Data Science Weekly Newsletter

The Data Science Weekly Newsletter provides detailed insights on data science, machine learning, AI, and data engineering. It covers trends, tools, practical applications, and industry developments, emphasizing data quality, visualization, AI ethics, and career tips. Interviews and updates on evolving technologies are also highlighted.

Data Science Machine Learning Artificial Intelligence Data Engineering Data Visualization AI Ethics Career Development Data Tools and Techniques

The hottest Substack posts of Data Science Weekly Newsletter

And their main takeaways

Data Science Weekly - Issue 442

19 implied HN points • 12 May 22
🕹 Technology Data science Machine Learning Artificial Intelligence Data Visualization NLP
  1. Splitting data into training, testing, and validation sets is crucial for building effective machine learning models. It helps ensure that we evaluate our models properly.
  2. Bandit algorithms can improve recommender systems by balancing exploration of new items and exploitation of known user preferences. This way, they can discover hidden gems instead of just repeating popular choices.
  3. Protecting machine learning models and their intellectual property is important, and best practices are still evolving. It's useful to stay updated on strategies to safeguard your work in this fast-changing field.

Data Science Weekly - Issue 440

19 implied HN points • 05 May 22
🕹 Technology Data science Machine Learning Artificial Intelligence Programming Natural Language
  1. Meta AI is sharing a big language model, OPT-175B, to help others learn about new technology. This model has 175 billion parameters and is based on publicly available data.
  2. Handling harmful text in data science is a tricky issue. Researchers are looking for ways to address this challenge while still making progress in natural language processing.
  3. There are many resources and courses available for learning data science and machine learning. These include guides for using Python and R, plus access to various data visualization tools.

Data Science Weekly - Issue 440

19 implied HN points • 28 Apr 22
🕹 Technology AI Machine Learning Data science Software Development Statistical Analysis
  1. AI is getting smarter, but we need a better way to understand how it makes decisions. A common language with AI could help us communicate our questions and concerns.
  2. Creating more synthetic data can help when there's not enough real data for training models. Techniques like data augmentation can help make our data better.
  3. Making data more accessible can solve big problems for society. If we can use available data properly, it can lead to more health and happiness for everyone.

[In case you missed it] Data Science Weekly - Issue 439

19 implied HN points • 24 Apr 22
🕹 Technology Data science Machine Learning Artificial Intelligence Data Visualization Software Development
  1. Building a recommendation system is challenging. It requires careful planning and execution to serve users quickly and efficiently.
  2. Understanding different probability distributions is essential in data science. They help us make better predictions and understand the variability in our data.
  3. Contrastive learning is an important method for training machine learning models. Recent advances in this area can improve how we represent data and solve complex problems.

Data Science Weekly - Issue 439

19 implied HN points • 21 Apr 22
🕹 Technology Data science Machine Learning AI Data Visualization Statistical Analysis
  1. Building recommendation systems requires careful planning and quick processing to handle live requests effectively. It's not just about creating a model but also about deploying it at scale.
  2. Contrastive learning is a powerful technique in machine learning that helps in improving model performance. New insights in this area can lead to better model training and application.
  3. Understanding different probability distributions is crucial in data science. It helps in modeling data accurately and predicting outcomes better.
Get a weekly roundup of the best Substack posts, by hacker news affinity:

Data Science Weekly - Issue 438

19 implied HN points • 14 Apr 22
🕹 Technology Data science Machine Learning Artificial Intelligence Data Engineering Visualization
  1. The Modern Data Stack is becoming crucial for handling data, with many tools available to improve the way businesses work with data. It helps users understand how to start using these tools effectively.
  2. DeepMind's AlphaFold is revolutionizing biology by accurately predicting protein shapes. This technology is changing how researchers approach biological problems.
  3. There are better ways to visualize SQL joins than using Venn diagrams. New methods like the checkered flag diagram can make understanding joins easier and clearer.

[in case you missed it] Data Science Weekly - Issue 437

19 implied HN points • 10 Apr 22
🕹 Technology Data science Machine Learning Artificial Intelligence Software Development Project management
  1. Distribution shift is a big challenge in machine learning. If we ignore how data changes in the real world, our models may fail.
  2. Tech apprenticeships are becoming more common and are a great way to learn while earning money. They help people start new careers in tech, even without a degree.
  3. There's ongoing research to give computers common sense. This could help AI understand the world better and make smarter decisions.

Data Science Weekly - Issue 437

19 implied HN points • 07 Apr 22
🕹 Technology Data science Machine Learning Artificial Intelligence Software Development Tech Conferences
  1. Data in the real world can change, and we need to think about that when we use machine learning. If we don't, our models may not work well when they are put to the test.
  2. Attending conferences can be a great way to learn and connect with others in the field. They often showcase new startups and many interesting themes that can inspire ideas.
  3. Tech apprenticeships are a rising opportunity. They allow you to earn while you learn skills for a technology career, making it accessible for more people.

