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 480

279 implied HN points • 02 Feb 23
🕹 Technology Data science Machine Learning AI Programming Big Data
  1. The newsletter is now hosted on Substack and remains free for everyone. A paid option is available for more features and interactions.
  2. Data teams need to build trust with stakeholders to effectively measure their value and justify their budgets. Having good relationships is more important than just metrics.
  3. Understanding MLOps is crucial for the industry. It involves not only the tools but also the culture and practices around machine learning operations.

Data Science Weekly - Issue 483

239 implied HN points • 23 Feb 23
🕹 Technology Data science Artificial Intelligence Machine Learning Data Visualization Programming
  1. The 2023 MAD landscape provides insights into machine learning and data trends. It has sections on the current market, infrastructure, and AI trends.
  2. A new tool called PyGWalker turns Pandas dataframes into easy-to-explore visual interfaces. It's great for beginners wanting to visualize their data without technical hassle.
  3. Cleaning data is essential for reliable research findings. New methods are being shared to improve and standardize the data cleaning process, making it more efficient.

Data Science Weekly - Issue 481

239 implied HN points • 09 Feb 23
🕹 Technology Data science AI Research Big Data Machine Learning Software Development
  1. Big Data is changing, and it's not as big a deal as we thought. Hardware is getting better faster than data sizes are growing.
  2. Research in AI can be learned just like a sport. It's about practicing skills like designing experiments and writing papers.
  3. Data Analytics can really help businesses understand their performance and make smarter decisions. It’s all about using data to solve problems and anticipate future issues.

Data Science Weekly - Issue 487

199 implied HN points • 23 Mar 23
🕹 Technology Data science Machine Learning AI Software Development Visualization
  1. This week's newsletter shares useful links in data science, machine learning, and AI. It's a great way to stay updated in these fields.
  2. One highlighted article discusses the importance of prompt engineering in interacting with language models. It's about how to communicate effectively with AI for desired results.
  3. There's also a report on how generative models like GPT might impact jobs. It shows that many workers could see changes in their tasks due to AI advancements.

What should I write about

39 implied HN points • 24 Feb 24
🚌 Education Tutorials Learning methods Content creation Skill development Teaching Strategies
  1. The writer plans to start creating tutorials again. It's a chance for people to learn more about various topics.
  2. They want feedback from subscribers about what specific topics they are interested in. This shows they value readers' opinions.
  3. There's a wide range of tools in data science, and the writer is keen to help navigate that complexity with useful content.
Get a weekly roundup of the best Substack posts, by hacker news affinity:

Data Science Weekly - Issue 482

199 implied HN points • 16 Feb 23
🕹 Technology Data science AI Tools Machine Learning Data Visualization Deep Learning
  1. Visual analytics can help make deep learning models easier to understand. Researchers are working to fill gaps and challenges in this area.
  2. AI tools like ChatGPT might change how we visualize data in the future. They could make it easier to find and interpret information quickly.
  3. A new method called Lion offers a better optimization algorithm for training deep neural networks. It uses less memory than existing methods like Adam.

Friday Finds - Bonus Links

19 implied HN points • 16 Feb 24
🕹 Technology AI Data science Programming Ecology Tutorials
  1. There are new tutorials available for those interested in AI and humanities. These tutorials aim to help people learn how to use AI tools effectively.
  2. The Leverhulme Programme is offering opportunities in ecological data science. This program is designed for doctoral training and focuses on important ecological research.
  3. A team is looking to hire a remote R programmer. They want someone to create an easy-to-use package for analyzing complex models in R.

Data Science Weekly - Issue 479

99 implied HN points • 27 Jan 23
🕹 Technology Data science Artificial Intelligence Machine Learning Programming Statistics
  1. Exploratory programming is important for data teams. It helps them find insights rather than just building software.
  2. Most datasets are not normally distributed, and there are many tests to check this but they can be tricky to use.
  3. AI is gaining a lot of attention, similar to what crypto once had. People are questioning if it can keep that interest alive.

