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 564

119 implied HN points • 12 Sep 24

Understanding AI interpretability is important for building resilient systems. We need to focus on why interpretability matters and how it relates to AI's resilience.
Testing machine learning systems can be challenging, but starting with basic best practices like CI pipelines and E2E testing can help. This ensures the models work well in real-world scenarios.
Visualizing machine learning models is crucial for better understanding and analysis. Tools like Mycelium can help create clear visual representations of complex data structures.

Data Science Weekly - Issue 563

139 implied HN points • 05 Sep 24

🕹 Technology Data science Artificial Intelligence Machine Learning Data Engineering Data Visualization

AI prompt engineering is becoming more important, and experts share helpful tips on how to improve your skill in this area.
Researchers in AI should focus on making an impact through their work by creating open-source resources and better benchmarks.
Data quality is a common concern in many organizations, yet many leaders struggle to prioritize it properly and invest in solutions.

Data Science Weekly - Issue 562

179 implied HN points • 29 Aug 24

🕹 Technology Data science Artificial Intelligence Machine Learning Data Engineering Statistics

Distributed systems are changing a lot. This affects how we operate and program these systems, making them more secure and easier to manage.
Statistics are really important in everyday life, even if we don't see it. Talks this year aim to inspire students to understand and appreciate statistics better.
Understanding how AI models work internally is a growing field. Many AI systems are complex, and researchers want to learn how they make decisions and produce outputs.

Data Science Weekly - Issue 559

219 implied HN points • 08 Aug 24

🕹 Technology Data science AI Machine Learning Software Development Statistics

Camera calibration is crucial in sports analysis. It helps track players' movements accurately by mapping video frame positions to real field locations.
Understanding the context of data is important for responsible data work. Datasets need good documentation and stories to highlight their historical and social backgrounds.
There's a new, free encyclopedia for learning about cognitive science. It offers easy-to-read articles on various topics for students and researchers.

Data Science Weekly - Issue 561

139 implied HN points • 22 Aug 24

🕹 Technology Data science AI Machine Learning Data Engineering Visualization

When building web applications, using Postgres for data storage is a good default choice. It's reliable and widely used.
A new study shows that agents can learn useful skills without rewards or guidance. They can explore and develop abilities just from observing a goal.
The list of important books and resources in Bayesian statistics is being compiled. It's a way to recognize influential ideas in this field.

Get a weekly roundup of the best Substack posts, by hacker news affinity:

Data Science Weekly - Issue 558

219 implied HN points • 01 Aug 24

🕹 Technology Data science Machine Learning AI Data Visualization Statistical Methods

Data science and AI are rapidly evolving fields with plenty of interesting developments. Staying updated with the latest articles and news can really help you understand these changes better.
Effective communication is key in data science. Using intuitive methods and visuals can make complex concepts easier to grasp for everyone.
Using tools and methods like quantization can help make large models more accessible. It's important to find efficient ways to work with vast amounts of data to improve performance.

Data Science Weekly - Issue 560

139 implied HN points • 15 Aug 24

🕹 Technology Data science AI Machine Learning Software Development Programming

The Turing Test raises questions about what it means for a computer to think, suggesting that if a computer behaves like a human, we might consider it intelligent too.
Creating a multimodal language model involves understanding different components like transformers, attention mechanisms, and learning techniques, which are essential for advanced AI systems.
A recent study tested if astrologers can really analyze people's lives using astrology, addressing the ongoing debate about the legitimacy of astrology among the public.

Data Science Weekly - Issue 557

159 implied HN points • 25 Jul 24

🕹 Technology Data science AI Machine Learning Data Visualization Engineering

AI models can break down when trained on data that is generated by other models. This can cause problems in how well they work.
There is scientific research about the history of Italian filled pasta. It shows that most types likely came from a single area in northern Italy.
There are new resources and guides available for improving predictive modeling with tabular data. These can help you build better models by focusing on how data is represented.

Data Science Weekly - Issue 530

1418 implied HN points • 19 Jan 24

🕹 Technology Data science Machine Learning Artificial Intelligence Data Visualization Software Development

Good data visualization is important. Some types of graphs can be misleading, and it's better to avoid them.
In healthcare, it's not just about having advanced technology like AI. The real focus should be on getting effective results from these technologies.
Netflix released a lot of data about what people watched in 2023. Analyzing this can help us understand trends in streaming better.