Data Science Weekly - Issue 436

19 implied HN points • 31 Mar 22
🕹 Technology Data science Machine Learning Artificial Intelligence Data Engineering Automation
  1. Aggregating data can hide important details and context. It's better to focus on specific aspects of the data to find deeper insights.
  2. Waymo is testing fully autonomous vehicles in San Francisco. This effort aims to integrate self-driving technology into everyday life for its employees.
  3. AI can help improve representation on platforms like Wikipedia. A new approach is being developed to ensure more diverse biographies are created.

Data Science Weekly - Issue 435

19 implied HN points • 24 Mar 22
🕹 Technology Data science Machine Learning Artificial Intelligence Healthcare Technology Statistical Analysis
  1. Algorithmic assessments can help ensure that healthcare technology benefits everyone involved. It's important to evaluate how data is used in these systems.
  2. Relying solely on deep learning for electronic medical records may not be the best idea right now. Instead, better IT support is needed to improve healthcare systems.
  3. Many claims about explaining AI technology are misleading. Experts agree that what we currently call 'explainable AI' often falls short of being truly understandable.

Data Science Weekly - Issue 434

19 implied HN points • 17 Mar 22
🕹 Technology Data science Machine Learning Artificial Intelligence Software Development Statistics
  1. Understanding NLP is important. It involves tokenization and encoding, which helps to improve how machines understand language.
  2. Performance in deep learning can often feel random, but reasoning from first principles can help simplify the process. Focus on compute, memory, and overhead to improve performance.
  3. There is a growing need for data product managers as data teams modernize. These managers bridge the gap between data science insights and product development.

Data Science Weekly - Issue 433

19 implied HN points • 10 Mar 22
🕹 Technology Artificial Intelligence Data science Machine Learning Automation Software Engineering
  1. Deep learning is facing challenges, and experts are exploring what it needs to improve. It's important for AI to overcome these hurdles to progress further.
  2. MLOps, or machine learning operations, is currently complicated, but it's a growing field that promises future innovations. New tools and methods are emerging rapidly, making it tricky for newcomers to find their way.
  3. Visualizing data effectively is essential for making sense of complex information. Standards are being developed to help create better visuals, which makes it easier for everyone to understand data.

Data Science Weekly - Issue 432

19 implied HN points • 03 Mar 22
🕹 Technology AI Machine Learning Data science Software Engineering Natural Language Processing
  1. AI art has evolved quickly, becoming more relatable and controllable thanks to advancements in technology. Many people, even experts, are surprised by how realistic and detailed AI-generated images can now be.
  2. Conversational agents, like chatbots, are becoming more common and can serve different purposes, from casual chats to helping users complete specific tasks. However, understanding their impact on society is important as they become more integrated into daily life.
  3. The CX-ToM framework improves explainable AI by creating a dialogue between machines and humans for better understanding. This approach focuses on the intentions of both the user and the machine, making AI decisions clearer.

Data Science Weekly - Issue 431

19 implied HN points • 24 Feb 22
🕹 Technology Data science Machine Learning AI Statistics Visualization
  1. Vector databases are important for storing and searching data in various applications like image search and drug discovery.
  2. Statistics may not be the best path to becoming a data scientist; other fields could be more relevant and useful.
  3. Teaching and practicing reproducible workflows in data science helps ensure that research and findings can be verified and built upon.

Data Science Weekly - Issue 430

19 implied HN points • 17 Feb 22
🕹 Technology Data science Machine Learning AI Analytics Software
  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

19 implied HN points • 10 Feb 22
🕹 Technology Data science Machine Learning AI Programming 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.

Data Science Weekly - Issue 428

19 implied HN points • 03 Feb 22
🕹 Technology Data science Machine Learning AI Ethics Information Theory Programming
  1. Information Theory has evolved over time, influenced by technology and significant events like the space race, shaping its focus and impact across various fields.
  2. DeepMind's AlphaCode can compete in programming challenges, showing how AI can be developed to solve complex problems requiring a mix of skills.
  3. Understanding the concept of typicality is important in generative models, as it helps clarify issues with common methods like beam search and anomaly detection.

Data Science Weekly - Issue 427

19 implied HN points • 27 Jan 22
🕹 Technology Data science Machine Learning Artificial Intelligence Statistics Data Engineering
  1. Using offline replay experimentation can help predict results faster, cutting down the time usually needed for online experiments.
  2. Bad data can seriously affect business operations, and understanding how it breaks is crucial for fixing dashboards and reports.
  3. Shapley values can explain machine learning models by distributing how each feature contributes to predictions, making the model's decisions clearer.

