The hottest Analytics Substack posts right now

And their main takeaways
Category
Top Technology Topics

Data Science Weekly - Issue 521

Data Science Weekly Newsletter • 339 implied HN points • 17 Nov 23
🕹 Technology 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 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.

Spotify, Revolut and the rise of the Super Sub

Department of Product • 393 implied HN points • 22 Jun 23
💼 Business Analytics
  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.
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Landing page improvements

André Casal's Substack • 19 implied HN points • 29 Jul 24
🕹 Technology 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.

Thank You, Goodbye and Good Luck!

davidj.substack • 143 implied HN points • 31 Jul 25
🕹 Technology Analytics
  1. Today is the author's last day at Cube and he expresses gratitude to his colleagues and investors. He feels fortunate to be in a good position and reflects on his time there.
  2. He believes in the importance and future of semantic layers in data management, which are getting better as AI technology develops. Many major cloud platforms now have their own semantic layers.
  3. The author wonders if semantic layers can operate in the background without needing constant human oversight. He is excited to see how these technologies will evolve and improve.

It's time to build

benn.substack • 1278 implied HN points • 19 Jan 24
🕹 Technology 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.

Leaving product analytics

timo's substack • 314 implied HN points • 05 Jun 23
🕹 Technology Analytics
  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.

From Boom to Bundle: The Great Consolidation of Data Tools

SeattleDataGuy’s Newsletter • 400 implied HN points • 17 Jan 25
🕹 Technology Analytics
  1. The data tools market is seeing a lot of consolidation lately, with companies merging or getting acquired. This means there are fewer companies competing, but it can lead to better tools overall.
  2. Acquisitions can be a mixed bag for customers. While some products improve after being bought, others might lose their features or support, making it risky for users.
  3. There's a push for bundled data solutions where customers want fewer, but more comprehensive tools. This could change how data companies operate and how startups survive in the future.

Data Science Weekly - Issue 516

Data Science Weekly Newsletter • 299 implied HN points • 13 Oct 23
🕹 Technology 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 Analytics
  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 Analytics
  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 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 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
  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 Analytics
  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.

Holy Grails of Data: Self-Service, Single Truths, and the Role of AI

SeattleDataGuy’s Newsletter • 365 implied HN points • 27 Dec 24
🕹 Technology Analytics
  1. Self-service analytics is still a goal for many companies, but it often falls short. Users might struggle with the tools or want different formats for the data, leading to more questions instead of fewer.
  2. Becoming truly data-driven is a challenge for many organizations. Trust issues with data, preference for gut feelings, and poor communication often get in the way of making informed decisions.
  3. People need to be data literate for businesses to succeed with data. The data team must present insights clearly, while business teams should understand and trust the data they work with.

Does Locker Drama Matter?

House of Strauss • 15 implied HN points • 08 Jan 26
🎾 Sports Analytics
  1. Stories of locker-room drama get a lot of attention, but once they’re widely reported the betting market usually prices that information in.
  2. Early rumblings are hard to measure and often make bettors nervous, even though it’s unclear how predictive they actually are.
  3. Many people believe strong team chemistry is important for winning in tough moments, but real cases show mixed outcomes so it’s not a reliable guarantee.

Embracing the New Era of Accelerated Testing - Issue 150

Data Analysis Journal • 235 implied HN points • 28 Jun 23
🕹 Technology Analytics
  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.

The Easier You Train The Faster You Finish

The Healthtech Initiative • 5 implied HN points • 09 Feb 26
🎾 Sports Analytics
  1. More total mileage in the six-month build-up leads to faster marathon times — about 4.4 minutes quicker per extra 100 km.
  2. Spending lots of time at or above race pace can backfire — extra race-pace minutes were linked to slower finish times.
  3. A large amount of easy, low-intensity running pays off — roughly 1,000 more minutes of easy training was associated with about 7 minutes faster.

The Sequence Knowledge #764: Wanna do Synthetic Data? Learn About Rephrasing

TheSequence • 28 implied HN points • 02 Dec 25
🕹 Technology Analytics
  1. Rephrasing is important for creating synthetic data. It involves rewriting data samples to keep the meaning while changing the words.
  2. This method helps to make data more diverse and reduces the risk of machines just memorizing it instead of understanding.
  3. You can use rephrasing for different types of data, like text, code, or images, and it saves time and costs compared to getting new data labeled.

Why Are Interceptions Overblown Now?

House of Strauss • 22 implied HN points • 12 Dec 25
🎾 Sports Analytics
  1. Interceptions get blown up by social media and highlight culture, so mistakes feel much bigger now and push players and teams toward avoiding visible errors.
  2. Modern efficiency stats (like passer ratings and QBR) overweight completions and punish interceptions, which incentivizes safer, shorter throws and can reduce overall offensive production.
  3. Offenses should balance efficiency with productivity by accepting some risk—more air yards, deeper targets, and occasional interceptions can lead to more yards and points than a purely conservative approach.

