The hottest Analytics Substack posts right now

And their main takeaways
Category
Top Technology Topics

Managing Dependencies Between Data Pipelines

Practical Data Engineering Substack • 0 implied HN points • 26 Aug 23
🕹 Technology Analytics
  1. Managing dependencies between data pipelines is crucial for ensuring that upstream tasks are completed before downstream tasks start. This avoids issues with incomplete or faulty data.
  2. There are different techniques to manage these dependencies, ranging from simple time-based scheduling to more complex orchestrations that adjust based on the successful completion of previous tasks.
  3. Choosing the right method for managing pipeline dependencies depends on the complexity of the data workflows and the need for independence between different teams and tasks.

Get rid of user confusion with better empty states

CommandBlogue • 0 implied HN points • 10 Apr 24
🕹 Technology Analytics
  1. Empty states in apps can confuse users when there's no data to show. It's important to fill that space with meaningful actions.
  2. Instead of just saying 'no events found,' apps can encourage users to create new content, making the experience more engaging.
  3. Sometimes users want to see empty spaces as they indicate they've reached their goals, like finishing a to-do list. Celebrating that can enhance satisfaction.

[in case you missed it ] Data Science Weekly - Issue 472

Data Science Weekly Newsletter • 0 implied HN points • 11 Dec 22
🕹 Technology Analytics
  1. Machine learning can have unintended biases if the training data includes wrong patterns. It's important to check how models make decisions to avoid mistakes.
  2. You can use machine learning in Google Sheets without any coding or data sharing. There are easy tools available that let anyone analyze data and make predictions.
  3. Realtime machine learning is becoming a trend in tech companies, which means they want to make their data analysis and model scoring faster and more efficient.

[in case you missed it] Data Science Weekly - Issue 468

Data Science Weekly Newsletter • 0 implied HN points • 13 Nov 22
🕹 Technology Analytics
  1. Before leaving Twitter, it's a good idea to download and save your data. This way, you can analyze important trends and insights you might miss if you just leave.
  2. The command line can make data processing easier and more readable. New tools like SPyQL help bridge familiarity with SQL and Python for better data analytics.
  3. Federated learning allows multiple users to train models without sharing their raw data. This technology can enhance privacy while still allowing valuable insights from diverse data sources.

[in case you missed it] Data Science Weekly - Issue 458

Data Science Weekly Newsletter • 0 implied HN points • 04 Sep 22
🕹 Technology Analytics
  1. Machine learning has best practices that can help improve projects. A document from Google shares these tips for those who have some background in ML.
  2. There is a lot of hype around deep learning technology, leading to confusion about its actual capabilities. People have been predicting big changes in jobs and advancements, but many advancements are still awaited.
  3. AI can create interesting art from text prompts using tools like DALL·E 2. This showcases how technology can blend creativity and machine learning.
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[in case you missed it] Data Science Weekly - Issue 457

Data Science Weekly Newsletter • 0 implied HN points • 28 Aug 22
🕹 Technology Analytics
  1. AI has limits when it comes to understanding human language. It can't fully replicate how humans think because language itself is restrictive.
  2. Observable now offers Free Teams, making it easier for data people to collaborate publicly. You can create teams quickly and share notebooks without complicated setups.
  3. The backpropagation algorithm in machine learning is often misunderstood. It is more complex than just applying the chain rule repeatedly, and oversimplifying it can lead to problems.

[in case you missed it] Data Science Weekly - Issue 409

Data Science Weekly Newsletter • 0 implied HN points • 26 Sep 21
🕹 Technology Analytics
  1. Trees are becoming a new model for understanding ecology and plant intelligence. They help researchers think more deeply about the environment.
  2. Effective machine learning often starts without actually using machine learning. It’s important to focus on gathering quality data and defining clear processes first.
  3. Business Intelligence (BI) tools are evolving, but they should focus on providing clear and complete answers to data-related questions for users.

