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 524

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.

Data Science Weekly - Issue 513

359 implied HN points • 21 Sep 23
🕹 Technology AI Data science Machine Learning Software Engineering Tech startups
  1. There's a new newsletter focusing on AI safety in China, showing that the country is more invested in AI safety than many think.
  2. A podcast discusses how startups can run better AI models without needing to upgrade their hardware—a big challenge in the field.
  3. An online event is coming up for those looking to secure data science jobs in big tech, focusing on interview strategies and market insights.

Data Science Weekly - Issue 537

139 implied HN points • 07 Mar 24
🕹 Technology Data science AI Machine Learning Data Engineering Data Visualization
  1. The newsletter shares valuable links about Data Science, AI, and Machine Learning each week. It's a great way to keep updated on the latest in the field.
  2. There are interesting articles highlighting statistical analyses and practical guides, like building GPU clusters at home. These resources help both beginners and experienced practitioners learn more.
  3. The newsletter also encourages people to participate in AI-related events and offers resources for job seekers. This can help you connect with others and grow your career.

Data Science Weekly - Issue 517

339 implied HN points • 19 Oct 23
🕹 Technology Data science AI Machine Learning Data Visualization Engineering
  1. Data science, AI, and ML are rapidly evolving fields, with new technologies and techniques emerging frequently. Staying updated through news and articles can help professionals keep their skills relevant.
  2. Fine-tuning large language models (LLMs) is a growing demand in the job market. Many companies are now looking for experience with LLMs alongside traditional skills like Python and SQL.
  3. Understanding different data visualization goals, like storytelling versus exploration, is important for effectively communicating data insights. This can improve how data is presented in reports and analyses.

Data Science Weekly - Issue 509

399 implied HN points • 25 Aug 23
🕹 Technology Data science Machine Learning Artificial Intelligence Data Engineering Data Visualization Software Development
  1. Each week, a newsletter shares important links and articles about data science, machine learning, and AI. It's a good way to keep updated on new happenings in the field.
  2. The newsletter features articles on various topics, including programming, AI forecasting, and data management practices. These articles are meant to help both newcomers and experienced professionals.
  3. Job listings and training resources are also provided, helping readers find opportunities and learn new skills beneficial for their careers in data science.
Get a weekly roundup of the best Substack posts, by hacker news affinity:

Data Science Weekly - Issue 514

339 implied HN points • 29 Sep 23
🕹 Technology Data science Machine Learning Artificial Intelligence Data Visualization Data Engineering
  1. Data science involves a mix of techniques for analyzing and visualizing data which can help make informed decisions.
  2. Learning about advanced customer segmentation methods can enhance how businesses understand and target their customers.
  3. There are various roles in data-related careers beyond just being a data scientist, so it's good to explore different paths.

Data Science Weekly - Issue 519

299 implied HN points • 03 Nov 23
🕹 Technology Data science AI Machine Learning Data Engineering Tech news Data Visualization
  1. Companies are increasingly sharing their advanced AI models openly, which can help them improve and build better products. This open sharing can lead to a more cooperative tech environment.
  2. Data science job applications are extremely competitive, with many positions receiving thousands of applicants within a day. This shows a high interest and demand in the data science field.
  3. Exploring advanced tools and frameworks in AI can be complex, but understanding how they work can help in building effective applications, especially in question-answering systems.

Data Science Weekly - Issue 522

259 implied HN points • 23 Nov 23
🕹 Technology Data science Artificial Intelligence Machine Learning Software Development Cloud Computing
  1. This newsletter shares weekly interesting links and updates in data science, AI, and machine learning. It's a great way to stay informed about new developments in these fields.
  2. There's a focus on practical tools and techniques for improving data science work, like using cloud processing for large datasets and methods for fine-tuning AI models effectively.
  3. The newsletter also highlights job opportunities and resources for those looking to enter or advance in the data science industry. It's beneficial for anyone looking to grow their career in this area.

Data Science Weekly - Issue 508

379 implied HN points • 18 Aug 23
🕹 Technology Data science Machine Learning Artificial Intelligence Data Visualization Data Engineering
  1. Writing clear and effective research papers is essential, and there are tips specifically for NLP papers that can help improve your writing skills.
  2. The job market for data-related roles has changed over the years, and analyzing hiring trends can provide insights into what skills and positions are in demand.
  3. Understanding AI hardware is important because it forms the backbone of many AI models, and knowing how it works can help in making better tech decisions.