Friday Finds - Bonus Links

19 implied HN points • 02 Feb 24
🕹 Technology AI Machine Learning Data science Software Innovation
  1. Paid subscribers get extra links and content. It's a nice way to say thank you for their support.
  2. There are interesting discussions on topics like AI and machine learning. These conversations help people learn more about the field.
  3. Links to simulations and insights about reality powered by AI are shared. They could spark curiosity and understanding about modern technology.

DSW Slack Group Invitation Link

19 implied HN points • 04 May 23
🕹 Technology Data science Software Development Machine Learning Artificial Intelligence Online Communities
  1. There's a Slack group for those who subscribe to Data Science Weekly. It's a great place to connect and learn together.
  2. The invite link for the Slack group is exclusive to paid subscribers, so make sure to keep it private.
  3. The group aims to help members interact, learn, and support each other in the field of data science.

Data Science Weekly - Issue 472

19 implied HN points • 08 Dec 22
🕹 Technology Data science Machine Learning Artificial Intelligence Software Development Data Engineering
  1. Machine learning can unintentionally develop biases from training data, which is important to detect and fix, especially in critical areas like healthcare and self-driving cars.
  2. Google Sheets now offers a way to use machine learning without coding skills, making it accessible for everyone to perform simple data tasks like predicting values and identifying anomalies.
  3. There is a trend in tech companies to make machine learning processes happen in real-time, which can lead to faster and more efficient data insights.

Data Science Weekly - Issue 471

19 implied HN points • 01 Dec 22
🕹 Technology Data science Machine Learning Artificial Intelligence Software Development Analytics
  1. MLOps is important for automating and managing machine learning products. It helps researchers and practitioners understand the principles and challenges of operating ML systems.
  2. Companies face trade-offs when transitioning to real-time machine learning pipelines. They must balance performance, cost, and infrastructure complexity to find the best solution.
  3. The FDA and other agencies have created guiding principles for using machine learning in medical devices. These principles aim to ensure the safety and effectiveness of AI/ML in healthcare.

Data Science Weekly - Issue 470

19 implied HN points • 24 Nov 22
🕹 Technology Data science Machine Learning Artificial Intelligence Data Engineering Software Development
  1. Using recommender systems can lead to problems like clickbait and addiction if they're only focused on engagement. We need to think differently to create better systems that really serve people's needs.
  2. GitLab has a detailed Data Team Handbook that explains how their data team works, what data is available, and how it helps different departments make decisions. This can guide other teams looking to improve their data processes.
  3. Deep learning techniques are being researched to playtest video games like Candy Crush. This shows how AI can create more human-like testing methods and improve the gaming experience.

Data Science Weekly - Issue 469

19 implied HN points • 17 Nov 22
🕹 Technology AI Machine Learning Data science Robotics Software Engineering
  1. Learning machine learning can be accomplished without an engineering background. It often requires hard work, perseverance, and adopting good software engineering practices.
  2. Robotics and AI are being increasingly used in fulfillment processes at companies like Amazon. These technologies face challenges but also provide innovative solutions for package handling.
  3. Large language models are evolving to act like agents that make decisions. This shift towards action-driven models may make them resemble artificial general intelligence (AGI) more closely.

Data Science Weekly - Issue 468

19 implied HN points • 10 Nov 22
🕹 Technology Data science Machine Learning Artificial Intelligence Analytics Visualization
  1. If you're thinking about leaving Twitter, it's a good idea to save your data first. You can use it to find trends and insights that might be really useful later.
  2. Learning command-line data analytics can make your data processing much easier. There's a new tool called SPyQL that makes it simpler to work with and understand data on the command line.
  3. Federated learning allows us to train models using data from many users without needing to see the actual data. This means we can protect privacy while still making progress in AI.

Data Science Weekly - Issue 467

19 implied HN points • 03 Nov 22
🕹 Technology Data science Artificial Intelligence UX Design Programming Machine Learning
  1. User experience (UX) is really important for startups using large language models. Many struggle because they focus on the wrong things instead of improving UX and product design.
  2. Data science notebooks have evolved a lot since they were first introduced. They are now essential tools in data science, and there’s an exciting future ahead for their development.
  3. OpenAI is financially supporting AI startups with a significant investment. They're offering early access to their systems to help these startups grow.

