism three times more than far-left extremism. This shows a noticeable difference in how each side is portrayed.
  2. The terms used to describe political extremism vary, with 'extreme' often linked more to the right and 'radical' to the left. However, the overall trend still favors right-wing mentions.
  3. These patterns in Wikipedia echo trends found in news media, suggesting that the way political extremism is discussed might be influenced by broader social and historical factors.
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Data Science Weekly - Issue 558

Data Science Weekly Newsletter • 219 implied HN points • 01 Aug 24
🕹 Technology Data science Machine Learning AI Data Visualization Statistical Methods
  1. Data science and AI are rapidly evolving fields with plenty of interesting developments. Staying updated with the latest articles and news can really help you understand these changes better.
  2. Effective communication is key in data science. Using intuitive methods and visuals can make complex concepts easier to grasp for everyone.
  3. Using tools and methods like quantization can help make large models more accessible. It's important to find efficient ways to work with vast amounts of data to improve performance.

Day 26. Visual Storytelling: INFOGRAPHICS

DrawTogether with WendyMac • 1827 implied HN points • 26 Jan 24
🎨 Art & Illustration Visual storytelling Infographics Data Visualization
  1. The regular GUT operates weekly instead of daily, offering lessons, drawing prompts, and artist visits.
  2. Hand-drawn infographics can make overwhelming information more manageable and human.
  3. Creating infographics about chosen families involves visualizing webs of support and care rather than traditional family tree structures.

Data Science Weekly - Issue 557

Data Science Weekly Newsletter • 159 implied HN points • 25 Jul 24
🕹 Technology Data science AI Machine Learning Data Visualization Engineering
  1. AI models can break down when trained on data that is generated by other models. This can cause problems in how well they work.
  2. There is scientific research about the history of Italian filled pasta. It shows that most types likely came from a single area in northern Italy.
  3. There are new resources and guides available for improving predictive modeling with tabular data. These can help you build better models by focusing on how data is represented.

Data Science Weekly - Issue 530

Data Science Weekly Newsletter • 1418 implied HN points • 19 Jan 24
🕹 Technology Data science Machine Learning Artificial Intelligence Data Visualization Software Development
  1. Good data visualization is important. Some types of graphs can be misleading, and it's better to avoid them.
  2. In healthcare, it's not just about having advanced technology like AI. The real focus should be on getting effective results from these technologies.
  3. Netflix released a lot of data about what people watched in 2023. Analyzing this can help us understand trends in streaming better.

Issue #10 - The Data Lifecycle

The Data Ecosystem • 159 implied HN points • 16 Jun 24
🕹 Technology Data science Data Management Data security Data Analysis Data Engineering Data Visualization
  1. The data lifecycle includes all the steps from when data is created until it is no longer needed. This helps organizations understand how to manage and use their data effectively.
  2. Different people and companies might describe the data lifecycle in slightly different ways, which can be confusing. It's important to have a clear understanding of what each term means in context.
  3. Properly managing data involves stages like storage, analysis, and even disposal or archiving. This ensures data remains useful and complies with regulations.

Data Science Weekly - Issue 555

Data Science Weekly Newsletter • 99 implied HN points • 11 Jul 24
🕹 Technology Data science AI Machine Learning Data Engineering Data Visualization
  1. Large language models can sometimes create false or confusing information, a problem known as hallucination. Understanding the cause of these mistakes can help improve their accuracy.
  2. Good data visualizations are important to effectively communicate patterns and insights. Poorly designed visuals can lead to misunderstandings, especially among those not familiar with graphics.
  3. There's an ongoing debate about copyright in the context of generative AI. Many believe it would be better to focus on finding compromises rather than pursuing strict legal battles.

Data Science Weekly - Issue 552

Data Science Weekly Newsletter • 139 implied HN points • 20 Jun 24
🕹 Technology Data science Artificial Intelligence Machine Learning Data Engineering Data Visualization
  1. Notebooks can be easy to use, but they might make you lazy in coding. It's important to follow good practices even when using them.
  2. When handling large datasets, it's crucial to learn how to scale effectively. Knowing how to use resources wisely can help you reach your goals faster.
  3. Retrieval Augmented Generation (RAG) can improve how models generate information. It's complex, but understanding it can boost the performance of your projects.

Data Science Weekly - Issue 553

Data Science Weekly Newsletter • 99 implied HN points • 27 Jun 24
🕹 Technology Data science Artificial Intelligence Machine Learning Data Engineering Data Visualization
  1. Data visualization can show important patterns, like changes in night and daylight globally. Understanding these trends helps us appreciate our environment better.
  2. In AI engineering, simplifying data preparation is crucial. Many new AI applications can be built without structured data, which might lead to rushed expectations about their effectiveness.
  3. Aquaculture technology is evolving with better methods to track and analyze fish behavior. New approaches like deep learning are making monitoring more accurate and efficient.