[in case you missed it] Data Science Weekly - Issue 405

Data Science Weekly Newsletter • 0 implied HN points • 29 Aug 21
🕹 Technology Analytics
  1. Data teams should treat their work as products for their colleagues, focusing on collaboration to create effective solutions. This helps ensure that the end result meets the needs of those using the data.
  2. Many machine learning funds in finance fail due to common mistakes, but the few that succeed can deliver impressive results for investors. Understanding these pitfalls is key to improving success rates.
  3. OpenAI's Ilya Sutskever has been a major influence in AI, contributing to key advancements in deep learning. His work has played a big role in the evolution of intelligence in machines.

[in case you missed it] Data Science Weekly - Issue 399

Data Science Weekly Newsletter • 0 implied HN points • 18 Jul 21
🕹 Technology Analytics
  1. There's a growing movement called 'Data for Good', which focuses on using data to help improve society. It's important to understand the different groups and initiatives within this space.
  2. Peer review in data science is crucial, especially for startups, but the process can be tricky. It's good to learn from experiences about what works and what doesn't.
  3. Big companies like Amazon collect a lot of data about their users, often more than people realize. It's important to be aware of how this data is being tracked and used.

[in case you missed it] Data Science Weekly - Issue 395

Data Science Weekly Newsletter • 0 implied HN points • 20 Jun 21
🕹 Technology Analytics
  1. TinyML is a growing field with many projects and papers exploring its potential. It's basically about running machine learning on small devices.
  2. There are different technologies like Dask and Vaex for processing large datasets in Python. Each has its own strengths, so it's good to know which one fits your needs.
  3. Understanding multi-objective optimization can help you make better decisions in complex situations. It's about looking at several goals at once instead of just one.

[in case you missed it] Data Science Weekly - Issue 385

Data Science Weekly Newsletter • 0 implied HN points • 11 Apr 21
🕹 Technology Analytics
  1. Building a good machine learning rig can be expensive. But with careful planning and research, you can create an effective setup.
  2. Understanding adaptive data analysis is important for trusting your models. New methods are being developed to address issues with model evaluation.
  3. Model compression techniques can help enhance performance. This includes strategies like quantization and knowledge distillation to make models smaller and faster.

[in case you missed it] Data Science Weekly - Issue 370

Data Science Weekly Newsletter • 0 implied HN points • 27 Dec 20
🕹 Technology Analytics
  1. 2020 saw significant advancements in AI, especially with neural volume rendering and models that can learn rules themselves.
  2. Data scientists are in high demand, and platforms like Vettery can help job seekers connect with employers.
  3. Resources are available to help aspiring data scientists improve their skills, build portfolios, and create impactful resumes.

[in case you missed it] Data Science Weekly - Issue 369

Data Science Weekly Newsletter • 0 implied HN points • 20 Dec 20
🕹 Technology Analytics
  1. Companies are now changing how they present information because machines and AI read their reports too. They're trying to make it easier for algorithms to understand, sometimes even avoiding negative words that might confuse them.
  2. Monitoring machine learning in production is crucial. It's important to catch any unusual patterns or changes in how models behave to ensure they keep performing well.
  3. Artificial intelligence is being developed to better interact with humans. By using virtual environments, researchers are teaching AI to mimic human behaviors and improve interaction quality.

[in case you missed it] Data Science Weekly - Issue 364

Data Science Weekly Newsletter • 0 implied HN points • 15 Nov 20
🕹 Technology Analytics
  1. Organizing data in spreadsheets helps reduce errors. Use consistent formats, avoid empty cells, and save backups to make analysis easier.
  2. AI is creating convincing fake music performances of famous artists. This raises legal concerns as the music industry watches closely.
  3. Monitoring performance is crucial in data science. Tools like Mona help track data and model performance to avoid issues like biases and errors.

