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 404

19 implied HN points • 19 Aug 21
🕹 Technology AI Machine Learning Data science Software Development Programming
  1. Foundation models in AI are powerful tools that can be used for various tasks like language and vision, but they come with risks like misuse and ethical concerns.
  2. Causal inference helps us understand the effects of actions in data and can be applied in tech industries to personalize services and improve decision making.
  3. MLOps focuses on effectively implementing machine learning in real-world applications, bridging the gap between traditional computing and machine learning challenges.

Data Science Weekly - Issue 403

19 implied HN points • 12 Aug 21
🕹 Technology Machine Learning Artificial Intelligence Data science Big Data Software Development
  1. Be careful with machine learning! There are common mistakes that researchers make. It's important to build models carefully and evaluate them properly.
  2. A court in Australia has decided that AI can be considered an inventor. This is a big change in how we think about inventions and who gets credit for them.
  3. Natural Language Understanding (NLU) with just big data might not work as well as we think. It's time to rethink how we approach this challenge.

Data Science Weekly - Issue 402

19 implied HN points • 05 Aug 21
🕹 Technology Data science Machine Learning Open Source Data Visualization AI Ethics
  1. Visualizing your code can help you understand its structure easily. It's a useful way to see what's happening in a GitHub repository at a glance.
  2. AI ethics should be understood by everyone in an organization, not just data scientists. This awareness can help prevent risks and guide better decisions.
  3. If you want to build a successful AI project, learn from those who have done it. They often share important lessons that can help others achieve similar success.

Data Science Weekly - Issue 401

19 implied HN points • 29 Jul 21
🕹 Technology Data science Machine Learning Artificial Intelligence Programming Statistics
  1. Open-ended play can help train AI agents to perform well on different tasks without needing direct human input. This means they can learn and adapt quickly to new challenges.
  2. Time-weighted averages are useful for getting accurate averages from data that isn't collected on a regular schedule. They help in making sense of messy time-series data.
  3. Triton is a new programming tool that makes it easier for researchers to write efficient GPU code, allowing even those without deep technical skills to optimize their computations effectively.

Data Science Weekly - Issue 400

19 implied HN points • 22 Jul 21
🕹 Technology Data science Machine Learning Artificial Intelligence Software Development Data Analysis
  1. Deepfake technology raises ethical questions about the use of AI-generated content without disclosure, as seen in the documentary about Anthony Bourdain.
  2. The way we use data is changing. A modern cloud data stack is becoming essential for building new businesses and improving access to data.
  3. GitHub Copilot is transforming coding by generating code automatically, making it feel like a magical assistant, though some users are still figuring out how to best use it.
Get a weekly roundup of the best Substack posts, by hacker news affinity:

Data Science Weekly - Issue 399

19 implied HN points • 15 Jul 21
🕹 Technology Data science Machine Learning AI Privacy Automation
  1. Data for good initiatives aim to use data positively but often face disconnects. It's important to understand what these initiatives do and how they differ from one another.
  2. Peer reviews in data science can improve project outcomes, but they may not go as planned in real situations. Learning from what works and what doesn’t is key to improving the process.
  3. Amazon collects a lot of user data through various services, which many people might not be aware of. Understanding privacy policies is important to know how your data is used.

Data Science Weekly - Issue 398

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

Data Science Weekly - Issue 397

19 implied HN points • 01 Jul 21
🕹 Technology AI Data science Machine Learning Art Software
  1. AI-generated art is gaining popularity, allowing artists to create visuals by simply using text prompts. This makes art creation more accessible and experimental.
  2. Understanding and mitigating biases in AI is crucial for developers. There's a focus on practical steps to limit biases during various stages of AI development.
  3. Preparing for machine learning job interviews can be simplified with resources that outline essential skills, questions, and the overall interview process. This helps candidates present themselves better.

