The hottest Analytics Substack posts right now

And their main takeaways
Category
Top Technology Topics

It's time to build

benn.substack • 1278 implied HN points • 19 Jan 24
🕹 Technology Data AI Software Startups Analytics
  1. The modern data stack ecosystem is shifting as interest in generative AI takes over.
  2. The hype surrounding data tools can lead to rapid product development but also instability and distraction.
  3. Startups can find success by focusing on rebuilding existing ideas in a more deliberate and stable manner.

Data Science Weekly - Issue 526

Data Science Weekly Newsletter • 419 implied HN points • 22 Dec 23
🕹 Technology Data science AI Machine Learning Analytics Software Development
  1. Generative AI is changing how we work with tools, improving the Human-Tool Interface. This can help us use technology in ways we never could before.
  2. Support Vector Machines (SVMs) can be very effective for prediction tasks, often outperforming other models in error rates. However, they aren’t as commonly used, possibly due to their complexity.
  3. Deep multimodal fusion is useful in surgical training. It helps classify feedback from experienced surgeons to trainees by combining different types of data like text, audio, and video.

Championship Sunday was a strange watch for Vikings fans

Purple Insider • 294 implied HN points • 29 Jan 24
🎾 Sports Football Draft Analytics Quarterbacks Strategy
  1. Sunday's games were strange for Vikings fans to watch from a unique perspective.
  2. Building a championship team can involve having an all-time great quarterback, hitting on many draft picks, or building a strong supporting cast around an affordable quarterback.
  3. Success in the NFL requires making bold decisions and it's challenging to win even with a great team.
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Data: the new oil or a new byproduct?

Topsoil • 511 implied HN points • 30 Jun 23
🕹 Technology Data Agriculture Framework Digitization Analytics
  1. Data in agriculture is essential for advancements like Generative AI, automation, and precision agriculture.
  2. Challenges in farm digitization include issues like connectivity, interoperability, data quality, trust, and incentives.
  3. Farmers derive value from data through decision-making, enabling technologies, sharing with advisors, compliance, and future income opportunities.

Data Science Weekly - Issue 542

Data Science Weekly Newsletter • 139 implied HN points • 12 Apr 24
🕹 Technology Data science AI Machine Learning Programming Analytics
  1. This newsletter provides links and updates about data science, AI, and machine learning. It's a helpful resource for anyone wanting to stay informed in this field.
  2. One article teaches how to handle real questions using Python, which is great for people wanting practical coding skills. Another discusses techniques to make sure AI outputs stay on task.
  3. The newsletter also features resources and courses to help people learn and improve their skills in data science and related areas. It's a good place to find learning opportunities.

sqlmesh init -t dbt

davidj.substack • 179 implied HN points • 02 Dec 24
🕹 Technology Software Data Engineering Development Analytics
  1. SQLMesh recently announced that it is backwards compatible with dbt projects. This means teams can gradually switch to SQLMesh without having to do a big migration all at once.
  2. Using SQLMesh can help improve the clarity of data workflows and avoid broken DAGs during development. It offers features that make managing complex data stacks easier.
  3. Migrating to SQLMesh is possible even for those who aren't very tech-savvy. The process can be simple and done in an afternoon, making it accessible for teams to test and implement.

Issue #2 - The Data Ecosystem: Where do you even start?

The Data Ecosystem • 119 implied HN points • 21 Apr 24
🕹 Technology Data science Machine Learning Data Engineering Data Governance Analytics
  1. Data can be really complicated, and it's easy to miss how everything connects. People often focus on their own area and forget about the bigger picture of the data ecosystem.
  2. Chief Data Officers (CDOs) are important but can only do so much to fix data issues. They deal with many challenges, including limited power, lack of experience, and politics within the organization.
  3. To improve in the data field, we need to recognize the gaps in our knowledge, prioritize what to focus on, and continuously educate ourselves in both our own areas and related data domains.