[in case you missed it] Data Science Weekly - Issue 362

Data Science Weekly Newsletter • 0 implied HN points • 01 Nov 20
🕹 Technology Analytics
  1. Using AI for form extraction can greatly help fields like journalism and medicine. This could be more impactful than just predictive models.
  2. Data intuition is an important skill for data scientists. It helps them avoid being misled by bad data and analyses.
  3. Data engineering and data science are interconnected, but they have different focuses. Data engineering deals with preparing data, while data science analyzes it for insights.

[in case you missed it] Data Science Weekly - Issue 358

Data Science Weekly Newsletter • 0 implied HN points • 04 Oct 20
🕹 Technology Analytics
  1. Data quality is really important for machine learning to work well. If the data is bad, it can mess up the whole project and make people doubt the results.
  2. The State of AI Report covers current trends and future predictions in artificial intelligence. It looks into research advances, talent availability, and the impact of AI on industries.
  3. Using mobile phone data can help understand and manage the COVID-19 pandemic. However, it's crucial to consider what types of behaviors and populations this data represents.

[in case you missed it] Data Science Weekly - Issue 355

Data Science Weekly Newsletter • 0 implied HN points • 13 Sep 20
🕹 Technology Analytics
  1. DeepMind and Google Maps teamed up to improve travel time predictions using advanced technology called Graph Neural Networks. This helps users get even more accurate arrival times in busy cities.
  2. AI technology is now being used to spot edited videos, like deepfakes, by detecting hidden signals called 'deepfake heartbeats'. This could make it easier to tell which video was made with what software.
  3. A new book aims to teach machine learning from scratch, breaking down complex algorithms to make them understandable. It's a good resource for anyone wanting to learn the basics of machine learning.

[in case you missed it] Data Science Weekly - Issue 346

Data Science Weekly Newsletter • 0 implied HN points • 12 Jul 20
🕹 Technology Analytics
  1. A workshop at the Santa Fe Institute explored the meaning and understanding in AI, involving participants from different fields to discuss how machines might understand like living beings.
  2. The cost of training AI is dropping much faster than expected, making it easier for companies to adopt AI technology in the coming years.
  3. Training Generative Adversarial Networks (GANs) presents challenges, but new algorithms are being developed to improve stability and performance in machine learning.

[in case you missed it] Data Science Weekly - Issue 345

Data Science Weekly Newsletter • 0 implied HN points • 05 Jul 20
🕹 Technology Analytics
  1. Machine learning is becoming more practical and useful in real-world applications. It's important to focus on making this technology work effectively for various industries.
  2. AI is a fast-evolving field with many developments happening globally, and discussions about its future are crucial for guiding its ethical use and advancements.
  3. Transparency in machine learning models is essential. Providing clear documentation about how they work helps ensure they are used correctly and responsibly.

[in case you missed it] Data Science Weekly - Issue 324

Data Science Weekly Newsletter • 0 implied HN points • 08 Feb 20
🕹 Technology Analytics
  1. Experimentation is key in product development. Good experiments help in understanding customer needs better and making informed decisions.
  2. AI technology can have a real-world impact, as seen with early warnings about health crises. Tools like AI can gather and analyze data faster than traditional methods.
  3. Improving AI means making it more human-like for better performance. Understanding the limits and potential of AI can help us use it more effectively.

[in case you missed it] Data Science Weekly - Issue 321

Data Science Weekly Newsletter • 0 implied HN points • 18 Jan 20
🕹 Technology Analytics
  1. Hiring smart people can be tricky because many recruiters rely on strict rules and fancy degrees. There’s a chance to find great talent if you look beyond the typical criteria.
  2. As machine learning gets better, it can sometimes cause mistakes in human decision systems, known as the 'Uncanny Valley.' It's important to design these systems carefully to avoid problems.
  3. TinyML is an exciting area of machine learning that lets small devices analyze data using minimal power. This means everyday items like printers and cars can now perform complex tasks with smarter tech.