Data Science Weekly - Issue 396

19 implied HN points • 24 Jun 21
🕹 Technology Data science Machine Learning Artificial Intelligence Software Development Cloud Computing
  1. Multi-task learning helps models make several predictions at once, making them smarter. It's better than sticking to just one task.
  2. Deep reinforcement learning is changing how industries like manufacturing work by teaching machines to take actions to achieve specific goals. This can really improve efficiency.
  3. The Netflix Prize taught Netflix valuable lessons, even if the main winning entry wasn't directly useful. It's a good reminder that competitions can offer more benefits than just the final prize.

Data Science Weekly - Issue 395

19 implied HN points • 17 Jun 21
🕹 Technology Data science Machine Learning Statistics Artificial Intelligence Programming
  1. TinyML is a growing field that covers small, efficient machine learning models. It's useful for projects where computing power is limited.
  2. Understanding Bayesian statistics can help tackle complex decision-making problems. Engaging with experts in the field can deepen your insights.
  3. Choosing the right tool for data processing is important. Tools like Dask and Vaex serve different purposes, so knowing when to use each is key.

Data Science Weekly - Issue 394

19 implied HN points • 10 Jun 21
🕹 Technology Data science AI Machine Learning Privacy Automation
  1. The data economy often harms our privacy as companies gather personal information for profit. It's important to think about how our data is used.
  2. New AI technologies, like deep reinforcement learning, can improve tasks like chip design significantly faster than traditional methods. This shows how AI can change engineering jobs.
  3. Data monitoring is crucial for machine learning applications. It helps ensure that models perform well and meet the needs of companies.

Data Science Weekly - Issue 393

19 implied HN points • 03 Jun 21
🕹 Technology Data science Machine Learning Algorithms Visualization Artificial Intelligence
  1. Generating coherent noise using Fourier transforms can create impressive 3D terrain effects. It's interesting to see how a complex math concept can produce realistic visuals.
  2. Deepfake technology can alter maps, which raises concerns about misinformation. It's a reminder to be cautious about what we see online.
  3. Learning data science should start with foundational knowledge, not just jumping into deep learning. Understanding basic concepts is key to building effective models.

Data Science Weekly - Issue 392

19 implied HN points • 27 May 21
🕹 Technology Data science Machine Learning Artificial Intelligence Software Development Data Analysis
  1. Archaeologists are using a neural network to help sort pottery fragments. This combines tech and human expertise to improve artifact classification.
  2. JavaScript is now favored for data analysis on the web. It allows for easier collaboration and better communication of insights.
  3. Companies are focusing on AI compliance and risk management. There's a growing need for legal support to handle AI-related challenges.

Data Science Weekly - Issue 391

19 implied HN points • 20 May 21
🕹 Technology Data science Machine Learning AI Software Engineering Automation
  1. Major League Baseball is testing an automated ball and strike calling system to help umpires make faster and more accurate calls during games.
  2. Twitter has updated its image cropping algorithm to be fairer and more equitable in how it represents different images to users.
  3. Reinforcement learning is gaining interest among big companies, but it's still a developing area compared to other machine learning techniques.

Data Science Weekly - Issue 390

19 implied HN points • 13 May 21
🕹 Technology Data science Machine Learning AI Software Engineering Statistical Analysis
  1. A crossword-solving AI named Dr. Fill has shown that machines can solve puzzles like humans, but humans still have their unique strengths.
  2. The concept of 'trees' in biology is more complex, as many plants we call trees don't fit a simple definition, mixing in non-trees in their evolutionary history.
  3. Advancements in synthetic data generation allow for the creation of realistic images, making it useful for training models even when real data is scarce.

Data Science Weekly - Issue 389

19 implied HN points • 06 May 21
🕹 Technology Data science AI Machine Learning Visualization Ethics
  1. The San Pellegrino label creates a wavy pattern called the Moiré effect. It happens when two repeating patterns overlap in a way that makes them look interesting and dynamic.
  2. AI in healthcare is changing how we make medical decisions, but it's also raising important moral questions. These include concerns about losing the role of doctors and the potential for bias in AI systems.
  3. Observable Plot is a new tool that helps visualize data better and easier. It's built on D3 and is designed for those who want a smoother experience in exploring data.