Data Science Weekly - Issue 426

19 implied HN points • 20 Jan 22
🕹 Technology Data science Artificial Intelligence Machine Learning Software Development Programming
  1. Prospective learning is important because it focuses on preparing for future challenges instead of just learning from past experiences. This helps both humans and AI to adapt to new situations better.
  2. AI is set to change the field of medicine greatly, making things better for both doctors and patients by improving medical tools and approaches. But there are important ethical and technical issues to consider, like data fairness and bias.
  3. Using vectorization can speed up Python code significantly, but it's essential to understand what it means and when to apply it. This way, you can handle large sets of data more efficiently.

Data Science Weekly - Issue 425

19 implied HN points • 13 Jan 22
🕹 Technology AI Data science Machine Learning Analytics Ethics
  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

19 implied HN points • 06 Jan 22
🕹 Technology Data science Machine Learning Artificial Intelligence Software Development 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.

Data Science Weekly - Issue 423

19 implied HN points • 30 Dec 21
🕹 Technology Artificial Intelligence Machine Learning Data science Software Development Programming
  1. 2021 was a great year for AI research, with many new papers and breakthroughs that need to be understood and followed up on.
  2. Graph machine learning gained a lot of attention, and there are many new trends and advancements worth knowing about.
  3. There are many resources and tools available for learning data science and machine learning, including free courses and beginner-friendly tutorials.

Data Science Weekly - Issue 422

19 implied HN points • 23 Dec 21
🕹 Technology Data science Machine Learning Programming Artificial Intelligence Software Development
  1. Games can be made within spreadsheets like Excel or Google Sheets, making learning fun and interactive.
  2. Testing is an important part of a data scientist's job, and understanding how to do it can help improve analysis work.
  3. Understanding language can help in developing smarter machines, opening new paths for machine learning beyond just text processing.

Data Science Weekly - Issue 421

19 implied HN points • 16 Dec 21
🕹 Technology Data science Machine Learning AI Visualization Programming
  1. Lee Wilkinson made a big impact in the field of interactive visualization. His work helped people better understand and create statistical graphics.
  2. A new journal for machine learning research is starting, aiming for quick and fair reviews. This will help share cutting-edge research in a transparent way.
  3. Feature engineering is still important in machine learning, despite the rise of deep learning. It turns out that creating good features can really boost model performance.

Data Science Weekly - Issue 420

19 implied HN points • 09 Dec 21
🕹 Technology Data science Machine Learning Artificial Intelligence Data Visualization Open Source
  1. D3 is a powerful tool for data visualization that has lasted over a decade. Its success is attributed to its flexibility and the community support it receives.
  2. Building AI models like open-source software can make these models better and more collaborative. This means involving a wider community in their development.
  3. Automated decision-making systems can still reflect human biases, which shows that technology doesn't always solve fairness issues.

Data Science Weekly - Issue 419

19 implied HN points • 02 Dec 21
🕹 Technology Data science Machine Learning AI Cloud Computing Programming
  1. FluxML is teaming up with NumFOCUS to enhance open science and improve machine learning tools. This partnership will support new applications in areas like scientific machine learning and differentiable programming.
  2. There’s a fun 30-day challenge involving mapping that highlights the importance of community in data science. It celebrates collaboration and innovation in creating visual representations of data.
  3. AI is making strides in pure mathematics by helping uncover new patterns and insights. This collaboration between AI and mathematicians could lead to significant advancements in understanding complex mathematical concepts.

Data Science Weekly - Issue 418

19 implied HN points • 25 Nov 21
🕹 Technology Data science Machine Learning Artificial Intelligence Neural Networks Natural Language
  1. Understanding data strategy is crucial for companies. Many invest in data, but few create a data-driven culture.
  2. Deep learning can help with smart, autonomous systems, but caution is needed in safety-critical applications.
  3. Tools like Retool make it easier for teams to build applications on their data without needing extensive coding skills.

Data Science Weekly - Issue 417

19 implied HN points • 18 Nov 21
🕹 Technology Data science Machine Learning AI Programming Visualization
  1. Brains are like prediction machines which help save energy. They do this by predicting what they will perceive in the world around them.
  2. AI is being used to help scientists study chimpanzee behavior in the wild. It can find important clips in hours of footage much faster than humans.
  3. Different approaches to AI governance exist between the EU and the US. This may affect how they collaborate on AI in the future.

Data Science Weekly - Issue 416

19 implied HN points • 11 Nov 21
🕹 Technology Data science Machine Learning AI Visualization Computer Vision
  1. Mature machine learning systems can be tough to improve. Even with cutting-edge technology, you might find that new models don't perform better than old ones.
  2. Data drift and outlier detection are important for monitoring ML models. They help identify issues when you lack ground truth labels to compare against.
  3. Language models score how 'human' a sentence sounds. To train these models, you can analyze and convert language into probabilities.