Celebrating An Anniversary: Three Years of Writing About Analytics - Issue 154

Data Analysis Journal • 452 implied HN points • 26 Jul 23
🕹 Technology Analytics Data science Newsletter Writing Career development
  1. The author reflects on three years of writing a newsletter about analytics, thanking supporters and subscribers.
  2. The author's newsletter aims to document their journey, bridge the gap between academics and industry, and encourage classic data analysis.
  3. The author shares insights on their writing strategy, the power of being small and independent, and future plans for the newsletter.

GroupBy #39: 2000+ DBT models in airflow; Serverless Jupyter Notebooks at Meta

VuTrinh. • 59 implied HN points • 11 Jun 24
🕹 Technology Data Engineering Software Development Cloud Computing Analytics Data science
  1. Meta has developed a serverless Jupyter Notebook platform that runs directly in web browsers, making data analysis more accessible.
  2. Airflow is being used to manage over 2000 DBT models, which helps teams create and maintain their own data models effectively.
  3. Building a data platform from scratch can be a valuable learning experience, revealing important lessons about data structure and management.

How to think about social effectiveness to protect creative budgets

The Social Juice • 24 implied HN points • 20 Dec 24
💼 Business Marketing Social media Strategy Branding Analytics
  1. When discussing social media success, it's important to focus on effectiveness, not just creativity. You need to tell a complete story about how your ideas impact the brand and business.
  2. To have a strong approach, measure three key areas: channel effects, brand effects, and commercial effects. This way, you can show not only how many people saw your content but also how it influenced brand awareness and sales.
  3. Always establish clear success measures before starting a project. This helps everyone understand how to evaluate success and can lead to better budget support in the future.

Data Science Weekly - Issue 521

Data Science Weekly Newsletter • 339 implied HN points • 17 Nov 23
🕹 Technology Data science Machine Learning AI Programming Analytics
  1. JAX is becoming popular for its speed and capabilities, and learning it may be essential for those familiar with PyTorch. It does have a steeper learning curve, but there are resources to help ease the transition.
  2. The demand for GPUs is skyrocketing, driven by various market factors. Understanding these dynamics can help anticipate the future of technology and resource availability in industries reliant on powerful computing.
  3. Freelancing in data science can lead to an overwhelming number of job offers. Tips on finding clients on platforms like Upwork and LinkedIn can help navigate this new freelance landscape.

Data Science Weekly - Issue 524

Data Science Weekly Newsletter • 299 implied HN points • 08 Dec 23
🕹 Technology Data science AI Machine Learning Data Engineering Analytics
  1. Data engineering is evolving with new design patterns that help improve efficiency in handling data. A new book dives into these patterns and their importance.
  2. Machine learning is being used to understand and control the movement of silicon atoms in materials, which could lead to advancements in technology like better electronics.
  3. A new model called PoseGPT can estimate 3D human poses from images and text, linking physical movements to broader concepts about humans, showing the capabilities of large language models.

Why Today Is The Perfect Time to Learn Data | Seattle Data Guy

Data Analysis Journal • 373 implied HN points • 25 Oct 23
🕹 Technology Data science Analytics Data Engineering Learning Resources Career Advice
  1. Learning data is more accessible and better now than in the past years.
  2. For transitioning into data engineering, focus on SQL, programming, data warehouse, and data pipelines.
  3. Analysts should focus on understanding the business problem, building maintainable systems, and following a data analytics process.

Spotify, Revolut and the rise of the Super Sub

Department of Product • 393 implied HN points • 22 Jun 23
💼 Business Tech Subscription Analytics Product Launch Market Trends
  1. Some tech companies are experimenting with higher-priced subscription tiers to offer new features to exclusive users.
  2. Revenue generation is a key focus for many product teams, leading to new pricing strategies.
  3. Pricing experiments like launching super premium subscriptions are worth monitoring for trends in the industry.