Data Science Weekly - Issue 388

19 implied HN points • 29 Apr 21
🕹 Technology Data science Machine Learning AI Visualization Statistics
  1. Cluster analysis can help identify groups in data, but knowing how many clusters to use is often tricky. A new method called a clustergram provides a better view of how observations flow between classes as you add more clusters.
  2. Bayesian and frequentist methods provide different types of statistical results that can't be directly compared. Each method answers different questions, so understanding their unique outputs is important.
  3. Netflix is tackling decision fatigue by developing a feature that automatically plays a show or movie when users open the app. This change aims to simplify the user experience.

Data Science Weekly - Issue 387

19 implied HN points • 22 Apr 21
🕹 Technology Data science Machine Learning Artificial Intelligence Environmental Impact
  1. Goodreads is a huge platform for readers where they discuss what makes a book a 'classic.' It shows how engaging with books online can shape opinions and communities.
  2. Scientists are using AI to decode whale language, which could help us understand more about these intelligent creatures and their communication.
  3. Neural networks are getting better at solving complex math problems quickly, making it easier to model complicated systems in science and engineering.

Data Science Weekly - Issue 386

19 implied HN points • 15 Apr 21
🕹 Technology Data science Machine Learning Artificial Intelligence Data Visualization Programming
  1. Accessibility in data visualization is important. Tools like Chartability help ensure that everyone can understand data, especially people with disabilities.
  2. Graph Neural Networks (GNNs) are a powerful tool for analyzing data, but their effectiveness can vary depending on how they use features and edges.
  3. There's a growing need for data observability. Companies must ensure data quality and avoid issues like missing or duplicate data as they handle more complex data pipelines.

Data Science Weekly - Issue 385

19 implied HN points • 08 Apr 21
🕹 Technology Data science Machine Learning Artificial Intelligence Data Analysis Automation
  1. Building a machine learning rig can be a fun project. It involves planning and buying the right hardware, especially GPUs.
  2. Data observability is crucial for businesses using large data sets. It helps ensure data quality and reduces issues in complex data pipelines.
  3. Using deep learning and automation can simplify tasks like monitoring bird nests. This can save time and keep track of nature without constant watching.

Data Science Weekly - Issue 384

19 implied HN points • 01 Apr 21
🕹 Technology Data science Machine Learning AI Ethics Optimization Visualization
  1. Maps are getting smarter with AI, offering real-time updates for traffic and information. This makes navigation easier and more efficient than ever before.
  2. It's important to stop labeling everything as AI. We need to focus more on creating useful machine learning systems that actually help people.
  3. Using data effectively can be tricky. Numbers can greatly influence policy, but relying solely on them can lead to problems.

Data Science Weekly - Issue 383

19 implied HN points • 25 Mar 21
🕹 Technology AI Data science Machine Learning Software Development Programming
  1. Artificial intelligence is making big strides in drug discovery, helping researchers tackle important problems more effectively. It's great to see technology playing a role in improving health outcomes.
  2. Jupyter notebooks are a popular tool among data scientists for data analysis and exploration, but some find them tricky to manage in production environments. It's a love/hate relationship for many users.
  3. Machine learning is becoming a key player in game development, helping to test and balance games more efficiently. This could lead to better gaming experiences for everyone.

Data Science Weekly - Issue 382

19 implied HN points • 18 Mar 21
🕹 Technology Data science Machine Learning Artificial Intelligence Statistical Modeling Data Visualization
  1. Computers will never truly understand or create good literature. They lack the ability to appreciate and express the complexities of human writing.
  2. Color scales are important in data visualization. Choosing the right color can make your data easier to understand and communicate.
  3. Data documentation and organization are crucial for effective data management. Having a clear framework helps teams work better and ensures everyone understands the data.