Data Science Weekly - Issue 415

19 implied HN points • 04 Nov 21
🕹 Technology Data science Machine Learning Artificial Intelligence Programming Visualization
  1. Audio signal processing is important for machine learning projects that involve sound. To analyze sound effectively, you need to convert it into spectrograms first.
  2. Algorithmic efficiency in deep learning has improved greatly, requiring much less computing power than before. This means we can train complex neural networks faster and more efficiently now.
  3. Understanding Gaussian processes can be complicated, but looking at them in different ways can help. Each perspective gives new insights and makes the concept easier to grasp.

Data Science Weekly - Issue 414

19 implied HN points • 28 Oct 21
🕹 Technology Data science Machine Learning AI Software Development Data Analysis
  1. Machine learning can work with messy data. The key is to adapt techniques to handle things like missing values instead of spending all the time cleaning the data.
  2. Visualizations should be clear and focused. Good designs help people understand the information better by removing clutter and emphasizing main points.
  3. There are emerging tools and techniques that can speed up scientific discovery through faster machine learning methods. This helps researchers process data in real time and make new discoveries.

Data Science Weekly - Issue 413

19 implied HN points • 21 Oct 21
🕹 Technology Data science Machine Learning Artificial Intelligence Programming 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 412

19 implied HN points • 14 Oct 21
🕹 Technology AI Machine Learning Data science Statistics Software Engineering
  1. Machine learning is much more than just nonparametric statistics. It involves complex principles that go beyond what you learn in basic statistics.
  2. The State of AI Report 2021 highlights important areas like research, talent supply, industry applications, politics, and future predictions for AI. It's a comprehensive look at how AI is evolving.
  3. Self-supervised learning is becoming a major player in AI research. It allows models to learn from data without needing labeled examples, which can lead to significant advancements.

Data Science Weekly - Issue 411

19 implied HN points • 07 Oct 21
🕹 Technology Data science Machine Learning Artificial Intelligence Data Analytics Programming
  1. Freelancing in data visualization can be difficult, and learning from others' mistakes can help avoid similar pitfalls.
  2. Using AI to restore lost art, like Klimt's paintings, shows how technology can creatively bring the past back to life.
  3. Resource constraints in smaller organizations can complicate how machine learning is developed, highlighting the need for better support and understanding in the field.

Data Science Weekly - Issue 410

19 implied HN points • 30 Sep 21
🕹 Technology Data science Machine Learning Statistics AI
  1. When looking for a job in data science, different companies suit different career stages, so it’s important to evaluate what works best for you.
  2. Advanced techniques in weather prediction are being developed to predict rain within the next couple of hours, showing a real-life application of data science.
  3. The effectiveness of deep learning is facing challenges as researchers approach the limits of what can be achieved, raising concerns about future improvements.

Data Science Weekly - Issue 409

19 implied HN points • 23 Sep 21
🕹 Technology Data science Machine Learning Artificial Intelligence Computer Science Software Development 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 408

19 implied HN points • 16 Sep 21
🕹 Technology Data science Machine Learning Artificial Intelligence Software Development Programming
  1. Many PhD and Master students need to rethink their work habits formed by years of homework and tests. It's important to develop a more flexible approach to learning and working in data science.
  2. The quality of training data is crucial in machine learning. It's no longer just about crafting better models; having good data can be a game changer for performance.
  3. Effective marketing budget allocation can be informed by Media Mix Modeling. This helps companies understand which advertising channels yield the best results for customer acquisition.

Data Science Weekly - Issue 407

19 implied HN points • 09 Sep 21
🕹 Technology Machine Learning Data science Artificial Intelligence Computing Software Development
  1. Machine learning compilers help improve the efficiency of ML models, especially for edge computing, by addressing compatibility and performance issues.
  2. Scikit-learn, a popular machine learning library, has reached a significant version milestone at 1.0.0, showcasing its growth and community support since it started back in 2007.
  3. Synthetic data is becoming more important in computer vision, and using 3D content from the gaming and film industries can greatly enhance the process of creating such data.

Data Science Weekly - Issue 406

19 implied HN points • 02 Sep 21
🕹 Technology AI Machine Learning Data science Computing Ethics
  1. MIT has developed a smart carpet that can estimate human poses without using cameras, which might be useful for healthcare and smart home technologies.
  2. Google has introduced amazing AI technology that can enhance photos, making them look much more realistic than before.
  3. The financial machine learning space has a high failure rate, with many managers making critical mistakes; learning from these can lead to better success.

Data Science Weekly - Issue 405

19 implied HN points • 26 Aug 21
🕹 Technology Data science Machine Learning Artificial Intelligence Analytics Software Development
  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.