Data Science Weekly - Issue 506

399 implied HN points • 04 Aug 23
🕹 Technology Data science Machine Learning Artificial Intelligence Robotics Data Engineering
  1. Integrating large language models into systems can be done using seven key patterns that balance performance and cost.
  2. Ethics in AI isn't just about explainability and fairness; we need a deeper understanding to prevent overall harm from AI systems.
  3. New approaches in robotics focus on current challenges and opportunities while advancing understanding of AI's role in planning tasks.

Data Science Weekly - Issue 516

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

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

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.

Data Science Weekly - Issue 512

299 implied HN points • 14 Sep 23
🕹 Technology Data science Artificial Intelligence Machine Learning Data Engineering Programming
  1. Nvidia has been a leader in AI technology, but its dominance might not last. Changes in the market and technology could shift the competitive landscape soon.
  2. For those who know R and want to learn Python, there are resources available to help make the transition easier. These resources provide advice and tips catered to R users.
  3. Reinforcement Learning with Human Feedback (RLHF) is an important part of training large language models. It's essential for improving how these models understand and respond to human preferences.

Data Science Weekly - Issue 520

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.

Data Science Weekly - Issue 510

279 implied HN points • 31 Aug 23
🕹 Technology Data science Machine Learning Artificial Intelligence Data Engineering Cloud Computing
  1. Autonomous drones can now race at human champion levels using deep reinforcement learning. This shows how advanced technology can mimic skilled human behavior in competitive sports.
  2. Google is rapidly developing its AI capabilities and plans to surpass GPT-4 by a significant margin soon. This could lead to more powerful AI tools for various applications.
  3. Reinforced Self-Training (ReST) is a new method for improving language models by aligning their outputs with human preferences. It offers better translation quality and can be done efficiently with less data.

Data Science Weekly - Issue 507

279 implied HN points • 11 Aug 23
🕹 Technology Data science Machine Learning Artificial Intelligence Data Engineering Big Data
  1. Large Language Models (LLMs) can take over some data tasks, but they won't replace all data jobs. Many tasks still need human insight and specialized skills.
  2. Understanding machine learning theory takes a long time, but in the industry, practical implementation is often more important. It's crucial to balance theory and hands-on skills.
  3. The new field of mechanistic interpretability is growing. Researchers are looking at how models learn and generalize, aiming to make sense of how AI works.

Data Science Weekly - Issue 502

319 implied HN points • 07 Jul 23
🕹 Technology Data science Machine Learning Artificial Intelligence Data Analytics Computing
  1. Generative design is making strides in drug discovery, but there are still challenges to address for better outcomes.
  2. The UK government is investing in a Foundation Model Taskforce to harness AI for societal benefits and safety.
  3. Keeping updated with developments in data science, such as new models and applications, is essential for professionals in the field.

Data Science Weekly - Issue 535

99 implied HN points • 23 Feb 24
🕹 Technology Data science Artificial Intelligence Machine Learning Software Engineering Data Engineering Statistical Analysis
  1. Scaling AI tools like ChatGPT involves overcoming many engineering challenges to handle large user demands. It's important to manage growth effectively to keep users satisfied.
  2. There's a lot of information out there about generative AI, making it hard to keep up. A guidebook can help condense this information and provide practical insights.
  3. Linear regression is still a valuable tool in data science. Sometimes going back to basics can yield better results than relying on complex models.

Data Science Weekly - Issue 491

419 implied HN points • 21 Apr 23
🕹 Technology Artificial Intelligence Machine Learning Data science Software Development Data Engineering
  1. AI academics are facing challenges keeping up with private sector investments. It's important for them to find survival strategies to remain competitive.
  2. There are ongoing discussions about the rapid progress in machine learning and how it can be overwhelming for developers. Many are sharing thoughts on adapting to this fast-paced change.
  3. Visualizing neural networks properly can help clarify concepts. There is a push for better diagrams to avoid confusion in understanding how these networks function.

Data Science Weekly - Issue 492

379 implied HN points • 28 Apr 23
🕹 Technology Data science Machine Learning Artificial Intelligence Data Visualization Software Engineering
  1. There is a new Slack community for paid subscribers focused on learning new tools and techniques in data science and career growth. It's a good place for support and sharing information.
  2. A/B testing is important for experiments and there are recommended resources to help design and run successful tests. Proper planning and communication are key to making A/B testing effective.
  3. Large Language Models (LLMs) are becoming more useful, and several resources are available for learning how to work with them. Understanding how they operate can help create valuable applications.

Data Science Weekly - Issue 484

439 implied HN points • 02 Mar 23
🕹 Technology Data science Machine Learning Artificial Intelligence Software Development Cloud Computing
  1. Data scientists need the right tools and environment to do their jobs effectively. Organizations can help by improving their data science infrastructure.
  2. Understanding how to choose and advocate for important metrics is vital for product teams. This can lead to significant growth in user engagement.
  3. A/B testing is crucial in fraud detection to compare models and determine their effectiveness. It can provide valuable insights that improve model performance.