Data Science Weekly - Issue 381

19 implied HN points • 11 Mar 21
🕹 Technology Data science Machine Learning Artificial Intelligence Data Visualization Data Sharing
  1. COVID-19 skeptics use data and social media to promote their views. A study analyzed tweets and visual data to uncover their strategies.
  2. New reports on AI development show that the COVID-19 pandemic has impacted research and hiring in this field. It highlights how AI technology is being utilized in health-related areas.
  3. Machine learning can struggle with new data it wasn't trained on. Research is ongoing to improve its reliability and performance in real-world situations.

Data Science Weekly - Issue 380

19 implied HN points • 04 Mar 21
🕹 Technology Data science Machine Learning Neuroscience Data Privacy Artificial Intelligence
  1. Managing up is about sharing important facts with your manager to improve teamwork. It helps them understand what's slowing you down and what support you need.
  2. Data discovery platforms are evolving from traditional data catalogs, focusing on better ways to understand data context. This helps users find and utilize data more effectively.
  3. Generative adversarial transformers are a new kind of model that can produce high-quality visuals while being more efficient in computation. They could enhance creativity in visual content creation.

Data Science Weekly - Issue 379

19 implied HN points • 25 Feb 21
🕹 Technology Data science Machine Learning Artificial Intelligence Software Development Algorithms
  1. Writing a book on data science can be a fun way to inspire others to use data in their lives. The process can feel challenging but is ultimately rewarding.
  2. Learning about Python concurrency can be tricky but understanding it is important for data scientists moving into software engineering roles. Engaging with live coding talks can clarify complex concepts.
  3. Feature stores are becoming essential for managing machine learning data and making it easier to deploy models. They help data scientists collaborate and quickly get their work into production.

Data Science Weekly - Issue 378

19 implied HN points • 18 Feb 21
🕹 Technology Data science Machine Learning Artificial Intelligence Data Visualization
  1. Creating morals in robots can be similar to parenting techniques, which raises interesting questions about how we teach values to machines.
  2. There is a growing collection of data science podcasts available, making it easy for enthusiasts to find quality content and stay updated in the field.
  3. Research is exploring better and more stable methods for training neural networks, which could improve how computers learn and function like human brains.

Data Science Weekly - Issue 377

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

Data Science Weekly - Issue 376

19 implied HN points • 04 Feb 21
🕹 Technology Data science Machine Learning Statistical Analysis Software Development
  1. Data quality is super important for AI, especially in high-stakes situations like medical diagnoses. Poor data can lead to serious mistakes in predictions.
  2. DanNet revolutionized deep learning by being the first successful deep CNN in competitions. Its success marked a turning point in computer vision.
  3. Cohort analysis is a powerful way to examine customer data over time, helping businesses improve their user engagement and marketing strategies.

Data Science Weekly - Issue 375

19 implied HN points • 28 Jan 21
🕹 Technology Machine Learning Data science AI Ethics Programming Cloud Computing
  1. When building a machine learning team, it's important to adapt the team's structure as projects grow. Start small, but be ready to scale up as your needs change.
  2. Creating machine learning systems that can generalize well requires us to use observations to make inferences. This process, known as induction, helps build smarter algorithms.
  3. Machine learning is now being applied to modeling audio equipment, which could change the way we think about sound and effects in music production.

Data Science Weekly - Issue 374

19 implied HN points • 21 Jan 21
🕹 Technology Data science Machine Learning Artificial Intelligence Software Development Research
  1. Controlled experiments are important for understanding the impact of new features in software. They help ensure that changes actually improve user experience and metrics.
  2. Deep learning is being used in various scientific fields, making tools like DeepChem important for democratizing access to advanced technologies. This helps researchers across disciplines like chemistry and bioinformatics.
  3. There are innovative methods for diagnosing diseases like prostate cancer using AI. These techniques can offer high accuracy and reduce the need for invasive procedures.

