The hottest Data Analysis Substack posts right now

And their main takeaways
Category
Top Technology Topics

Microsoft Sentinel SOC 101: How to Detect and Mitigate SQL Injection Attacks with Microsoft Sentinel

Rod’s Blog • 119 implied HN points • 27 Sep 23
🕹 Technology Data Analysis
  1. SQL injection attacks exploit vulnerabilities in web applications to access sensitive data.
  2. Microsoft Sentinel uses advanced analytics rules and integrates with Defender for SQL to detect and respond to SQL injection attacks effectively.
  3. Organizations can benefit from automated incident response, threat hunting, and incident investigation capabilities in Microsoft Sentinel to mitigate the impact of SQL injection attacks.

In Defense of Human Senses

Cybernetic Forests • 119 implied HN points • 30 Apr 23
🕹 Technology Data Analysis
  1. Human perception of images is deeply intertwined with personal experiences and emotions, shaping how images are interpreted and associated with memories.
  2. Creating art involves a fusion of individual lived experiences and learned skills over time, contrasting with the quick generation of images by AI devoid of personal experiences.
  3. AI images are structured based on categories and datasets, emphasizing the need for artists to negotiate these categories and infuse individualized interpretations into the process.

Microsoft Sentinel SOC 101: How to Detect and Mitigate Inactive Account Sign-ins with Microsoft Sentinel

Rod’s Blog • 59 implied HN points • 05 Feb 24
🕹 Technology Data Analysis
  1. Microsoft Sentinel helps in detecting and mitigating inactive account sign-ins by collecting and analyzing sign-in logs from Microsoft Entra ID using the Kusto Query Language.
  2. To mitigate inactive account sign-ins, actions include investigating the source, blocking or disabling the account, resetting credentials, and educating users on security best practices.
  3. Best practices for managing inactive accounts in Microsoft Entra ID include defining a policy for account lifecycle, implementing provisioning and deprovisioning processes, monitoring account activity, and educating users.

Tanks for the memories

Logging the World • 179 implied HN points • 11 Dec 22
🔬 Science Data Analysis
  1. In a raffle with a large number of tickets, the biggest number drawn out starts to show some structure as more tickets are selected.
  2. By looking at the maximum value drawn in a raffle, one can estimate the total number of tickets, a concept applied in statistics like the German tank problem.
  3. Sequential numbering schemes can reveal interesting insights, as seen in situations like the Skripal poisonings and Novak Djokovic's COVID test, highlighting the importance of careful numbering practices.

Creating Individualised Chess Engines

Chess Engine Lab • 39 implied HN points • 26 Mar 24
🕹 Technology Data Analysis
  1. An engine called Maia focused on predicting human moves accurately instead of just being the strongest in chess, resulting in a more meaningful impact, especially for club-level players.
  2. By individualizing chess engines to predict moves of specific players, accuracy can be increased by 4-5% and players can be identified with 98% accuracy from a pool of 400, based on their game patterns.
  3. Identifying players through their mistakes is a crucial aspect - as mistakes are unique to individual players, understanding and fixing them can greatly aid in chess improvement.
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Mass Value Report for March

Planetocracy • 117 implied HN points • 31 Mar 23
🔬 Science Data Analysis
  1. The analysis focuses on SpaceX Falcon 9 launches and its mass-to-orbit capability
  2. SpaceX is increasing its flight rate through reuse and adding more boosters to its fleet
  3. Future analysis will include data on Falcon 9, mass estimates for beyond Earth orbits, and the transition to Starship for maintaining pace

Why Beats What: Prioritize Qualitative User Research Over Quantitative

Jakob Nielsen on UX • 54 implied HN points • 31 Jul 25
🕹 Technology Data Analysis
  1. Focus on qualitative user research to understand why users succeed or fail with a design. This approach offers deeper insights than just looking at numbers.
  2. Testing with just a few users can uncover most usability problems. Don’t wait for large studies; small, quick tests can lead to improvements faster.
  3. Always watch what users actually do instead of relying on what they say. People often don't know why they struggle, so observing their behavior gives you better clues for design changes.

AI SOC Automation isn't the right problem to solve

Frankly Speaking • 152 implied HN points • 14 Jan 25
🕹 Technology Data Analysis
  1. Focusing on better detection engineering is key in security operations. It helps identify threats more effectively rather than just automating processes.
  2. Many traditional security operations centers (SOCs) may not be necessary for most companies. Smaller, more efficient models or managed detection services can be better alternatives.
  3. The future of SOCs is likely to involve fewer human analysts and more automation, emphasizing custom detections that fit the specific needs of a business.

Novaxia vs Bigpharmia

Logging the World • 199 implied HN points • 04 Nov 22
🔬 Science Data Analysis
  1. Understand the impact of vaccines on disease spread: Novaxia and Bigpharmia are examples of two scenarios showing how vaccines can affect the spread of a disease differently.
  2. Graphs help visualize data trends: Using different types of graphs can show how disease spread changes over time and the effectiveness of interventions like vaccines.
  3. Consider the importance of logarithmic scales: Logarithmic scales can provide a different perspective on data trends, allowing for better understanding of the impact of interventions like vaccines.