Data Science Weekly - Issue 466

19 implied HN points • 27 Oct 22
🕹 Technology Data science Machine Learning Artificial Intelligence Software Development Open Source
  1. Science education should focus on teaching scientific virtues first, rather than just tools and techniques. This approach helps students understand the core values of scientific inquiry.
  2. A data dictionary is essential for ensuring quality data collection and interpretation. It's best created before data collection to guide your research design.
  3. The Farama Foundation is aimed at improving open-source reinforcement learning by maintaining and standardizing existing libraries. This will help in developing more effective RL tools for the community.

Data Science Weekly - Issue 465

19 implied HN points • 20 Oct 22
🕹 Technology AI Data science Machine Learning Software Development Neuroscience
  1. AI writing assistants are helping indie authors write faster and come up with story ideas. Tools like Lex are changing how creatives approach their writing.
  2. Recent research shows that parts of the brain, like the hippocampus, work similarly to AI models known as transformers. This discovery helps us understand both artificial intelligence and human memory.
  3. The State of AI Report 2022 reviews important trends in AI, including technology breakthroughs, commercial applications, and safety concerns. It provides valuable insights for both researchers and industry leaders.

Data Science Weekly - Issue 464

19 implied HN points • 13 Oct 22
🕹 Technology Data science Artificial Intelligence Machine Learning Software Development Research Methods
  1. Building a community around R in the pharmaceutical industry can help users connect and share knowledge more effectively. It's important to identify who the users are and create a space for collaboration.
  2. Creating research ideas can start with understanding gaps in existing literature. By reading a single paper, you can learn frameworks to generate new ideas and improve your research quality.
  3. Data cleaning for machine learning models is crucial, starting from the ETL pipeline. It’s important to commit to high-quality data from the beginning to avoid common pitfalls that impact model accuracy.

Data Science Weekly - Issue 462

19 implied HN points • 29 Sep 22
🕹 Technology Data science Artificial Intelligence Machine Learning Programming Data Visualization
  1. Teaching students about scientific failure helps them build resilience. It prepares them for real-world challenges in research.
  2. Understanding uncertainty in deep learning models is crucial for effective use. It helps in making better predictions and decisions.
  3. Increasing data maturity in organizations leads to more strategic use of data. Assessing data maturity can guide teams in improving their data practices.

Data Science Weekly - Issue 461

19 implied HN points • 22 Sep 22
🕹 Technology Data science Machine Learning Natural Language Processing Artificial Intelligence Software Development
  1. Working in Natural Language Processing (NLP) involves keeping up with evolving models and figuring out how to effectively use data. It's still challenging for many to find practical applications for NLP.
  2. Generative AI has the potential to make workers significantly more efficient and creative. This could result in substantial economic value across various industries.
  3. Building trust in machine learning is crucial but challenging. It's important to address concerns about model reliability to maximize its business value.

Data Science Weekly - Issue 460

19 implied HN points • 15 Sep 22
🕹 Technology Data science Machine Learning Artificial Intelligence Software Engineering Data Visualization
  1. Soft skills are super important for data scientists. Being able to communicate well and work in a team can make a big difference in their effectiveness.
  2. There are great resources available online for learning data science, including live streams on platforms like Twitch. It’s a fun way to learn and engage with others.
  3. Use the right fonts and designs in data visualizations. They can greatly affect how your data is understood and appreciated.

Data Science Weekly - Issue 459

19 implied HN points • 08 Sep 22
🕹 Technology Data science Machine Learning Artificial Intelligence Software Engineering Analytics
  1. Organizations need to invest in creating better data to gain an advantage over competitors. Good data can drive value and improve decision-making.
  2. The activation layer of the modern data stack helps you use data in a more impactful way. This allows for personalized experiences rather than just viewing dashboards.
  3. Using standard formats like ONNX for model exporting makes your machine learning models more portable across different programming environments, reducing dependencies on specific languages.