Data Science Weekly - Issue 529

999 implied HN points • 12 Jan 24

🕹 Technology Data science Artificial Intelligence Machine Learning Data Engineering Software Development

Using ChatGPT can help you budget better. It can track and categorize your spending easily.
When coding, it's important to find a balance between moving quickly and keeping your code well-structured. This is a real challenge for many developers.
Language models, like GPT-4, are becoming very advanced, but there are big philosophical questions about what that really means for intelligence and understanding.

Data Science Weekly - Issue 527

959 implied HN points • 29 Dec 23

🕹 Technology Data science Machine Learning Artificial Intelligence Data Engineering Analytics

This week, there's a focus on using data science techniques for practical decision-making, highlighted by an interview with Steven Levitt, who discusses making tough choices using data.
There's a roundup of AI developments from 2023, showing how the field has evolved over the past year, which can help professionals stay updated.
Understanding data quality is essential, as it directly impacts how useful data is for decision-making and analysis in any organization.

Data Science Weekly - Issue 528

799 implied HN points • 05 Jan 24

🕹 Technology Data science AI Machine Learning Software Engineering Research

Data Science Weekly shares curated news and articles each week related to data science, AI, and machine learning. This helps readers stay updated on important trends and topics.
Deepnote emphasizes using its own platform for building data infrastructure, showcasing how versatile tools can simplify data tasks. It highlights the importance of a universal computational medium.
A reliable A/B testing system is essential for businesses to make informed decisions and optimize performance. Companies that use effective experimentation platforms can significantly improve their outcomes and reduce manual work.

Data Science Weekly - Issue 554

119 implied HN points • 04 Jul 24

🕹 Technology Data science AI Machine Learning Data Engineering Visualization

Staying updated in data science, AI, and machine learning is essential for improving skills and knowledge. Weekly newsletters provide curated articles and resources that help you keep up with the latest trends.
Effective structuring of data science teams can greatly enhance productivity. Learning from past experiences on team reorganizations can help in clarifying roles and increasing effectiveness.
Building interactive dashboards in Python can make data more accessible. Using tools like PostgreSQL and specific libraries can simplify the process and enhance data visualization.

Data Science Weekly - Issue 550

179 implied HN points • 07 Jun 24

🕹 Technology Data science AI Machine Learning Computing Data Engineering

Curiosity in data science is important. It's essential to critically assess the quality and reliability of the data and models we use, especially when making claims about complex issues like COVID-19.
New fields, like neural systems understanding, are blending different disciplines to explore complex questions. This approach can help unravel how understanding works in both humans and machines.
Understanding AI advancements requires keeping track of evolving resources. It’s helpful to have a well-organized guide to the latest in AI learning resources as the field grows rapidly.

Data Science Weekly - Issue 555

99 implied HN points • 11 Jul 24

🕹 Technology Data science AI Machine Learning Data Engineering Data Visualization

Large language models can sometimes create false or confusing information, a problem known as hallucination. Understanding the cause of these mistakes can help improve their accuracy.
Good data visualizations are important to effectively communicate patterns and insights. Poorly designed visuals can lead to misunderstandings, especially among those not familiar with graphics.
There's an ongoing debate about copyright in the context of generative AI. Many believe it would be better to focus on finding compromises rather than pursuing strict legal battles.

Data Science Weekly - Issue 551

159 implied HN points • 13 Jun 24

🕹 Technology Data science AI Machine Learning Software Development Computer Science

Data Science Weekly shares curated articles and resources related to Data Science, AI, and Machine Learning each week. It's a helpful way to stay updated in the field.
There are various interesting projects mentioned, such as the exploration of Bayesian education and improving code completion for languages like Rust. These projects can help in learning and improving skills.
Free passes to an upcoming AI conference in Las Vegas are available, offering a chance to network and learn from industry leaders. It's a great opportunity for anyone interested in AI.

