The hottest Analytics Substack posts right now

And their main takeaways
Category
Top Technology Topics

Data Science Weekly - Issue 430

Data Science Weekly Newsletter • 19 implied HN points • 17 Feb 22
🕹 Technology Analytics
  1. Data businesses are important but not well-studied, and understanding their models can help in a tech-focused market.
  2. Investors are focusing on machine learning and its challenges, which can show opportunities for startups in that field.
  3. Machine learning is evolving, especially with advances in compute requirements, which are becoming crucial for training complex models.

Data Science Weekly - Issue 429

Data Science Weekly Newsletter • 19 implied HN points • 10 Feb 22
🕹 Technology Analytics
  1. Data science models need regular monitoring after deployment. They can lose effectiveness over time, so it's important to keep an eye on their performance.
  2. Recommender systems help users find relevant content among large amounts of data. They are essential tools for platforms like YouTube and Facebook.
  3. Causal knowledge is important for making good business decisions. Relying solely on prediction-based methods may not address complex managerial problems.

✳✴❇ Behavioural Science, Better Data Engineering & Timnit Gebru’s New Institute

Sector 6 | The Newsletter of AIM • 19 implied HN points • 05 Dec 21
🕹 Technology Analytics
  1. Behavioral science can improve how data engineering is done. Understanding how people think and behave helps create better tech solutions.
  2. There’s a new hackathon for data scientists featuring a challenge to predict loan defaults. It has already attracted over 1,000 participants.
  3. A conference for machine learning developers will be held in-person in Bangalore. It's a great opportunity to learn and connect with others in the field.

Data Science Weekly - Issue 425

Data Science Weekly Newsletter • 19 implied HN points • 13 Jan 22
🕹 Technology Analytics
  1. Be careful when joining a data or tech team; look for warning signs that could mean trouble. It's important to ensure a good fit for your career.
  2. The AI job market is constantly changing, so it's good to stay informed and adapt your strategies for landing jobs in this field.
  3. Transformers are now widely used in natural language processing and are also making their way into computer vision, making it important to understand how they work.

Data Science Weekly - Issue 424

Data Science Weekly Newsletter • 19 implied HN points • 06 Jan 22
🕹 Technology Analytics
  1. New data science managers have a lot to learn in their first year. They should focus on gaining experience and reflecting on their journey to improve their skills.
  2. Chatbots still struggle with understanding complex human queries. They often provide confusing answers because they lack real-world comprehension.
  3. Real-time machine learning is a growing trend with unique challenges. Companies are talking about their pain points and seeking practical solutions for online predictions and continual learning.
Get a weekly roundup of the best Substack posts, by hacker news affinity:

✅ Monday briefing: Pandora Papers, PR pathways, graduate jobs, environmental marketing code, content effectiveness, analytics attribution, annual Foodbank fundraiser, and more...

Wadds Inc. newsletter • 19 implied HN points • 11 Oct 21
💼 Business Analytics
  1. The Pandora Papers reveal secret financial dealings of billionaires and leaders, highlighting issues of wealth secrecy. It's important to stay informed about these findings.
  2. Public relations job opportunities are on the rise, especially for fresh graduates, with agencies actively hiring. This is a good time for job seekers in PR.
  3. New guidelines on environmental marketing have been introduced to ensure businesses are honest about their eco-friendly claims. Consumers should look for transparency in environmental messaging.

Data Science Weekly - Issue 413

Data Science Weekly Newsletter • 19 implied HN points • 21 Oct 21
🕹 Technology Analytics
  1. AI can help create music, but it raises questions about artistic value and originality. It's a mix of excitement and skepticism over how machines understand creativity.
  2. Learning practical tools in computer science, like command-line and version control, is often overlooked in traditional classes. A new course aims to fill this gap by teaching these essential skills.
  3. When developing AI models, it’s important to think about their impact and safety in real-world applications. There are challenges in ensuring these models are ethical and reliable.

Data Science Weekly - Issue 409

Data Science Weekly Newsletter • 19 implied HN points • 23 Sep 21
🕹 Technology Analytics
  1. Trees can teach us a lot about intelligence and ecology. They inspire new ways to think about nature and our relationship with it.
  2. Before jumping into machine learning, focus on gathering quality data and building a solid framework. This can often mean starting without machine learning in your first steps.
  3. Business intelligence tools are changing and should help everyone make sense of data easily. They need to provide clear answers to data questions for all kinds of users.

