The hottest Data Analysis Substack posts right now

And their main takeaways
Category
Top Technology Topics

A 5 Step Process to Improve Your Investment Acumen With Zero Risk

DeFi Education • 919 implied HN points • 07 Sep 22
💰 Finance Investment Trading Market Analysis Risk management Data Analysis
  1. To get better at investing, you need to practice regularly and learn from different situations. The more you engage with investments, the better you'll become.
  2. Gather data from various sources to form a complete understanding of an investment. Don't just look at numbers; think about what they mean and how they relate to the market.
  3. After creating your investment plan, it's key to track how your decisions pan out. Reflect on what you did right or wrong to keep improving your skills over time.

You can now run single-cell experiments in a tube with 4.5 hours of hands-on time

LatchBio • 12 implied HN points • 26 Dec 24
🕹 Technology Biotechnology Genomics Data Analysis
  1. A new single-cell sequencing technology makes experiments easier and faster, only needing about 4.5 hours of hands-on work. This means more scientists can do these experiments without needing a big budget or lots of extra equipment.
  2. The new method allows for better scalability, letting researchers run from 1 to 96 samples easily. This flexibility can lead to more data and insights in various experiments, such as drug development or studying disease.
  3. The SimpleCell technology also includes user-friendly analysis tools, making it easier for scientists to understand and visualize their results. This helps them feel more in control of their research and get valuable insights quickly.
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Reflecting on key ideas from two past Signals posts

Stealing Signals • 139 implied HN points • 10 Feb 24
🎾 Sports Fantasy Football Player evaluation Market Valuation Data Analysis
  1. Success in fantasy football is not solely reliant on the advice of experts but on the effort and engagement of the players themselves.
  2. Examining individual players within the context of their own careers is more valuable than making comparisons between players in different situations.
  3. Considering full-season data without understanding how it was accumulated and the conditions present can lead to flawed predictions for the upcoming season.

Exploring recent Python repositories, part 2

Mostly Python • 628 implied HN points • 29 Jun 23
🕹 Technology Programming Open Source Software Development Data Analysis Web Development
  1. The post explores new Python repositories that have gained just a small number of stars, filtering out the projects with no attention.
  2. Over 300,000 Python repositories are pushed to GitHub each month, showing the challenge of getting noticed among the vast amount of projects.
  3. Projects with a few stars can still be interesting and worth exploring, like a Pygame project inspired by Factorio.

Balancing the equation

Logging the World • 239 implied HN points • 11 Jun 23
🏥 Health & Wellness Data Analysis
  1. The author discusses instances where the UK Government may have under-reacted during the COVID-19 pandemic, pointing out missed opportunities for better responses based on data.
  2. There is a focus on specific timeframes like March 2020, September 2020, November to December 2020, September 2020 to July 2021, and Autumn 2021, highlighting key moments for potential improvement in managing the pandemic.
  3. The author presents a balanced view by acknowledging areas where the official response missed out in reacting appropriately, apart from the more commonly discussed issues like PPE and testing.

Machine learning eats up science

Mindful Modeler • 219 implied HN points • 18 Oct 23
🔬 Science Machine Learning Research Technology Book Writing Data Analysis
  1. Research papers increasingly focus on AI and ML, indicating a growing trend in the scientific community.
  2. AI and ML offer significant benefits in terms of saving time, automating tasks, and enabling research.
  3. Challenges like bias, fraud, and lack of reproducibility persist, with a major concern being the reliance on pattern recognition over understanding in ML and AI.

Difference-in-Differences: No Anticipation with Parallel Trends (with a simulation and equations)

Scott's Substack • 117 implied HN points • 31 Jan 24
🔬 Science Causal Inference Statistics Simulation Equations Data Analysis
  1. No anticipation means the baseline period is equal to Y(0) not Y(1)
  2. Difference-in-differences coefficient equals ATT in the post period for the treatment group plus parallel trends bias minus ATT in the incorrectly specified baseline period
  3. Difference-in-differences always requires three assumptions to point identify the ATT: SUTVA, Parallel trends, and No Anticipation

E10 - Future Trends: Gen AI and the Next Generation of Products

The Product Channel By Sid Saladi • 6 implied HN points • 16 Jan 25
🕹 Technology AI Product Management Innovation Automation Data Analysis
  1. Generative AI is reshaping industries by creating new opportunities and enhancing product development. It's not just a technology; it can change the way we work and create.
  2. Real-world examples, like DeepMind's AlphaFold, show how generative AI can lead to breakthroughs in fields like healthcare, making processes faster and more efficient.
  3. Product managers should harness generative AI to create better user experiences. By integrating this technology, they can offer more personalized and engaging products.