Data Science Weekly - Issue 552

139 implied HN points • 20 Jun 24

🕹 Technology Data science Artificial Intelligence Machine Learning Data Engineering Data Visualization

Notebooks can be easy to use, but they might make you lazy in coding. It's important to follow good practices even when using them.
When handling large datasets, it's crucial to learn how to scale effectively. Knowing how to use resources wisely can help you reach your goals faster.
Retrieval Augmented Generation (RAG) can improve how models generate information. It's complex, but understanding it can boost the performance of your projects.

Data Science Weekly - Issue 556

79 implied HN points • 18 Jul 24

🕹 Technology Data science Artificial Intelligence Machine Learning Programming Data Engineering

AI research in China is progressing rapidly, but it hasn't received much attention compared to developments in the US. There are many complexities in understanding the implications of this advancement.
There are new methods to improve large language models (LLMs) using production data, which can enhance their performance over time. A structured approach to analyzing data quality can lead to better outcomes.
Evaluating modern machine learning models can be challenging, leading to some questionable research practices. It's important to understand these issues to ensure more accurate and reproducible results.

Data Science Weekly - Issue 549

159 implied HN points • 31 May 24

🕹 Technology Data science Artificial Intelligence Machine Learning Data Engineering Cloud Computing Software Development

Mediocre machine learning can be very risky for businesses, as it may lead to significant financial losses. Companies need to ensure their ML products are reliable and efficient.
Understanding logistic regression can be made easier by using predicted probabilities. This approach helps in clearly presenting data analysis results, especially to those who may not be familiar with technical terms.
Data quality management is becoming essential in today's data-driven world. It's important to keep track of how data is tested and monitored to maintain trust and accuracy in business decisions.

Data Science Weekly - Issue 553

99 implied HN points • 27 Jun 24

🕹 Technology Data science Artificial Intelligence Machine Learning Data Engineering Data Visualization

Data visualization can show important patterns, like changes in night and daylight globally. Understanding these trends helps us appreciate our environment better.
In AI engineering, simplifying data preparation is crucial. Many new AI applications can be built without structured data, which might lead to rushed expectations about their effectiveness.
Aquaculture technology is evolving with better methods to track and analyze fish behavior. New approaches like deep learning are making monitoring more accurate and efficient.

Data Science Weekly - Issue 547

179 implied HN points • 17 May 24

🕹 Technology Data science AI Machine Learning Data Visualization Software Development

Learning Rust programming can be made easy with exercises designed for beginners, even if you know another language already. You’ll work through small tasks to build confidence.
Data scientists need to learn how to work with databases to scale their analytics. Many face challenges when transitioning to this part of their work.
There are helpful tools, like Data Wrangler for VS Code, that simplify data cleaning and analysis. These tools help generate code automatically as you work with your data.

Data Science Weekly - Issue 541

279 implied HN points • 05 Apr 24

🕹 Technology Data science AI Machine Learning Software Development Data Engineering

AI agents have unique challenges that traditional laws may not effectively solve. New rules and systems are needed to ensure they are managed properly.
JS-Torch is a new JavaScript library that makes deep learning easier for developers familiar with PyTorch. It allows building and training neural networks directly in the browser.
Data acquisition is crucial for AI start-ups to succeed. There are strategies outlined to help these businesses gather the right data efficiently.

Data Science Weekly - Issue 543

219 implied HN points • 19 Apr 24

🕹 Technology Data science Machine Learning AI Analytics Data Engineering

Statistical ideas have a big impact on the world. Learning about important papers can help us understand how statistics shape modern research and decision-making.
Machine Learning teams have different roles that face unique challenges. Understanding these personas can help leaders support their teams better.
Using vector embeddings can greatly improve search experiences in apps. They simplify processes that previously seemed too complex and highlight their usefulness in technology.

Data Science Weekly - Issue 548

139 implied HN points • 24 May 24

🕹 Technology Data science Machine Learning Artificial Intelligence Data Visualization Data Engineering

Good communication is key for statisticians to explain their complex work to non-experts. Finding ways to relate data to everyday situations can make it easier for others to understand.
Using histograms can speed up the training process for gradient boosted machines in data science. This simple technique can improve efficiency significantly.
There are efforts to use machine learning algorithms to detect type 1 diabetes in children earlier. This can help avoid serious health issues by improving recognition of symptoms.