Data Science Weekly - Issue 490

379 implied HN points • 13 Apr 23
🕹 Technology Data science Machine Learning Artificial Intelligence Data Engineering Software Development
  1. Data science is evolving quickly, and many new tools and techniques are being developed. This opens up exciting job opportunities in various fields like AI and machine learning.
  2. Using programming languages like R and SQL can extend beyond traditional data analysis. They can be powerful tools for creative applications in data science.
  3. Learning and implementing good practices in software development, such as automating tests and improving code efficiency, can save time and resources in data science projects.

Data Science Weekly - Issue 494

319 implied HN points • 12 May 23
🕹 Technology Data science Machine Learning Artificial Intelligence Data Visualization Engineering
  1. Open source AI is rapidly advancing, but may always lag behind the best quality models. It's great for innovation but has its limits.
  2. Many academic papers promise data sharing but often fail to deliver, which can hinder scientific research and verification.
  3. Understanding how to craft effective prompts is essential when using generative AI tools. This skill can greatly enhance the results you get from those tools.

Data Science Weekly - Issue 504

239 implied HN points • 21 Jul 23
🕹 Technology Data science Artificial Intelligence Machine Learning Data Engineering Data Visualization
  1. AI companies are complicated and must consider many factors like research, funding, and competition. Understanding these can help predict how they might evolve in the future.
  2. Debriefs, or team discussions after projects, can greatly boost team performance. They help everyone learn from experiences and improve future collaboration.
  3. New research shows that specific ingredient pairings in food can be explained by flavor networks. This indicates there are universal patterns in how different foods complement each other.

Data Science Weekly - Issue 493

319 implied HN points • 05 May 23
🕹 Technology Data science Machine Learning Artificial Intelligence Data Visualization Engineering
  1. Data scientists often lack key skills needed for the job, which can be frustrating for those hiring. It's important for data scientists to continually improve their skills and adapt to job requirements.
  2. There's a significant increase in data downtime and resolution times, signaling that overall data quality management needs improvement. Companies should focus on better data practices to enhance their operations.
  3. New programming languages, like Mojo, are emerging that aim to simplify coding and enhance user experience. These advancements can make programming more accessible and enjoyable for everyone.

Data Science Weekly - Issue 486

359 implied HN points • 17 Mar 23
🕹 Technology Data science Machine Learning Artificial Intelligence Data Engineering 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.

Data Science Weekly - Issue 565

1 HN point • 19 Sep 24
🕹 Technology Data science Artificial Intelligence Machine Learning Data Engineering Data Visualization
  1. Reading The Data Science Weekly is a great way to stay updated on AI and machine learning topics. It shares links, news, and resources that can help anyone interested in these fields.
  2. There are many useful techniques in data science, like the Hampel Filter for outlier detection, which can help improve data quality. Exploring these methods can really enhance your understanding and skills.
  3. Effective communication is crucial in data science. How you explain your findings can significantly impact your career, so it's important to work on your communication skills.

Data Science Weekly - Issue 503

219 implied HN points • 14 Jul 23
🕹 Technology Data science Machine Learning Artificial Intelligence Data Engineering Cloud Computing
  1. Machine learning is making its way into finance, and researchers are identifying practical uses for it. This can help finance professionals learn new tools and statisticians find interesting financial problems to solve.
  2. AI platforms, like social media, are becoming crucial in our lives but can be confusing and unreliable. People are figuring out how to use these platforms effectively despite their unpredictability.
  3. Large language models are changing how data scientists work. These models can automate many tasks, allowing data scientists to focus on managing and assessing the AI's outputs.

Data Science Weekly - Issue 496

259 implied HN points • 26 May 23
🕹 Technology Data science Machine Learning Artificial Intelligence Software Development Data Visualization
  1. AI has great potential to improve our lives but also comes with risks if misused. It's important to balance optimism and caution.
  2. Tools like Copilot in Power BI make it easier for users to analyze and visualize data by allowing them to communicate their needs in plain language.
  3. The concept of the 'Curse of Dimensionality' shows that sometimes having too much data can confuse models instead of helping them make better predictions.

Data Science Weekly - Issue 505

199 implied HN points • 28 Jul 23
🕹 Technology Data science Machine Learning Artificial Intelligence Data Engineering Data Visualization
  1. Large language models use complex methods like word vectors and transformers to understand language, but this can be explained simply without heavy math. They need a lot of data to perform well.
  2. Using AI tools like ChatGPT for real-world programming tasks can streamline the coding process, as it allows for a more focused workflow without switching between different resources.
  3. Building effective data storage systems, like Amazon S3, involves overcoming interesting challenges and nuances, demonstrating the amazing technology behind big data management.