[in case you missed it] Data Science Weekly - Issue 293

Data Science Weekly Newsletter • 0 implied HN points • 06 Jul 19
🕹 Technology Analytics
  1. The State of AI Report 2019 highlights how fast AI is growing and important developments from the past year.
  2. Machine learning is now being used to translate languages that were previously lost, opening up new ways to understand history.
  3. There are many resources and guides available for those wanting to get started in data science, covering everything from building a portfolio to writing a great resume.

[in case you missed it] Data Science Weekly - Issue 260

Data Science Weekly Newsletter • 0 implied HN points • 16 Nov 18
🕹 Technology Analytics
  1. There are many resources available for learning machine learning, so it's helpful to gather them in one place for quick access.
  2. Lyft has developed tools to handle seasonal market changes, which could help predict when driver incentives are needed.
  3. Getting a data science job can be tough, but reflecting on the journey can show how previous challenges helped lead to success.

[in case you missed it] Data Science Weekly - Issue 244

Data Science Weekly Newsletter • 0 implied HN points • 28 Jul 18
🕹 Technology Analytics
  1. Companies need to define data science roles clearly, focusing on three areas: Analytics, Inference, and Algorithms. This helps businesses meet their specific needs effectively.
  2. Google's AutoML grabs attention for simplifying machine learning tasks, but understanding concepts like transfer learning is essential to grasp its true potential.
  3. Multi-task learning allows machines to learn multiple tasks at once, making them smarter and better at complex challenges, similar to how humans learn.

[in case you missed it] Data Science Weekly - Issue 239

Data Science Weekly Newsletter • 0 implied HN points • 23 Jun 18
🕹 Technology Analytics
  1. AI can argue like a human but it doesn't really understand what it's saying. This raises questions about the limits of AI in communication.
  2. Researchers are working hard to make algorithms fair to avoid biases in machine learning. This is important as technology becomes more involved in our lives.
  3. Experts are discussing how AI and robotics can change healthcare, pointing to a future where technology plays a big role in medicine.

Building CAUSL - Episode 12: The Future of CAUSL

CAUSL Effect • 0 implied HN points • 30 Aug 23
💼 Business Analytics
  1. A merger with LeanConvert means a big change for CAUSL. The consulting side will stop, but the focus will shift to analytics and experimentation.
  2. The journey isn't over; it's just changing direction. The goal was always to get to this new role, and it feels like a new beginning.
  3. There will still be updates through the Building CAUSL newsletter. The author plans to share the development of a free analytics toolkit for everyone.

GroupBy #24: Enabling near real-time data analytics on the data lake at Grab, Aligning Velox and Apache Arrow at Meta.

VuTrinh. • 0 implied HN points • 27 Feb 24
🕹 Technology Analytics
  1. Grab is working on letting users analyze data quickly with their new approach to data lakes. This helps businesses get insights much faster.
  2. Meta is aligning Velox and Apache Arrow to improve data management. This should make it easier to handle and analyze large amounts of data.
  3. PayPal is using Spark 3 and NVIDIA's GPUs to cut their cloud costs by up to 70%. This helps them process a lot of data without spending too much money.

Mastering Window Functions: Your Gateway to Advanced SQL Analytics

DataSketch’s Substack • 0 implied HN points • 07 Oct 24
🕹 Technology Analytics
  1. Window functions let you do calculations across rows related to your current row without losing any details. This helps you get both summarized and detailed data at the same time.
  2. Using window functions can make complex data tasks easier, like ranking items or finding running totals. They are very helpful in fields like healthcare to analyze patient data and improve efficiency.
  3. It's important to test how window functions perform on a smaller dataset before using them widely. Combining multiple window functions and partitioning your data smartly can also boost performance.