PostHog vs. The Industry

Alex's Personal Blog • 131 implied HN points • 17 Jun 25
🕹 Technology Analytics
  1. PostHog is a startup doing things differently in the software world, like offering mostly free and open-source tools for product development. They focus on customer-friendly policies instead of typical sales tactics.
  2. There’s increasing investment in defense and AI startups, showing a trend towards innovation in these sectors. Companies like Helsing and xAI are raising significant funds to grow their projects.
  3. High costs for coding tools are becoming more common, as shown by Anysphere's price increase for its AI coding service. Developers might need to adjust to spending more to access advanced technology.

Everyone is a CDP now

timo's substack • 157 implied HN points • 27 Nov 23
🕹 Technology Analytics
  1. The concept of a Customer Data Platform (CDP) is evolving with a focus on defining its functionality more clearly.
  2. There is a trend towards composable CDP solutions, allowing for flexibility but also potential complexity.
  3. The key value of a CDP lies in activation - using customer data to create targeted audiences for more efficient marketing strategies.

BigQuery processing engine: Shuffle

VuTrinh. • 119 implied HN points • 06 Jan 24
🕹 Technology Analytics
  1. BigQuery uses a processing engine called Dremel, which takes inspiration from how MapReduce handles data. It improves how data is shuffled between workers for faster processing.
  2. Traditional approaches have issues like resource fragmentation and unpredictable scaling when dealing with huge data. Dremel solves this by managing shuffle storage separately from the worker, which helps in scaling and resource management.
  3. By separating the shuffle layer, Dremel reduces latency, improves fault tolerance, and allows for more flexible worker allocation during execution. This makes it easier to handle larger data sets efficiently.

Worldbuilding with data

Data People Etc. • 231 implied HN points • 11 Feb 25
🕹 Technology 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.

The Analytics Requirements Document

Sarah's Newsletter • 359 implied HN points • 27 Oct 22
💼 Business Analytics
  1. Analytics should be a first-class citizen in crafting product launches to avoid wasted time and ensure measurable success.
  2. Utilize detailed agreements like Product Requirements Documents (PRD) and Analytics Requirements Documents (ARD) to align teams, outline goals, data criteria, assumptions, and finalize expectations.
  3. Involving analytics early in the product evolution lifecycle is crucial for gathering and analyzing data effectively, helping in decision-making, and ensuring alignment across technical and business teams.

Five Effective Marketing Strategies for Small Businesses on a Budget

The Small Business Corner • 39 implied HN points • 06 May 24
💼 Business Analytics
  1. Craft a targeted marketing plan to save time and effort by focusing on potential customers with a detailed strategy that aligns with your business goals.
  2. Create engaging content that resonates with your audience, providing value and building trust over time to attract and convert followers into customers.
  3. Utilize social media platforms to connect with your target audience cost-effectively, engaging with users, choosing the right platforms, and leveraging analytics for success.

Retention Analytics - the definitive guide

timo's substack • 176 implied HN points • 12 Mar 23
🕹 Technology Analytics
  1. Focus on retention rate, especially first-week retention for free users, as a key metric for product analytics
  2. Retention analytics require solid user identification to track if users are returning and engaging with your product
  3. Measure retention with cohorts to understand performance over time, highlighting improvements or decreases in user retention

Data Science Weekly - Issue 486

Data Science Weekly Newsletter • 359 implied HN points • 17 Mar 23
🕹 Technology Analytics
  1. AI and data science are evolving rapidly, making it challenging for many to keep up. It's common for professionals to feel overwhelmed as they try to understand new advancements.
  2. There's a growing discussion about whether we should slow down AI development. Some people believe we need to pause and figure out the implications of current technologies before moving forward.
  3. Many professionals are exploring career shifts between data science and data engineering. It's important to consider personal interests and skills when deciding which path to take.

The Hall of Fame is about more than WAR

Silver Bulletin • 232 implied HN points • 06 Jan 25
🎾 Sports Analytics
  1. The Hall of Fame should consider many factors, not just one statistic like Wins Above Replacement (WAR). This means looking at achievements, player talent, and character too.
  2. Players might have high WAR scores but lack the greatness often associated with Hall of Fame status. For example, a consistent but average player shouldn't necessarily be in the Hall over a standout who had fewer career years.
  3. Voters for the Hall of Fame are required to consider a player's overall impact, including postseason performances and fan appeal. This makes it a more complex decision than just focusing on statistics.

How to Win at Gambling and in Life

Kneeling Bus • 185 implied HN points • 28 Feb 25
🎾 Sports Analytics
  1. Courtsiding is when someone at a game places bets based on what they see in real time, taking advantage of the delay in betting apps. This shows how technology can create new opportunities to win in gambling.
  2. Sports betting is changing the way we consume sports media, with odds and spreads becoming more common on screens. This shift reflects a deeper trend where everything is becoming about numbers and predictions.
  3. As gambling expands into everyday life, people might start betting on personal actions. This can create new ways to have agency, suggesting that even if traditional success seems difficult, there are still ways to find success in unexpected places.

Use feature analytics for better products

timo's substack • 157 implied HN points • 31 Jul 23
🕹 Technology Analytics
  1. Digital products benefit from feature analytics for iteration speed and feedback.
  2. Features are the core aspect of product development that teams and users focus on.
  3. Creating feature dashboards and analyzing feature initiatives can enhance product development and refinement.