5 learnings from making UX less annoying for 20M+ users

CommandBlogue • 19 implied HN points • 28 May 24
🕹 Technology Data Analysis
  1. Users don't easily forget bad experiences, like annoying pop-ups. Once trust is lost, it's hard to regain, so it's important to be careful with how you present information to them.
  2. Beautiful design attracts users and keeps them engaged. Nowadays, a nice look matters just as much as solving a problem, since many products are similar.
  3. Users prefer having multiple options. If they feel like they don't need help at first, they might still end up needing it later, so providing a way for them to revisit guides is key.

Measuring Drug Target Success

Rough Diamonds • 9 implied HN points • 16 Dec 25
🔬 Science Data Analysis
  1. Most modern drugs are built around a specific molecular target, and researchers pick targets using genetic, animal, or in‑vitro evidence that suggests the target is causally involved in disease.
  2. Targets backed by human genetic evidence more than double a drug's chance of clinical success, while pursuing mechanisms similar to past failures increases the odds of failing.
  3. Preclinical signals can improve early selection but don't replace human trials, so improving the ROI of drug development means making trials cheaper and/or picking better candidates early, rather than relying only on rational design.

Concise Chain-of-Thought (CCoT) Prompting

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 59 implied HN points • 24 Jan 24
🕹 Technology Data Analysis
  1. Concise Chain-of-Thought (CCoT) prompting helps make AI responses shorter and faster. This means you save on costs and get quicker answers.
  2. Using CCoT, the response length can be reduced by almost 50%, but it can lead to lower performance in math problems. So, it’s a trade-off between speed and accuracy.
  3. For cost-saving in AI, focusing on reducing the number of output tokens is key since they are generally more expensive. CCoT is one way to achieve this without sacrificing performance too much.

2023 Wrapped: a year of sickness and health

art fish intelligence • 58 implied HN points • 21 Jan 24
🏥 Health & Wellness Data Analysis
  1. In 2023, the author analyzed their patterns of sickness and health through data collected from sources like Google Maps location history and Apple Health.
  2. The analysis revealed insights such as spending almost half of the year unwell and correlations between health factors like exercise and location.
  3. Key findings included the impact of menstrual cycle on sickness, the importance of rest during certain phases, and the value of personal data exploration for health insights.

Exploring recent Python repositories, part 2

Mostly Python • 628 implied HN points • 29 Jun 23
🕹 Technology Data Analysis
  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.

How to Get UEBA Costs for Microsoft Sentinel

Rod’s Blog • 99 implied HN points • 09 Oct 23
🕹 Technology Data Analysis
  1. UEBA costs for Microsoft Sentinel are based on the amount of data analyzed and can vary based on factors like the tables used.
  2. A KQL query can help estimate and break down the costs for UEBA in Microsoft Sentinel.
  3. By utilizing the provided KQL query, you can calculate and observe the estimated costs for the UEBA solution within Microsoft Sentinel.

Microsoft Sentinel SOC 101: How to Detect and Mitigate Phishing Attacks with Microsoft Sentinel

Rod’s Blog • 99 implied HN points • 19 Sep 23
🕹 Technology Data Analysis
  1. Phishing attacks are a significant threat that targets human vulnerabilities and can lead to identity theft or financial fraud.
  2. Organizations can mitigate phishing attacks by adopting a 'defense in depth' strategy that includes user education, email filtering, and incident response planning.
  3. Utilizing Microsoft Sentinel, Kusto Query Language (KQL), and integrating with Microsoft 365 Threat Protection can enhance proactive threat hunting and response capabilities against phishing attacks.

Getting GEO Information for IP Addresses without Using a Microsoft Sentinel Playbook

Rod’s Blog • 99 implied HN points • 06 Jun 23
🕹 Technology Data Analysis
  1. A Kusto function called geo_info_from_ip_address() enables retrieving geolocation details for IP addresses without relying on third-party APIs.
  2. This function can gather Country, State, City, Latitude, and Longitude info for both IPv4 and IPv6 addresses.
  3. While IP-API.com offers additional details like IP management entity and mobile device indication, they may not always be necessary.

Group Assignment in a Frontend vs. Backend A/B Test

Sarah's Newsletter • 99 implied HN points • 19 Sep 23
🕹 Technology Data Analysis
  1. Decide which product feature should be behind a test, read the results of an A/B test, prioritize features based on data
  2. Understand that frontend tests focus on user experience and user groups in the browser, while backend tests require business logic and user assignment in the database
  3. Choose frontend user group assignment for speed and simplicity via firing analytics events; go for backend assignment for more complete data by storing user assignment in a database model

Understanding Variation in PTEN

Holodoxa • 99 implied HN points • 07 Sep 23
🔬 Science Data Analysis
  1. Understanding genomic data variation and its effect is a significant challenge in genetic research.
  2. Deep Mutational Scanning (DMS) and Multiplex Assays of Variant Effects (MAVEs) are crucial methods to study how mutations impact protein function.
  3. MAVE data on PTEN has provided insights into its function, stability, and clinical implications, aiding in the understanding of PTEN variation.