Data Science Weekly - Issue 458

19 implied HN points • 01 Sep 22
🕹 Technology Artificial Intelligence Machine Learning Data science Deep Learning Analytics
  1. Machine learning best practices are shared in a guide from Google, helping those with some knowledge to improve their skills.
  2. There's skepticism about deep learning promises, as experts continue to predict big changes that haven't happened yet.
  3. AI is being used creatively, like generating art from Bible stories, which showcases the potential of technology in different fields.

Data Science Weekly - Issue 457

19 implied HN points • 25 Aug 22
🕹 Technology AI Data science Machine Learning Software Development Collaboration
  1. AI systems struggle with language limitations and won't fully replicate human thinking. This shows that our understanding of thought and language needs to evolve.
  2. Observable launched Free Teams to encourage more open collaboration in data science. It allows users to easily work together on projects and share insights for free.
  3. There is a problem in the data industry where roles are too narrowly defined, leading to a lack of collaboration. This makes it hard for teams to communicate and understand each other's work.

Data Science Weekly - Issue 456

19 implied HN points • 18 Aug 22
🕹 Technology Data science Machine Learning Artificial Intelligence Software Development Analytics
  1. Machine learning models need ongoing maintenance after they're deployed. The world changes, and so do the needs for the models.
  2. Using machine learning can make software testing more efficient, especially in complex applications like browsers.
  3. There are many resources available for people who want to get into machine learning and deep learning, including courses, videos, and discussions on best practices.

Data Science Weekly - Issue 455

19 implied HN points • 11 Aug 22
🕹 Technology Data science AI Machine Learning Data Engineering Algorithms
  1. Data professionals spend a lot of time checking data quality, which costs companies a lot of money every year. Poor data quality can affect a company's revenue significantly.
  2. Understanding how AI models behave is important for data scientists. They need to develop good mental models to train and work effectively with these systems.
  3. Vector search is becoming popular in retail for improving various aspects like revenue and customer satisfaction. It helps teams make better use of their data.

Data Science Weekly - Issue 454

19 implied HN points • 04 Aug 22
🕹 Technology Data science Machine Learning Artificial Intelligence Remote Sensing Data Visualization Big Data
  1. NASA is using machine learning to organize millions of astronaut photos of Earth. This technology helps scientists access and study these images more effectively.
  2. Data-driven companies can have a competitive edge in the market. The right expertise and data strategy can influence investors' decisions.
  3. There are many resources and discussions available online about using machine learning and data science effectively. Engaging with these can help keep skills and knowledge up to date.

Data Science Weekly - Issue 453

19 implied HN points • 28 Jul 22
🕹 Technology Data science Machine Learning Python AI Deep Learning
  1. Creating a focused GitHub repository can help others in the field, like those working with satellite images and deep learning.
  2. There are unique Python packages available that can enhance your data workflow, making tasks easier and more efficient.
  3. Understanding the technology behind AI and how to use it effectively is crucial for building better models and systems.

Data Science Weekly - Issue 452

19 implied HN points • 21 Jul 22
🕹 Technology Data science Machine Learning Artificial Intelligence Software Development Data Engineering
  1. The role of data scientist remains popular and well-paid, with growth expected in the field by 2029.
  2. Large language models (LLMs) are rapidly evolving and are becoming integral to various applications in our daily lives.
  3. Many industries are seeing the rise of domain experts who can now create and work with deep learning models without needing advanced degrees.

Data Science Weekly - Issue 451

19 implied HN points • 14 Jul 22
🕹 Technology Data science Artificial Intelligence Machine Learning Big Data Statistics
  1. Many people believe that data scientists today often do tasks very similar to data analysts. They're not just creating charts; there's a concern that their work lacks deeper statistical analysis.
  2. There's a lively debate about what it means to be a data scientist. While some argue the role has become too diluted, others believe that practical application in companies differs from academic definitions.
  3. Data science is evolving, with new techniques and applications emerging, like the importance of understanding datasets and using principles from various fields to improve intelligence in AI.