Data Science Weekly - Issue 539

259 implied HN points • 22 Mar 24

🕹 Technology Data science AI Machine Learning Data Engineering Data Visualization

Data storytelling is important for sharing insights, and AI can help people create better stories. The research looks at how different tools assist in each storytelling stage.
Switching from R to Python in data science isn't just about learning new syntax; it's a mindset change. New Python tools can help make this transition smoother for users coming from R's tidyverse.
Emerging technologies often face skepticism, as seen throughout history. New inventions have raised concerns about their impact, but they eventually become part of everyday life.

Data Science Weekly - Issue 532

379 implied HN points • 02 Feb 24

🕹 Technology Data science Machine Learning Artificial Intelligence Data Engineering Data Visualization

Forecasting in data science is challenging because time series data can be non-stationary. Using the right evaluation methods can help bridge the gap between traditional and modern forecasting techniques.
It's important to consider the smartness of your data structures. Creating overly complicated dashboards that ultimately just produce simple outputs may not be the best use of time.
There are clear distinctions between well-built data pipelines and amateur setups. Understanding what makes a pipeline production-grade can improve the quality and reliability of data processing.

Data Science Weekly - Issue 533

339 implied HN points • 09 Feb 24

🕹 Technology Data science Machine Learning Artificial Intelligence AI Research Data Engineering

Satellite data is important for machine learning and should be treated as a unique area of research. Recognizing this can help improve how we use this data.
Many data science and machine learning projects fail from the start due to common mistakes. Learning from past experiences can help increase the chances of success.
Open source software plays a crucial role in advancing AI technology. It's important to support and protect open source AI from regulations that could harm its progress.

Data Science Weekly - Issue 544

159 implied HN points • 26 Apr 24

🕹 Technology Data science Machine Learning Artificial Intelligence Data Engineering Data Visualization

Evaluating AI models can be expensive, but tools like lm-buddy and Prometheus help do it on cheaper hardware without high costs.
Installing and deploying LLaMA 3 is made simple with clear guides that cover everything from setup to scaling effectively.
Understanding best practices in machine learning is essential, and resources like the 'Rules of Machine Learning' provide valuable guidelines for beginners.

Data Science Weekly - Issue 526

419 implied HN points • 22 Dec 23

🕹 Technology Data science AI Machine Learning Analytics Software Development

Generative AI is changing how we work with tools, improving the Human-Tool Interface. This can help us use technology in ways we never could before.
Support Vector Machines (SVMs) can be very effective for prediction tasks, often outperforming other models in error rates. However, they aren’t as commonly used, possibly due to their complexity.
Deep multimodal fusion is useful in surgical training. It helps classify feedback from experienced surgeons to trainees by combining different types of data like text, audio, and video.

Data Science Weekly - Issue 545

139 implied HN points • 03 May 24

🕹 Technology Data science Artificial Intelligence Machine Learning Software Development Big Data

Reusing data analysis work can save time and help teams focus on building new capabilities instead of just repeating old ones.
Open-source models can be a better choice than proprietary ones for developing AI applications, making them cheaper and faster.
Causal machine learning helps predict treatment outcomes by personalizing clinical decisions based on individual patient data.

Data Science Weekly - Issue 546

119 implied HN points • 10 May 24

🕹 Technology Data science AI Machine Learning Data Engineering Data Visualization

Time-series analysis and Gaussian processes are powerful tools for interpreting data. They allow for flexibility and control in modeling data, making them essential for data practitioners.
Understanding A/B testing is crucial for making informed business decisions. Using a reliable experimentation system can save time and lead to better results.
New advancements in AI and data science are enhancing applications in various fields, like biomedical research and recommendation systems. These innovations help combine human creativity with machine learning capabilities.

Data Science Weekly - Issue 540

179 implied HN points • 29 Mar 24

🕹 Technology Data science AI Machine Learning Data Engineering Automation

SQL is seen as an easier way to write relational algebra, but it's not ideal for building new query tools. Understanding its limits can help in learning and using SQL better.
Many successful companies have developed their own AI models, showing a trend in the tech industry. Knowing about these companies can give insights into future developments in AI.
Binary vector search methods can save a lot of memory compared to traditional methods. However, it's important to balance memory savings with maintaining accuracy.