Online Reviews: An Untapped Resource for UX Research?

UX Psychology • 119 implied HN points • 26 Jan 24
🕹 Technology UX Research Data Analysis User Feedback Competitor Analysis Ethical considerations
  1. Online reviews offer easy access to real user feedback, going beyond predefined questions and providing insights into user profiles and product features that traditional research may miss.
  2. Large datasets from online reviews allow for analysis at a vast scale, enabling the discovery of weak signals affecting small user subsets that traditional research could overlook, especially in companies with limited research budgets.
  3. Sentiment analysis of online reviews can uncover user frustrations, needs, and pain points, helping identify where experiences fall short of expectations and providing insights into specific features and aspects of the user experience.

Data at Depth 11: Diversity, Adversity, Case Study With GPT-4 and External Validation

Data at Depth • 59 implied HN points • 18 Apr 24
🕹 Technology AI Social media Data Analysis Writing Machine Learning
  1. Documenting and analyzing your journey as a creator can help identify patterns of growth and areas for improvement, like diversification across social media platforms.
  2. Engaging in strategic thinking, research, and creation can lead to significant accomplishments, such as getting articles published and boosted, validating your skills as a writer.
  3. When using tools like GPT-4 for tasks like title generation, it's crucial to validate their output externally to ensure accuracy and effectiveness.

Intermarriage in America Post-Loving v. Virginia

Cremieux Recueil • 253 implied HN points • 02 Feb 24
🎭️ Culture Marriage Race Demographics Data Analysis Social Trends
  1. Before Loving v. Virginia in 1967, state laws banning interracial marriage were common in the U.S., stretching back to the 1600s.
  2. Since the legalization of interracial marriage, the rates have increased over time, showing a more mixed ethnoracial composition in America.
  3. Analysis of interracial marriage rates can provide insights into race relations, impact of societal movements like the 'Great Awokening,' and patterns of intermixing across different races and sexes.

Do Large Language Models have a "theory of mind"?

The Counterfactual • 219 implied HN points • 14 Sep 23
🕹 Technology AI Machine Learning Cognitive Science Human-computer interaction Data Analysis
  1. Large language models (LLMs) show some ability to understand the beliefs of other characters in scenarios, indicating a form of Theory of Mind. This means they can predict behaviors based on what a character knows or believes.
  2. However, LLMs don't perform as well as humans on these tasks, suggesting their understanding is not as deep or reliable. They score above chance but below the typical human accuracy.
  3. Research on LLMs and Theory of Mind is ongoing, raising questions about how these models process mental states compared to humans and if traditional tests for mentalizing are sufficient.

This Is The Most Important Thing We Can Build With AI

America 2.0 (by Gary Sheng) • 216 implied HN points • 05 Apr 23
🕹 Technology AI Data Analysis Decision-making
  1. A human-powered, AI-supercharged network is crucial to make collective decisions and bring about positive change.
  2. The bottleneck to effective coordination lies in the quality of input data in attempts to coordinate.
  3. An AI-powered civic information network can revolutionize our ability to understand collective desires and serve the community better.

Why Product Data Beats Company Data

The Security Industry • 18 implied HN points • 24 Nov 24
🕹 Technology Data Analysis Cybersecurity Product Management Market research Competitor Analysis
  1. Product data is more useful than company data. Knowing what products a company offers helps you find competitors better.
  2. You can categorize products accurately to see how they stack up against each other. This way, you can identify direct competition more effectively.
  3. Having detailed product information helps customers find the right solutions for their needs. You can easily search by features or requirements.