Joe's Nerdy Rants #3

Joe Reis • 98 implied HN points • 03 Jun 23
🕹 Technology Data Analysis
  1. In many companies, there is a divide between software engineering and data teams.
  2. Data is becoming more integrated into applications, blurring the lines between data and software.
  3. The divide between software and data teams will eventually disappear as data becomes more critical to businesses.

Practical AI is a Big Tent: Five Ideas for CEOs, Leaders, and Investors

Mike Talks AI • 98 implied HN points • 27 Aug 23
🕹 Technology Data Analysis
  1. Practical AI encompasses various machine learning algorithms and techniques, including optimization and Operations Research.
  2. The concept of Practical AI allows for the inclusion of both established and emerging approaches in the field.
  3. To effectively solve real-world problems, AI leaders need a diverse set of skills and expertise, and must understand the strengths and weaknesses of different algorithms.

The Counterfactual's science poll #1

The Counterfactual • 59 implied HN points • 03 Jan 24
🔬 Science Data Analysis
  1. Subscribers can vote on which research topics to explore each month. This makes it a fun way for people to get involved in science.
  2. Most research will focus on concrete questions and often involve Large Language Models. The goal is to keep projects manageable and achievable in a month.
  3. Some topics will involve summarizing existing research. This helps everyone understand what we know about a subject more clearly.

The GenAI D&R Revolution Begins

Detection at Scale • 19 implied HN points • 13 May 24
🕹 Technology Data Analysis
  1. Security companies at RSA are increasingly focusing on AI to enhance Detection and Response (D&R) processes.
  2. Automated Tier 1 Triage using autonomous SOC analysts can streamline alert triage and analysis, improving efficiency for SecOps teams.
  3. GenAI can also improve D&R through AI-powered chatbots for automating organizational Q&A and log summarization for quicker insights and analysis.

Triangulation in UX Research

UX Psychology • 238 implied HN points • 14 Jun 22
🕹 Technology Data Analysis
  1. Triangulation in UX research involves using multiple research methods or data sources to study the same phenomenon, enhancing credibility and providing more robust insights.
  2. There are 4 main types of triangulation recognized in research: data triangulation, investigator triangulation, theory triangulation, and methodological triangulation.
  3. Using triangulation in user research can lead to more confidence in data, reveal unexpected findings, and help to understand a problem more clearly, although it may also increase chances of confirmation bias.

5 Cybersecurity Predictions for 2024

Frankly Speaking • 305 implied HN points • 29 Feb 24
🕹 Technology Data Analysis
  1. Security companies are shifting focus to platforms, leading to acquisitions and consolidations to improve operational efficiency.
  2. Cybersecurity is moving towards more building and software engineering, away from solely relying on buying tools to solve problems.
  3. The adoption of reasonable metrics is becoming crucial for cybersecurity, allowing for better justification of funding and overall security enhancement.

Measuring Lines/Function

Software Design: Tidy First? • 132 implied HN points • 05 Dec 24
🕹 Technology Data Analysis
  1. Measuring lines of code in functions can be more complicated than expected. It's helpful to keep track of this while working on software projects.
  2. Looking for patterns in software, like Pareto distributions, can provide valuable insights. It's good practice to analyze your own code for these patterns.
  3. Documenting your findings is important. Sharing your experiences can help others who are trying to understand their software better.

Charts & More Charts

Spilled Coffee • 44 implied HN points • 16 Jul 25
💰 Finance Data Analysis
  1. There are many intriguing charts out there that can help us understand market movements. Sharing these visuals can provide valuable insights to readers.
  2. A significant amount of money is currently in Money Market Funds, but it's actually a small fraction compared to total assets. It's important to see these numbers in the right context.
  3. Mortgage applications are picking up, suggesting a possible shift in the housing market. If mortgage rates drop, there could be a surge in home buying soon.

Choosing the right UX metrics - Part 2

UX Psychology • 158 implied HN points • 03 Oct 22
🕹 Technology Data Analysis
  1. Identifying clear goals is crucial in choosing the right UX metrics, involving team and stakeholders can help define meaningful and actionable metrics.
  2. Mapping goals to signals helps track progress towards goals; gathering user feedback and reviews can be essential signals to measure UX success.
  3. Refining signals into specific metrics is the final step, where data scientists can assist in ensuring metrics are measured accurately; focus on key metrics and avoid adding unnecessary data.

Project Tailwind

Adjacent Possible • 538 implied HN points • 11 May 23
🕹 Technology Data Analysis
  1. Project Tailwind is an experimental 'tool for thought' being developed with Google
  2. Project Tailwind uses a 'source-grounded AI' approach to assist with research and information exploration
  3. Features of Project Tailwind include creating on-the-fly glossaries and suggesting additional product features based on uploaded materials