Data Science Weekly - Issue 538

199 implied HN points • 14 Mar 24

🕹 Technology Data science Machine Learning Artificial Intelligence Data Engineering Cloud Computing

Serverless computing can handle big tasks without limits, but it also brings challenges like managing large uploads effectively.
Art careers can be influenced by the reputation of institutions, with established artists facing less access to elite spaces early on compared to newcomers.
Learning about LLM evaluation metrics can help improve understanding and performance when working with large language models.

Data Science Weekly - Issue 525

359 implied HN points • 15 Dec 23

🕹 Technology Data science AI Machine Learning Data Engineering Data Privacy

Learning about causal models is important in data analysis because it helps explain what caused the data. This understanding can improve how we interpret results using Bayesian methods.
There's growing concern over data privacy in AI tools like Dropbox. Users are worried their private files could be used for AI training, even though companies deny this.
Netflix recently held a Data Engineering Forum to share best practices. They discussed ways to improve data pipelines and processing, which could benefit many in the data engineering community.

Data Science Weekly - Issue 542

139 implied HN points • 12 Apr 24

🕹 Technology Data science AI Machine Learning Programming Analytics

This newsletter provides links and updates about data science, AI, and machine learning. It's a helpful resource for anyone wanting to stay informed in this field.
One article teaches how to handle real questions using Python, which is great for people wanting practical coding skills. Another discusses techniques to make sure AI outputs stay on task.
The newsletter also features resources and courses to help people learn and improve their skills in data science and related areas. It's a good place to find learning opportunities.

Data Science Weekly - Issue 523

339 implied HN points • 01 Dec 23

🕹 Technology Data science Machine Learning Artificial Intelligence Data Analysis Software Development

Data science is evolving quickly, and it's important to stay updated with new advances and tools. Courses and reading lists can help you catch up and enhance your skills.
Using machine learning to solve real-world problems, like correctly attributing quotes, shows the practical applications of data science. Collaboration between universities and organizations can lead to innovative solutions.
The job market for data scientists is challenging right now. Many applicants are competing for limited positions, so if you're looking for a job, patience is key.

Data Science Weekly - Issue 536

179 implied HN points • 01 Mar 24

🕹 Technology Data science AI Machine Learning Programming Statistics

The DSPy framework makes working with large language models easier by focusing on programming instead of complex prompting techniques. This helps reduce errors and improves usability.
A new sequence model approach shows better performance than traditional Transformers, especially for long data sequences. It also works faster, making it a promising development in the field.
Learning resources like online courses and free books on deep learning and causal ML can help deepen understanding of data science. They provide structured material that is great for both beginners and advanced learners.

Data Science Weekly - Issue 521

339 implied HN points • 17 Nov 23

🕹 Technology Data science Machine Learning AI Programming Analytics

JAX is becoming popular for its speed and capabilities, and learning it may be essential for those familiar with PyTorch. It does have a steeper learning curve, but there are resources to help ease the transition.
The demand for GPUs is skyrocketing, driven by various market factors. Understanding these dynamics can help anticipate the future of technology and resource availability in industries reliant on powerful computing.
Freelancing in data science can lead to an overwhelming number of job offers. Tips on finding clients on platforms like Upwork and LinkedIn can help navigate this new freelance landscape.

Data Science Weekly - Issue 518

379 implied HN points • 27 Oct 23

🕹 Technology Data science Machine Learning Artificial Intelligence Programming Data Engineering

Web development is evolving with the use of local models and technologies for building applications, moving beyond just Python-based machine learning.
It's becoming increasingly important for developers to understand GPUs since they're widely used in deep learning and can greatly enhance performance.
Companies are exploring various use cases for generative AI that provide real value, focusing on practical implementations that drive return on investment.

Data Science Weekly - Issue 531

219 implied HN points • 26 Jan 24

🕹 Technology Data science Artificial Intelligence Machine Learning Data Engineering Data Visualization

AI often gets criticized for the quality of its output, but that might not be the real issue people have with it. If quality is fixed, the conversation about AI could change significantly.
Common sense is tricky to define and measure, but researchers are developing ways to quantify it both individually and collectively. This could help clarify how we understand common sense in different contexts.
Large language models (LLMs) can transform education by encouraging hands-on learning. They offer opportunities for more interactive and engaging learning experiences.