Unleashing LLMs in Cybersecurity: A Playbook for All Industries

Gradient Flow • 259 implied HN points • 20 Apr 23
🕹 Technology Cybersecurity AI Automation Data Analysis Software Development
  1. Large Language Models (LLMs) are gaining interest in various industries, especially in cybersecurity, and can be used as a playbook for implementation in other domains.
  2. Custom LLMs can be created for cybersecurity applications, leading to potential advancements like specialized chatbots and content generation for enhanced security measures.
  3. LLMs are transforming automation processes in cybersecurity, offering improved accuracy and convenience, and displaying potential for impact across multiple industries through domain-specific adaptations.

People Should Not Prioritize What They Think Are "Vibes" Over Actual Data: The Economic Situation Really Is—Relative to All Other Years since 2000—Excellent

Brad DeLong's Grasping Reality • 215 implied HN points • 10 Feb 24
🇺🇸 U.S. Politics Economy Media Polling Partisanship Data Analysis
  1. Don't prioritize 'vibes' over actual data - the economy is actually excellent compared to past years.
  2. Partisanship influences perceptions of the economy - Democrats more optimistic than Republicans.
  3. Journalists sometimes emphasize negative news, even when data shows a positive economic situation.

Data At Depth 13: Substack Love Affair, On Reading, Python Plotly Bubbles and Bars

Data at Depth • 39 implied HN points • 16 May 24
🕹 Technology Data Analysis Python Data Visualization Web Development
  1. The author shares insights on their data analysis for the past 2 weeks, highlighting significant growth on Substack, experiences on Medium and LinkedIn, and struggles with Twitter-X.
  2. The author emphasizes the importance of taking time to read and detach from the pressure of creating content, as well as the value of ownership and direct engagement through Substack newsletters.
  3. A tutorial is provided on creating interactive Python Plotly dashboards for data visualizations, specifically focusing on a bubble map and bar chart to showcase data on global undernourishment.

E3 - 🛠️ Prompt Engineering Basics and 50+ Free 🎁 Product Management Prompts 🚀

The Product Channel By Sid Saladi • 20 implied HN points • 24 Nov 24
🕹 Technology AI Tools Product Management Software Development Prompt engineering Data Analysis
  1. Prompt engineering is about crafting the right questions to get useful responses from AI. Think of it like asking the AI to help you with specific tasks in a clear way.
  2. This skill can help product managers speed up their work by automating tasks and generating creative ideas. It's a powerful tool for making better decisions based on data.
  3. Understanding how to structure prompts effectively can lead to more relevant and accurate results. It involves giving clear instructions, context, and examples to guide the AI.

Behind the scenes with '24 hours in an invisible epidemic'

Big Charts • 199 implied HN points • 29 Sep 23
🔬 Science Research Data Analysis Social Interaction Loneliness Personal narratives
  1. The story discusses the correlation between day-to-day activities and happiness, highlighting how social interaction plays a significant role in people's well-being and happiness levels.
  2. Data visualization can sometimes present challenges in clearly conveying findings, emphasizing the importance of ensuring that the visualization aligns with the story being told.
  3. Visualizing individual diaries can make the concept of loneliness feel universal, prompting important conversations about struggles with loneliness in everyday life.

Part 1: AI Use Among UX Professionals

UX Psychology • 198 implied HN points • 17 Aug 23
🕹 Technology AI UX Tools Data Analysis Ethics
  1. Artificial Intelligence is significantly impacting User Experience (UX) by providing new tools and methods for research and design.
  2. UX professionals have varying levels of AI knowledge and usage, with concerns including potential errors, biases, and job security.
  3. Even though many UX professionals are incorporating AI into their work, there is still caution and a desire to ensure responsible AI use and human augmentation.

Anomalous Radioactive Variations

Remote View • 196 implied HN points • 29 Mar 23
🔬 Science Research Experimentation Data Analysis Astrophysics
  1. Joe Parr conducted experiments with pyramids and radioactive sources, noting cyclical variations in radioactive counts possibly linked to moon phases and solar activity.
  2. Parr's hypothesis of a 'hyperspace bubble' forming around the pyramid passing through magnetic fields is based on anomalous events in the data.
  3. The test setups involved rotating pyramids between magnetic fields, with a sophisticated setup to measure radioactive counts and variations.