Data Science Weekly - Issue 547

Data Science Weekly Newsletter • 179 implied HN points • 17 May 24
🕹 Technology Data science AI Machine Learning Data Visualization Software Development
  1. Learning Rust programming can be made easy with exercises designed for beginners, even if you know another language already. You’ll work through small tasks to build confidence.
  2. Data scientists need to learn how to work with databases to scale their analytics. Many face challenges when transitioning to this part of their work.
  3. There are helpful tools, like Data Wrangler for VS Code, that simplify data cleaning and analysis. These tools help generate code automatically as you work with your data.

UI Components of Microsoft Security Copilot

Rod’s Blog • 515 implied HN points • 09 Jan 24
🕹 Technology Plugins Data Visualization
  1. Home Menu allows you to navigate the Security Copilot portal effectively by providing options like Home, My sessions, Settings, and Tenant.
  2. Manage Plugins feature lets you control and access Microsoft security services through Security Copilot to perform various actions such as managing threats and incidents.
  3. Prompt Bar is where you can interact with Security Copilot by asking questions, running commands, or requesting reports using natural language inputs.

Data Science Weekly - Issue 548

Data Science Weekly Newsletter • 139 implied HN points • 24 May 24
🕹 Technology Data science Machine Learning Artificial Intelligence Data Visualization Data Engineering
  1. Good communication is key for statisticians to explain their complex work to non-experts. Finding ways to relate data to everyday situations can make it easier for others to understand.
  2. Using histograms can speed up the training process for gradient boosted machines in data science. This simple technique can improve efficiency significantly.
  3. There are efforts to use machine learning algorithms to detect type 1 diabetes in children earlier. This can help avoid serious health issues by improving recognition of symptoms.

Data Science Weekly - Issue 539

Data Science Weekly Newsletter • 259 implied HN points • 22 Mar 24
🕹 Technology Data science AI Machine Learning Data Engineering Data Visualization
  1. Data storytelling is important for sharing insights, and AI can help people create better stories. The research looks at how different tools assist in each storytelling stage.
  2. Switching from R to Python in data science isn't just about learning new syntax; it's a mindset change. New Python tools can help make this transition smoother for users coming from R's tidyverse.
  3. Emerging technologies often face skepticism, as seen throughout history. New inventions have raised concerns about their impact, but they eventually become part of everyday life.

How visualization reveals implicit prejudices

Alberto Cairo's The Art of Insight • 219 implied HN points • 08 Apr 24
🎭️ Culture Data Visualization Media representation Gender Issues Social norms
  1. Data visualization can show our hidden biases. Seeing how we react to certain graphs might make us realize our feelings about different groups.
  2. Negative reactions to visual data about trans and nonbinary people may reflect societal prejudices. People should think about why they feel the way they do when looking at such charts.
  3. Many mainstream media outlets report on gender issues in a biased way. Understanding our biases can lead to better reporting and broader acceptance of gender diversity.

Data Science Weekly - Issue 532

Data Science Weekly Newsletter • 379 implied HN points • 02 Feb 24
🕹 Technology Data science Machine Learning Artificial Intelligence Data Engineering Data Visualization
  1. Forecasting in data science is challenging because time series data can be non-stationary. Using the right evaluation methods can help bridge the gap between traditional and modern forecasting techniques.
  2. It's important to consider the smartness of your data structures. Creating overly complicated dashboards that ultimately just produce simple outputs may not be the best use of time.
  3. There are clear distinctions between well-built data pipelines and amateur setups. Understanding what makes a pipeline production-grade can improve the quality and reliability of data processing.

Heat maps on Germany: renewables and prices

GEM Energy Analytics • 239 implied HN points • 15 Mar 24
🌞 Climate & Environment Renewables Energy Data Visualization Sustainability
  1. Germany's renewable energy sources like solar and wind are working well together, especially during the winter. This means Germany can rely on both types of energy to help meet their needs.
  2. Heat maps show that solar energy has a big impact on electricity prices, especially during sunny afternoons in the spring and summer. When there's lots of solar power, prices can drop.
  3. Comparing Germany with France on energy prices, we see Germany benefits from more solar energy during the day. This can lead to lower prices in Germany, especially in the afternoon.

Data Science Weekly - Issue 544

Data Science Weekly Newsletter • 159 implied HN points • 26 Apr 24
🕹 Technology Data science Machine Learning Artificial Intelligence Data Engineering Data Visualization
  1. Evaluating AI models can be expensive, but tools like lm-buddy and Prometheus help do it on cheaper hardware without high costs.
  2. Installing and deploying LLaMA 3 is made simple with clear guides that cover everything from setup to scaling effectively.
  3. Understanding best practices in machine learning is essential, and resources like the 'Rules of Machine Learning' provide valuable guidelines for beginners.

Maturity and enthusiasm

Alberto Cairo's The Art of Insight • 239 implied HN points • 08 Mar 24
🪑 Design Information Design Data Visualization Teaching Methods Creative Process Professional development
  1. Maturity in a profession can bring new insights and clarity. It's a journey that includes both personal growth and the evolution of skills and knowledge.
  2. Learning how to design information helps us communicate and think better. It's a valuable skill that can benefit anyone, not just designers.
  3. This newsletter will share personal experiences, analysis, and recommendations about data visualization. It's an exploration of the craft and the joy of learning together.