Data Science Weekly - Issue 373

19 implied HN points • 14 Jan 21
🕹 Technology Data science Machine Learning Artificial Intelligence Computer Vision Software Development
  1. Machine learning is being used a lot in developmental biology. It helps scientists work with big data from things like images and gene studies, making analysis easier.
  2. There's a growing need for data engineers, with many companies looking for these roles. Focusing on engineering skills can open up more job opportunities than traditional data scientist roles.
  3. The U.S. government has started an initiative to promote and oversee artificial intelligence. This shows how important AI is to the economy and security of the nation.

Data Science Weekly - Issue 372

19 implied HN points • 07 Jan 21
🕹 Technology Data science Machine Learning Artificial Intelligence NLP Deep Learning
  1. DALL·E is a powerful AI that creates images from text descriptions, showcasing its ability to combine different ideas and concepts in creative ways.
  2. Machine learning is making significant strides in healthcare, but it also comes with risks that need careful consideration to ensure patient safety.
  3. Transformers have revolutionized natural language processing and are now being applied to various tasks in computer vision, improving how we manage data.

Data Science Weekly - Issue 371

19 implied HN points • 31 Dec 20
🕹 Technology Machine Learning Data science AI Research Technology Tools Statistical Analysis
  1. Real-time machine learning is becoming important for many companies. Some have invested heavily in the right infrastructure and are seeing good results.
  2. There are many new tools for machine learning and MLOps. Keeping track of these tools can help in improving workflow and project success.
  3. Understanding concepts like Markov models can help in planning routines, such as workouts, based on previous choices. This helps in making smart decisions about what to do next.

Data Science Weekly - Issue 370

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

Data Science Weekly - Issue 369

19 implied HN points • 17 Dec 20
🕹 Technology AI Data science Machine Learning Software Engineering Statistics
  1. Companies are changing how they share information because of AI. They're making their reports easier for machines to read, which can influence market behavior.
  2. Monitoring machine learning models is essential for maintaining accuracy. It's important to detect issues like outliers and changes in data patterns in real-time.
  3. Deep learning research often helps engineers tackle real-world problems effectively. Insights from recent research can guide better practices in building and deploying models.

Data Science Weekly - Issue 368

19 implied HN points • 10 Dec 20
🕹 Technology Data science Machine Learning Artificial Intelligence Deep Learning Data Tools
  1. Machine learning needs systematic approaches to create strong systems for real-world use. This means looking beyond just algorithms to see the bigger picture.
  2. Deep neural networks are powerful, but understanding how they work can be tricky. Tools like network dissection can help us figure out what these networks are really doing.
  3. Feature stores are becoming important for machine learning. They allow teams to share and manage data better for creating and deploying models quickly.

Data Science Weekly - Issue 367

19 implied HN points • 03 Dec 20
🕹 Technology AI Machine Learning Data science Software Development Robotics
  1. AlphaFold is a huge breakthrough in biology that helps solve the protein folding problem, which has puzzled scientists for 50 years. It shows how AI can speed up scientific discovery.
  2. Spotify needs good tools to make sense of its massive data from millions of users. Designing user-friendly data tools is key for them to understand and improve their services.
  3. Having high-quality data is essential for companies. New technologies can help businesses maintain data quality without spending huge amounts of money.

Data Science Weekly - Issue 366

19 implied HN points • 26 Nov 20
🕹 Technology Data science Machine Learning Artificial Intelligence Infrastructure Software Engineering
  1. Pinterest improved its machine learning signals by updating its data infrastructure. They moved from a Lambda architecture to a Kappa architecture for better real-time performance.
  2. DoorDash built a feature store to handle the massive amounts of data needed for its machine learning models. This helps them manage costs and maintain fast performance when retrieving data.
  3. When choosing between a data lake, warehouse, or lakehouse, it's important to consider the specific needs of your data platform. The right choice depends on the tools that best fit your project requirements.

Data Science Weekly - Issue 365

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