Data Science Weekly - Issue 405

Data Science Weekly Newsletter • 19 implied HN points • 26 Aug 21
🕹 Technology Analytics
  1. Data teams should treat what they create as a product for their colleagues, focusing on what the product should feel like to ensure effective collaboration.
  2. Financial machine learning has a high failure rate, but successful managers can achieve great results; knowing the common mistakes can help avoid failure.
  3. There's a lot of potential in using AI for complex tasks, like how DeepMind's agents can play new games without prior training, showcasing advancements in reinforcement learning.

Deep Learning vs XGBoost, MXNet, Hugging Face & more...

Sector 6 | The Newsletter of AIM • 19 implied HN points • 20 Jun 21
🕹 Technology Analytics
  1. Deep learning is powerful for tasks like image and speech recognition due to its complex layers. It's great for understanding patterns in large datasets.
  2. XGBoost and MXNet are tools that can be very efficient for structured data and competitions, often requiring less data than deep learning.
  3. Hugging Face is popular for natural language processing, making it easy to use advanced models without needing deep expertise in AI.

Data Science Weekly - Issue 398

Data Science Weekly Newsletter • 19 implied HN points • 08 Jul 21
🕹 Technology Analytics
  1. Data science is actively used in many areas like music analysis and causal inference for pricing strategies. These projects help us understand large datasets and make better decisions.
  2. Languages vary in how they describe colors, reflecting cultural differences. Some cultures have fewer color terms, which sparks curiosity about societal influences on language.
  3. Combining different models, like CNNs and Transformers in computer vision, can lead to better performance. This blend helps create more accurate and diverse predictions in image-related tasks.

Google’s Ethical Dilemma, Cloud leaders & Maths

Sector 6 | The Newsletter of AIM • 19 implied HN points • 21 Mar 21
🕹 Technology Analytics
  1. Google is facing tough choices about ethics in AI development. They need to balance innovation with responsibility to avoid negative impacts.
  2. Leaders in cloud computing are crucial for the growth and integration of AI technology. Their strategies will shape the future of this industry.
  3. Mathematics underpins AI and data analytics. A strong understanding of math is essential for anyone looking to work in these fields.

Shipt: Moving from startup to mature product organization

Behind the Product • 1 HN point • 13 Feb 24
💼 Business Analytics
  1. The shift from project-led to product-led culture is important for growth and longevity.
  2. Structuring a product and tech organization around market segments and operational functions is crucial for a multi-sided marketplace like Shipt.
  3. Emphasizing outcome-focused and metrics-driven problem-solving, and actively seeking and incorporating customer feedback are key priorities for Shipt's product organization.

Causal Learning, Transformers & Facebook's SEER

Sector 6 | The Newsletter of AIM • 19 implied HN points • 14 Mar 21
🕹 Technology Analytics
  1. Causal learning helps us understand cause-and-effect relationships in data. This makes it easier to make informed decisions based on the information we have.
  2. Transformers are a type of AI model that help with processing language and understanding context. They are crucial for creating advanced, responsive AI systems.
  3. Facebook's SEER project is focused on improving AI understanding by using large datasets. This aims to enhance how well AI can recognize and categorize images.

More Than 1 In 5 Analytics Teams Grew In India Amid The Pandemic

Sector 6 | The Newsletter of AIM • 19 implied HN points • 21 Feb 21
🕹 Technology Analytics
  1. More than 20% of analytics teams in India saw growth during the pandemic. This shows a rising interest in data analysis roles.
  2. Data science education is a huge market in India, nearing a billion dollars. But many people feel confused about which courses to take due to too many options.
  3. There are lots of different course names and structures, making it hard for learners to choose the best fit for their needs. A clearer platform for education could help.

The Belamy is the weekly newsletter of Analytics India Magazine. We share a curated list of best stories on AI & machine learning

Sector 6 | The Newsletter of AIM • 19 implied HN points • 07 Feb 21
🕹 Technology Analytics
  1. The Belamy newsletter shares top stories about AI and machine learning each week. It's a great way to stay updated in these fast-changing fields.
  2. Analytics India Magazine also highlights important technological advancements in analytics, data science, and big data. This helps readers understand new trends and innovations.
  3. You can sign up for a free trial to explore the newsletter's archives. This is a good chance to see if the content is a good fit for you.