Landing page improvements

André Casal's Substack • 19 implied HN points • 29 Jul 24
🕹 Technology Web Development User Experience Product Design Market Strategy Analytics
  1. Improving color contrast on a landing page helps make it more accessible for users. Clearer visuals can attract more visitors and keep them engaged.
  2. Adding logos and use-case sections to a landing page can help communicate what the product is about. It makes it easier for potential customers to understand if the product fits their needs.
  3. Getting feedback on a landing page and iterating on it is essential for creating a successful product. Regular updates based on user input help build trust and improve overall user experience.

📽 Webinar: How Convirza Scaled SLMs for Real-Time Call Analytics – Without Breaking the Bank

TheSequence • 126 implied HN points • 15 Nov 24
🕹 Technology AI Analytics Webinars Infrastructure Scalability
  1. Convirza found a way to analyze call data quickly and affordably. They combined many tools into one setup, making everything run smoother.
  2. Their response time for customers is now under two seconds, even when many people are using the service. This helps workers get the info they need fast.
  3. By switching to a new system, they reduced costs a lot. They no longer need expensive machines for each task, which keeps their expenses low while still providing accurate results.

What does Alteryx do?

Technically • 12 implied HN points • 07 Jan 25
🕹 Technology Data Tools Analytics Software Business Intelligence Data Management
  1. Alteryx is a tool that helps teams make sense of messy data without needing to code. It allows people to clean and analyze their data easily.
  2. Many companies have limited access to specialized data teams, which makes tools like Alteryx important for non-technical users.
  3. Alteryx started with a simple workflow builder for data cleaning but has grown to include many other analytics tools over time.

SDF

davidj.substack • 59 implied HN points • 12 Feb 25
🕹 Technology Data Tools Software Development Data Engineering Machine Learning Analytics
  1. SDF and SQLMesh are alternatives to dbt for data transformation. They are both built with modern tech and aim to provide better ease of use and performance.
  2. SDF has a built-in local database, allowing developers to test queries without costs from a cloud data warehouse. This can speed up development and reduce costs.
  3. Both tools offer column-level lineage to track changes, but SQLMesh provides a better workflow for managing breaking changes. SQLMesh also has unique features like Virtual Data Environments that enhance developer experience.

Leaving product analytics

timo's substack • 314 implied HN points • 05 Jun 23
🕹 Technology Analytics Product Data Warehousing
  1. Product analytics tools like Amplitude, Mixpanel, and Heap are evolving to offer new features like marketing attribution and user experience analytics.
  2. New players in the market like Kubit are focusing on providing product analytics directly on cloud data warehouses.
  3. The future of analytics is moving towards event analytics, opening up new possibilities and challenges for businesses.

Data Science Weekly - Issue 516

Data Science Weekly Newsletter • 299 implied HN points • 13 Oct 23
🕹 Technology Data science AI Machine Learning Data Engineering Analytics
  1. The newsletter is deciding whether to publish twice a week, but will stick to one issue for now to review feedback from readers.
  2. There's a focus on providing useful resources for data science, including articles and job opportunities in the field.
  3. New tools and methods in AI and data engineering are highlighted, addressing challenges like data integration and AI model training.

Data Science Weekly - Issue 511

Data Science Weekly Newsletter • 319 implied HN points • 07 Sep 23
🕹 Technology Data science AI Machine Learning Analytics Software Development
  1. AI startups can receive significant support through programs like AI Grant, offering up to $250,000 for development.
  2. Recent studies have shown that large language models can learn from just one example, which challenges previous beliefs about their efficiency.
  3. Using advanced tools like the Semantic Layer and LLMs can greatly improve data accuracy and speed for businesses, making analytics much easier.