Nathan Yau's friendly voice

Alberto Cairo's The Art of Insight • 99 implied HN points • 29 May 24
🎨 Art & Illustration Data Visualization Infographics Statistics Graphic design Book Reviews
  1. Nathan Yau is known for making data visualization fun and approachable, both in his blog and his book, 'Visualize This'.
  2. The second edition of 'Visualize This' offers updated examples and tools, making it more cohesive than the first edition.
  3. Reading Yau's work feels like getting hands-on help from an experienced designer, which makes learning enjoyable.

Data Science Weekly - Issue 546

Data Science Weekly Newsletter • 119 implied HN points • 10 May 24
🕹 Technology Data science AI Machine Learning Data Engineering Data Visualization
  1. Time-series analysis and Gaussian processes are powerful tools for interpreting data. They allow for flexibility and control in modeling data, making them essential for data practitioners.
  2. Understanding A/B testing is crucial for making informed business decisions. Using a reliable experimentation system can save time and lead to better results.
  3. New advancements in AI and data science are enhancing applications in various fields, like biomedical research and recommendation systems. These innovations help combine human creativity with machine learning capabilities.

Data Science Weekly - Issue 531

Data Science Weekly Newsletter • 219 implied HN points • 26 Jan 24
🕹 Technology Data science Artificial Intelligence Machine Learning Data Engineering Data Visualization
  1. AI often gets criticized for the quality of its output, but that might not be the real issue people have with it. If quality is fixed, the conversation about AI could change significantly.
  2. Common sense is tricky to define and measure, but researchers are developing ways to quantify it both individually and collectively. This could help clarify how we understand common sense in different contexts.
  3. Large language models (LLMs) can transform education by encouraging hands-on learning. They offer opportunities for more interactive and engaging learning experiences.

Data Science Weekly - Issue 537

Data Science Weekly Newsletter • 139 implied HN points • 07 Mar 24
🕹 Technology Data science AI Machine Learning Data Engineering Data Visualization
  1. The newsletter shares valuable links about Data Science, AI, and Machine Learning each week. It's a great way to keep updated on the latest in the field.
  2. There are interesting articles highlighting statistical analyses and practical guides, like building GPU clusters at home. These resources help both beginners and experienced practitioners learn more.
  3. The newsletter also encourages people to participate in AI-related events and offers resources for job seekers. This can help you connect with others and grow your career.

Data Science Weekly - Issue 517

Data Science Weekly Newsletter • 339 implied HN points • 19 Oct 23
🕹 Technology Data science AI Machine Learning Data Visualization Engineering
  1. Data science, AI, and ML are rapidly evolving fields, with new technologies and techniques emerging frequently. Staying updated through news and articles can help professionals keep their skills relevant.
  2. Fine-tuning large language models (LLMs) is a growing demand in the job market. Many companies are now looking for experience with LLMs alongside traditional skills like Python and SQL.
  3. Understanding different data visualization goals, like storytelling versus exploration, is important for effectively communicating data insights. This can improve how data is presented in reports and analyses.

Data Science Weekly - Issue 509

Data Science Weekly Newsletter • 399 implied HN points • 25 Aug 23
🕹 Technology Data science Machine Learning Artificial Intelligence Data Engineering Data Visualization Software Development
  1. Each week, a newsletter shares important links and articles about data science, machine learning, and AI. It's a good way to keep updated on new happenings in the field.
  2. The newsletter features articles on various topics, including programming, AI forecasting, and data management practices. These articles are meant to help both newcomers and experienced professionals.
  3. Job listings and training resources are also provided, helping readers find opportunities and learn new skills beneficial for their careers in data science.

Simple Interactive Python Streamlit Maps That Will Make You Shout

Data at Depth • 79 implied HN points • 03 May 24
🕹 Technology Data Visualization Python Data storytelling
  1. Python Streamlit is great for creating interactive maps from GIS point data, allowing for more engaging data storytelling.
  2. Interactive maps offer a better way to present data compared to static maps, enabling users to interact and explore the information further.
  3. Streamlit is a useful tool for creating interactive maps with user input functionalities, making it ideal for data visualization projects.

Data Science Weekly - Issue 514

Data Science Weekly Newsletter • 339 implied HN points • 29 Sep 23
🕹 Technology Data science Machine Learning Artificial Intelligence Data Visualization Data Engineering
  1. Data science involves a mix of techniques for analyzing and visualizing data which can help make informed decisions.
  2. Learning about advanced customer segmentation methods can enhance how businesses understand and target their customers.
  3. There are various roles in data-related careers beyond just being a data scientist, so it's good to explore different paths.

Data Science Weekly - Issue 519

Data Science Weekly Newsletter • 299 implied HN points • 03 Nov 23
🕹 Technology Data science AI Machine Learning Data Engineering Tech news Data Visualization
  1. Companies are increasingly sharing their advanced AI models openly, which can help them improve and build better products. This open sharing can lead to a more cooperative tech environment.
  2. Data science job applications are extremely competitive, with many positions receiving thousands of applicants within a day. This shows a high interest and demand in the data science field.
  3. Exploring advanced tools and frameworks in AI can be complex, but understanding how they work can help in building effective applications, especially in question-answering systems.