Data Science Weekly - Issue 450

19 implied HN points • 07 Jul 22
🕹 Technology Data science Machine Learning Artificial Intelligence Data Engineering Analytics
  1. AI forecasting contests help predict future progress and improve forecasting skills. It’s important to evaluate predictions against actual outcomes to see how accurate forecasters are.
  2. Analytics engineering has become a popular job choice, shifting from being less desired to highly sought after. This change reflects the growing need for skilled professionals in data analytics.
  3. High-quality machine translation is now possible for low-resource languages through models like NLLB-200. This will make information more accessible to speakers of these languages worldwide.

Data Science Weekly - Issue 449

19 implied HN points • 30 Jun 22
🕹 Technology Data science Machine Learning AI Ethics Data Analysis NLP
  1. Machine learning exercises can deepen your understanding of concepts like linear algebra and optimization. Practicing these can help you think critically about model building.
  2. Ethical AI development toolkits play a crucial role in shaping how companies approach ethics in technology. It's important to recognize the gaps between what these toolkits suggest and the real work involved in implementing ethical practices.
  3. Recent studies on adaptive optimizers show that models can go through phases of overfitting before suddenly generalizing very well. Understanding this 'grokking' phenomenon can help refine training processes for better performance.

Data Science Weekly - Issue 448

19 implied HN points • 23 Jun 22
🕹 Technology Data science Machine Learning Artificial Intelligence Software Development Data Engineering
  1. Machine learning can help the IRS process a huge amount of tax data more efficiently, improving enforcement actions on tax compliance.
  2. Denoising Diffusion Probabilistic Models are showing great success in generating images and audio, making them popular in creative AI applications like DALL-E 2.
  3. Training and developing skills in SQL can greatly enhance your data handling abilities, leading to better opportunities in data analysis and engineering.

Data Science Weekly - Issue 447

19 implied HN points • 16 Jun 22
🕹 Technology Artificial Intelligence Machine Learning Data science Software Development Natural Language Processing
  1. Natural language processing is getting better, but it's important to remember that it's just imitating consciousness, not actually having it.
  2. Scaling AI models may improve performance, but there are limits due to the quality of the data they learn from.
  3. Emerging techniques like optical neural networks are being developed to speed up image classification significantly.

Data Science Weekly - Issue 446

19 implied HN points • 09 Jun 22
🕹 Technology Data science Machine Learning Artificial Intelligence Software Development Programming
  1. The history of AI in literature shows how machines have been involved in writing since the 19th century. It's fascinating to see how far technology has come in helping with creative tasks.
  2. Jupyter Notebooks are versatile tools for data scientists, used for more than just coding. They can creatively combine text, visuals, and code to make data exploration easier.
  3. Using machine learning with small data sets can be tricky, but there are effective techniques to make it work. Smaller datasets can still yield valuable insights with the right approaches.

Data Science Weekly - Issue 445

19 implied HN points • 02 Jun 22
🕹 Technology AI Data science Machine Learning Ethics Research
  1. There's a new set of best practices for safely using large language models, aiming to help the industry work together responsibly.
  2. We are using less agricultural land now, even though we're producing more food, which is good for both us and nature.
  3. Qualitative research is important in AI. It helps us ask the right questions and understand how AI affects society beyond just numbers.

Data Science Weekly - Issue 444

19 implied HN points • 26 May 22
🕹 Technology Machine Learning Data science Artificial Intelligence Computer Vision Software Development
  1. Operationalizing machine learning models is important. There are key differences between how ML is used in research and in real-world applications, and understanding these can improve system design.
  2. DALL-E and similar AI models show that composition in AI can produce unexpected and enjoyable results. This is a fun way to think about how AI works with semantics, even if it doesn't always make sense.
  3. Data can sometimes lead to worse decisions. It's essential to think critically about how we use data rather than just relying on it blindly.

Data Science Weekly - Issue 443

19 implied HN points • 19 May 22
🕹 Technology Data science Artificial Intelligence Machine Learning Software Development Data Visualization
  1. Data scientists should improve their software development skills by learning about project structure, testing, reproducibility, and version control.
  2. AI-generated artwork may not be considered true art because it lacks the communication and consciousness involved in traditional art creation.
  3. Using optimized tools like DuckDB can enhance the data processing experience by making it faster and easier to work with large datasets.