Data Science Weekly - Issue 515

Data Science Weekly Newsletter • 299 implied HN points • 06 Oct 23
🕹 Technology Data science Artificial Intelligence Machine Learning Analytics Software Development
  1. There's a lot happening in data science right now. The team is considering adding a second newsletter each week to cover more exciting content.
  2. High-performing data scientists have specific traits that set them apart from others. Companies are researching these traits to help improve their teams.
  3. Art institutions can greatly benefit from data and analytics. Collaborating with leaders can help them use data to improve their operations and strategies.

Favorable Winds for AMD in the GenAI Chip Market

Gradient Flow • 139 implied HN points • 08 Feb 24
🕹 Technology Hardware AI Software Privacy Analytics
  1. AMD's hardware offers performance and efficiency gains for AI tasks, with specialized optimizations making them well-suited for training and inference in advanced AI scenarios.
  2. AMD has invested in mature and optimized open-source software like the ROCm stack, providing a critical foundation for maximizing the performance of their hardware in real-world AI applications.
  3. Market trends are aligning favorably for AMD, with shorter lead times improving chip availability, notable endorsements from industry leaders, and growing momentum indicating a strong position in the AI silicon landscape.

Data Science Weekly - Issue 520

Data Science Weekly Newsletter • 239 implied HN points • 10 Nov 23
🕹 Technology Data science Machine Learning Artificial Intelligence Data Visualization Analytics
  1. Data scientists share interesting links and news weekly about AI, machine learning, and data visualization. It's a great way to stay updated on trends and tools in the field.
  2. Learning about the basics of deep learning and mathematical foundations is important for anyone starting in machine learning. Understanding key concepts helps you tackle complex problems more effectively.
  3. There are many job opportunities in data science and related fields. Keeping an eye on openings can lead to exciting career advancements and collaborations.

Revenge of the Nerds

The Better Letter • 196 implied HN points • 08 Dec 23
🎾 Sports Analytics Baseball Innovation Management Statistics
  1. Baseball's analytics revolution owes its existence to a smart security guard creating statistical analysis accessible and interesting.
  2. The success of 'Moneyball' accelerated the statistical disruption in baseball and led to the widespread use of advanced statistical measures in MLB.
  3. The Bill James approach transformed baseball analysis to be more objective, relevant, and useful, impacting team strategies and decision-making.

Issue #4 - The Five Minute History of Data

The Data Ecosystem • 59 implied HN points • 05 May 24
🕹 Technology Data science AI Analytics Data processing Cloud Computing
  1. Data is generated and used everywhere now, thanks to smart devices and cheaper storage. This means businesses can use data for many purposes, but not all those uses are helpful.
  2. Processing data has become much easier over the years. Small companies can now use tools to analyze data without needing a team of experts, although some guidance is still necessary.
  3. Analytics has shifted from just looking at past data to predicting future trends. This helps companies make better decisions, and AI is starting to take over some of these tasks.

You can take your gold and shove it...

davidj.substack • 59 implied HN points • 13 Jan 25
🕹 Technology Data architecture Data processing Analytics Data Models Software Development
  1. The gold layer in data architecture has drawbacks, including the loss of information and inflexibility for users. This means important data could be missing, and making changes is hard.
  2. Universal semantic layers offer a better solution by allowing users to request data in plain language without complicated queries. This makes data use easier and more accessible for everyone.
  3. Switching from a gold layer to a semantic layer can improve efficiency and user experience, as it avoids the rigid structure of the gold layer and adapts to user needs more effectively.

Embracing the New Era of Accelerated Testing - Issue 150

Data Analysis Journal • 235 implied HN points • 28 Jun 23
🕹 Technology Data science Analytics A/B Testing Experimentation Tooling
  1. Embracing accelerated testing in the modern data analysis landscape is essential for success.
  2. The current traditional academic workflow for A/B testing may not be suitable for the evolving landscape of experimentation.
  3. To thrive in the era of rapid feature flagging and A/B testing, teams need to adapt by automating statistical checks, simplifying documentation, and eliminating bias.