FortuneGPT: When AI Reads Your Future (and Your Slack Messages)

Phoenix Substack • 0 implied HN points • 25 Feb 25
🕹 Technology Analytics
  1. FortuneGPT mixes tarot reading with AI to predict your future based on your data and habits. It's like having a digital fortune teller who uses real information to give you insights.
  2. The app learns from each reading, becoming better at understanding your worries over time. It can adjust its advice based on your mood and past decisions.
  3. FortuneGPT offers a free version and multiple paid plans that upsell deeper insights and predictions. It's designed to keep users engaged and curious, almost like a subscription service for mystical insights.

Build Robust Enterprise AI Vaults

OSS.fund Newsletter • 0 implied HN points • 05 Jun 25
🕹 Technology Analytics
  1. Having clean and well-organized data is really important for making AI systems work properly. If the data is messy, it can cause a lot of problems.
  2. Creating an AI-ready vault helps businesses manage their data better. It can reduce costs, improve efficiency, and keep sensitive information private.
  3. The process of building this vault should be well-managed like a product, with a dedicated owner to keep track of progress and improvements.

What would you most like to learn in Quant Finance?

Quantitative Finance - Research, Trading, Investing, & Algos • 0 implied HN points • 03 Jun 25
💰 Finance Analytics
  1. Learning about stochastic calculus, like Brownian motion and Itô’s Lemma, is important for understanding financial models. These concepts help us predict how prices will change over time.
  2. Mastering derivatives pricing, including the Black-Scholes model, is crucial for anyone dealing with options and risk management. It helps you figure out how much options should be worth.
  3. Exploring portfolio optimization techniques, like mean-variance, can help investors make better choices about how to allocate their money. It's about balancing risk and return effectively.

Online event, 4pm, This Wednesday, Federated Learning for data scientists and statisticians

RSS DS+AI Section • 0 implied HN points • 09 Jun 25
🕹 Technology Analytics
  1. There's an online talk about federated learning happening this Wednesday at 4 PM. It's a great chance to learn from experts in the field.
  2. The talk will explain how federated learning is different from traditional analysis. You'll find out what it means for the future of data science.
  3. Participants will also discuss the challenges of federated analytics and how it works today. It's a good opportunity to think about new possibilities in data analysis.

Hedge-fund quality equity research, on demand

Valuabl • 0 implied HN points • 16 Jul 25
💼 Business Analytics
  1. ValuationBot helps you get stock valuations quickly, taking just 10 minutes unlike traditional methods that can take weeks. It uses the same techniques as top analysts for accurate results.
  2. Each report costs only $2.50 and comes with downloadable PDF and Excel models, making it affordable and easy to use. No need for subscriptions or complicated plans.
  3. If you're not happy with your reports, you can get a full refund without any hassle. It's a risk-free way to try out new research methods.

Using Omni’s AI Assistant on the Semantic Layer

Inside Data by Mikkel Dengsøe • 0 implied HN points • 18 Jul 25
🕹 Technology Analytics
  1. Using AI tools like Omni’s Assistant can help make data requests easier for everyone in a company. This self-serve system allows users to ask their own questions and get answers without waiting for analysts.
  2. It's important to carefully set up the data and provide clear guidelines. By organizing the data into 'Topics' and adding descriptions, the AI can better understand the information it needs to work with.
  3. However, there are still some challenges. The AI can struggle with complex questions or unclear requests, so it's good to test its performance and make adjustments when things don't go well.

Scope discipline when AI makes building fast

Bit Byte Bit • 0 implied HN points • 23 Dec 25
🕹 Technology Analytics
  1. Choose the right tool: build core, domain-specific messaging yourself and use SaaS like PostHog only where it clearly adds value (surveys, A/B tests).
  2. AI makes building fast and encourages scope creep, so keep your MVP narrow, put extras on an ideas list, and only implement features that solve the current problem.
  3. Don’t keep perfectly clean code you don’t need because it creates a maintenance burden. Use simple, flexible patterns (global LiveView hooks and small function-based rules) so you can extend behavior later without heavy rewrites.