Data Science Weekly - Issue 377

Data Science Weekly Newsletter • 19 implied HN points • 11 Feb 21
🕹 Technology Analytics
  1. Machine learning is being used in interesting ways, like tracking pets at home with Bluetooth and specialized detectors. It's cool to see technology helping us keep track of our furry friends.
  2. There's a shift from using Excel to Python in industries that need tech improvements. Companies are finding that Python can handle complex tasks and data much better than traditional methods.
  3. Active learning in machine learning helps reduce the amount of labeled data needed to train models. By letting the model ask questions about uncertain data, it learns more efficiently.

Data Science Weekly - Issue 370

Data Science Weekly Newsletter • 19 implied HN points • 24 Dec 20
🕹 Technology Analytics
  1. NeRF technology made big waves in 2020, changing how we render 3D images with neural networks. It’s a cool new area in data science that’s just starting to grow.
  2. DeepMind's MuZero AI is impressive because it learns the rules of games by itself, improving how we analyze videos. This could lead to cost cuts for platforms like YouTube.
  3. If you're looking to start a career in data science, there are practical guides available. These can help you with everything from filling knowledge gaps to creating a strong portfolio.

Data Science Weekly - Issue 365

Data Science Weekly Newsletter • 19 implied HN points • 19 Nov 20
🕹 Technology Analytics
  1. It's important to connect with AI researchers as people, not just through their work. Personal stories can give better insights into their lives and motivations.
  2. Dynamic data testing is crucial for effective data analysis. Unlike software testing, data needs flexible tests that can adjust as it changes.
  3. Creating open datasets for sound events helps improve research in machine learning. These datasets can provide valuable resources for training models.

Data Science Weekly - Issue 351

Data Science Weekly Newsletter • 19 implied HN points • 13 Aug 20
🕹 Technology Analytics
  1. Machine learning models need regular maintenance after deployment. It's important to monitor data and model behavior to avoid problems and improve performance.
  2. Collaboration and good understanding of problems are key in AI development. This helps teams create better applications and make profits.
  3. New tools and resources are becoming available for data science, like access to research papers on Kaggle. These can help improve machine learning techniques and open up new possibilities.

Data Science Weekly - Issue 344

Data Science Weekly Newsletter • 19 implied HN points • 25 Jun 20
🕹 Technology Analytics
  1. As AI systems become more common, it’s important to think about who is responsible when things go wrong. Recent incidents raise questions about how to share accountability between people, companies, and governments.
  2. Scientists are learning more about years of small earthquakes in California, and they found that fluids moving through the ground might have caused them. This shows how understanding these events can help with studying earthquakes around the world.
  3. There are many tools for machine learning, but the landscape is still developing. A study looked at over 200 tools to find out what works best and what challenges people face when using them.

Data Science Weekly - Issue 330

Data Science Weekly Newsletter • 19 implied HN points • 19 Mar 20
🕹 Technology Analytics
  1. COVID-19 spreads very quickly, especially without measures to control it. Understanding how outbreaks work can help people take action sooner.
  2. Data and models are essential to understanding how COVID-19 will affect local areas. People should act decisively based on available information.
  3. New tools and research in data science are helping track and analyze the impact of COVID-19. These resources are making it easier to study and respond to the pandemic.

Data Science Weekly - Issue 321

Data Science Weekly Newsletter • 19 implied HN points • 16 Jan 20
🕹 Technology Analytics
  1. Hiring smarter in the job market can be achieved by looking beyond the usual qualifications. There are talented candidates who might not fit the typical mold, and recognizing this can create great opportunities.
  2. Introducing machine learning into human decision systems can lead to issues, often referred to as the 'Uncanny Valley.' It’s important to carefully design these systems to avoid performance problems.
  3. TinyML is a growing field that allows advanced machine learning to happen on small devices. This means everyday products can become smarter without needing a lot of power.

Data Science Weekly - Issue 320

Data Science Weekly Newsletter • 19 implied HN points • 09 Jan 20
🕹 Technology Analytics
  1. Creating effective data projects involves more than just building a model; you also need to consider context, strategy, and maintenance.
  2. AI can speed up material discovery by analyzing large datasets and predicting useful combinations, which could change many industries.
  3. Data lakes allow for more flexible data storage than data warehouses, but this flexibility comes with important tradeoffs to think about.