Data Science Weekly - Issue 489

299 implied HN points • 06 Apr 23
🕹 Technology Data science Machine Learning Artificial Intelligence Data Visualization Data Engineering
  1. Understanding linear programming can help solve complex problems using Python. It's useful in various fields and can optimize outcomes.
  2. MLOps is closely related to data engineering, showing that managing data for machine learning involves more engineering than initially thought.
  3. The new pandas 2.0 version has exciting features like the Apache Arrow backend, which will enhance its performance and capabilities.

Data Science Weekly - Issue 485

319 implied HN points • 09 Mar 23
🕹 Technology Data science Machine Learning AI Data Engineering Data Visualization
  1. The newsletter shares interesting links about data science, machine learning, and AI each week. It’s a good way to keep up with new trends and knowledge in the field.
  2. There's a discussion on what databases should do but often don’t. Understanding these gaps can help you improve your data projects by knowing what to build yourself.
  3. AI's impact on jobs and industries is being researched, especially how language models like ChatGPT could change certain occupations. It's important to understand how AI can affect your career choices.

Data Science Weekly - Issue 500

219 implied HN points • 23 Jun 23
🕹 Technology Data science Artificial Intelligence Machine Learning Data Visualization Data Engineering
  1. AI technology is advancing quickly and can even cover public meetings, but we need to think carefully about its readiness for everyday use.
  2. Engineers can improve their people skills and interactions by applying the same problem-solving mindset they use in their technical work.
  3. Generative AI is becoming important in data science for creating synthetic data, which helps in privacy and enhances analysis without losing useful information.

Data Science Weekly - Issue 499

219 implied HN points • 16 Jun 23
🕹 Technology Data science Machine Learning Artificial Intelligence Data Engineering NLP
  1. Using large language models can help kids learn to ask curious questions by automating the teaching process.
  2. New techniques for 3D space reconstruction can make indoor views on platforms like Google Maps look more realistic and interactive.
  3. There's a growing need to understand the value of personal data in online shopping, especially as new regulations come into play.

Data Science Weekly - Issue 495

239 implied HN points • 19 May 23
🕹 Technology Data science Machine Learning Artificial Intelligence Natural Language Software Development
  1. Absence of evidence can often serve as strong evidence of absence, and this idea can be explored with Bayesian methods.
  2. Natural language processing is being used to analyze global supply chains, helping create networks from news articles.
  3. It's crucial to understand the unique challenges and opportunities in personalizing search results, as seen with Netflix's approach.

Data Science Weekly - Issue 498

219 implied HN points • 09 Jun 23
🕹 Technology Data science Machine Learning AI Big Data Data Visualization
  1. Data modeling in data science is complex and often messy, making it hard to get reliable answers. This issue highlights the need for better practices and understanding in this area.
  2. There are ongoing discussions about the realities of working in data science. Sharing these experiences can help others prepare for the challenges they may face.
  3. Generative AI is a big topic right now, and there are frameworks being developed to help organizations strategize its use effectively. Exploring these can guide businesses in adopting AI responsibly.

Data Science Weekly - Issue 488

279 implied HN points • 30 Mar 23
🕹 Technology Data science Machine Learning Artificial Intelligence Software Development Data Visualization
  1. This week's newsletter features discussions on AI and its potential risks, highlighting different viewpoints on the future of technology.
  2. Career development in data science is important. There are resources and talks from experts that focus on skills that help you succeed in this field.
  3. New updates in the Tidyverse can improve your coding experience in data science, making it easier and more efficient to work with data.

Data Science Weekly - Issue 501

179 implied HN points • 30 Jun 23
🕹 Technology Data science Machine Learning Artificial Intelligence Data Engineering Data Visualization
  1. Data scientists are sharing tips on how to make their scientific data more accessible and useful. This helps others to understand and use the data better.
  2. There are many discussions happening about the benefits and drawbacks of large language models (LLMs) like ChatGPT. Some people believe they are amazing, while others think they aren't very helpful.
  3. Naming things in programming can be tough, but there are resources and books that can help. Learning the right naming conventions can improve coding practices.

Data Science Weekly - Issue 497

199 implied HN points • 02 Jun 23
🕹 Technology Data science Machine Learning Artificial Intelligence Software Engineering Statistical Analysis
  1. Data drift doesn't always hurt model performance, so it's important to analyze the context before reacting to it.
  2. Work on solving bigger problems as you grow in your career, instead of waiting for difficult tasks to be handed to you.
  3. To improve a model's reasoning skills, reward it for each correct step in problem-solving, not just the final answer.