Data Science Weekly - Issue 310

Data Science Weekly Newsletter • 19 implied HN points • 31 Oct 19
🕹 Technology Analytics
  1. Rising sea levels could affect more cities than we realized, based on new research using artificial intelligence to correct earlier mistakes.
  2. Machine learning has made it possible to solve complex math problems, like the three-body problem, much faster than before.
  3. AI can learn to play video games like StarCraft II at a high level by practicing against itself, showcasing advances in gaming and strategy development.

Data Science Weekly - Issue 299

Data Science Weekly Newsletter • 19 implied HN points • 15 Aug 19
🕹 Technology Analytics
  1. AI is now being used to train models for games like video soccer, building on its success in chess and Go. This shows how far AI technology has come in mastering complex tasks.
  2. Nvidia has made big strides in AI by speeding up the training process for advanced language models. This improvement can help in developing better conversational AI systems.
  3. To become a data scientist, it's more effective to start in a related job and learn along the way. Focusing too much on skills from blog posts can lead to confusion and delay.

Highlights from June, 2023

trydeepwork • 6 implied HN points • 07 Jul 23
🕹 Technology Analytics
  1. Beeminder users are three times more productive than non-users.
  2. Website improvements: Accidental closures alert, new music option 'synth', and Power Goals polish.
  3. Upcoming enhancements and more frequent Substack posts are planned.

NeuralNextG v0.1.0: Analyzing Rocket League Games automatically with Machine Learning

joeydotcomputer’s Substack • 1 HN point • 19 Feb 23
🕹 Technology Analytics
  1. The project analyzed 200,000 Rocket League games with a neural network to predict scoring probabilities.
  2. The tool NeuralNextG can provide analysis frame-by-frame and aims to expand into coaching, scouting, win probabilities, and detecting smurfs/bots.
  3. The potential business model suggests integrating analytics tools like NeuralNextG into free-to-play games for users to pay for personalized data services.

Data Science Weekly - Issue 297

Data Science Weekly Newsletter • 19 implied HN points • 01 Aug 19
🕹 Technology Analytics
  1. Integrating data science teams within companies can help improve collaboration and effectiveness. It's important to explore different models to find what works best.
  2. Automated thinking may lead to overdependence on AI, which can cause us to miss critical thinking skills. We should be cautious about relying too much on technology.
  3. Understanding how machine learning models work is crucial for building trust. New techniques are emerging that can help explain complex models better.

Data Science Weekly - Issue 293

Data Science Weekly Newsletter • 19 implied HN points • 04 Jul 19
🕹 Technology Analytics
  1. AI is rapidly advancing, and there are important reports that analyze its progress and future implications. Staying updated can keep us informed about these changes.
  2. Machine learning is being used to translate ancient languages, bringing new opportunities to understand lost histories. This tech could unlock communication from the past.
  3. Building a strong data science portfolio and resume is crucial for job seekers in the field. Good guidance can help you showcase your skills effectively to potential employers.

Chasing highs that hurt your product

Product Managers at Work • 6 implied HN points • 19 Jun 23
💼 Business Analytics
  1. Jumping into implementation without sufficient customer discovery can hurt product success.
  2. Focusing on firefighting and feature development without data analytics can be detrimental to the product.
  3. Constantly pinging team members about updates may not be an efficient way to drive execution; creating efficient systems is key.

Data Science Weekly - Issue 286

Data Science Weekly Newsletter • 19 implied HN points • 16 May 19
🕹 Technology Analytics
  1. Los Angeles has significant noise pollution, mainly from airports and heavy traffic. A recent map highlights how loud different neighborhoods are.
  2. There's a growing debate on whether data can truly act as a competitive advantage for companies, especially with AI startups. It's worth questioning if real evidence supports this idea.
  3. A Swedish distillery is set to release the first whisky designed by artificial intelligence. It raises interesting questions about how AI can influence creative processes.

Data Science Weekly - Issue 284

Data Science Weekly Newsletter • 19 implied HN points • 02 May 19
🕹 Technology Analytics
  1. Research on reinforcement learning is showing that agents can learn as quickly as humans by combining fast and slow learning techniques.
  2. Insurance and healthcare companies can use pictures of houses to better predict risk and improve their models.
  3. Artificial intelligence could help in designing buildings by providing new insights and alternative strategies for floor plans.