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 207

19 implied HN points • 09 Nov 17
🕹 Technology AI Data science Machine Learning Algorithms Analytics
  1. Feature visualization helps us understand how neural networks work. It's a useful tool for exploring the inner workings of AI models.
  2. Many deep learning models are more complex than necessary, which can slow down progress. Using simpler baselines can help us better measure our advancements in the field.
  3. Humans and machines can achieve better results when they work together. Instead of worrying about job loss from AI, we should focus on how to collaborate effectively.

Data Science Weekly - Issue 207

19 implied HN points • 09 Nov 17
🕹 Technology AI Machine Learning Data science Algorithms Analytics
  1. Feature visualization helps us understand how neural networks operate. It's a tool that gives us insights into what's going on inside these complex systems.
  2. Using simpler models can sometimes be better than powerful ones. When we rely too much on complicated models, we may lose sight of our actual progress.
  3. Working together, humans and machines can achieve more than either can alone. It's important to focus on collaboration rather than just worrying about job losses due to AI.

Data Science Weekly - Issue 206

19 implied HN points • 02 Nov 17
🕹 Technology Data science Machine Learning Artificial Intelligence Big Data Software Development
  1. A Fortune 50 company is looking to build a strong data science team in NYC. They want to hire both senior and junior data scientists.
  2. There's an interesting article about how humans are currently better than AI at playing StarCraft. A human gamer won a contest against AI with a score of 4-0.
  3. A new tool called Bounter can quickly count item frequencies in large datasets. It uses little memory and is designed for speed.

Data Science Weekly - Issue 206

19 implied HN points • 02 Nov 17
🕹 Technology Data science Machine Learning AI Big Data Analytics
  1. A big company is looking to hire a skilled data science team in NYC, including both senior and junior positions. If you're interested, reach out with your details.
  2. There are various articles about interesting projects in data science, like using machine learning for costume recommendations and understanding what causes wildfires. These kinds of studies show the diverse applications of data science.
  3. New tools and resources are being developed to make data science easier, like TensorFlow's eager execution. These advancements help data scientists to work more effectively with large datasets.

Data Science Weekly - Issue 205

19 implied HN points • 26 Oct 17
🕹 Technology AI Data science Machine Learning Analytics Programming
  1. AlphaGo's victories sparked discussions about the significance and implications of AI developments. People are curious about how AI researchers view these breakthroughs.
  2. Machine learning software can be tricky to debug, so using unit tests is really important. They can save a lot of time and help ensure your algorithms work correctly.
  3. Adversarial attacks can trick machine learning models into making wrong predictions, raising safety concerns about AI systems that we rely on.
Get a weekly roundup of the best Substack posts, by hacker news affinity:

Data Science Weekly - Issue 204

19 implied HN points • 19 Oct 17
🕹 Technology AI Machine Learning Data science Deep Learning Neural Networks
  1. Google is working on smart software that can create other software, making tech easier and more efficient.
  2. Our brains limit us to having meaningful relationships with only about five close friends, which is interesting for understanding social networks.
  3. There are many resources available, like open-source tools and training, that can help anyone learn data science and AI skills easily.

Data Science Weekly - Issue 202

19 implied HN points • 05 Oct 17
🕹 Technology Data science Machine Learning Artificial Intelligence Algorithms Big Data
  1. Algorithms can be used in designing unique structures, like concert halls, by creating specific shapes for materials based on calculations.
  2. Understanding bias in AI is crucial because it can lead to intelligent systems that reflect human prejudices rather than being fair.
  3. New York City is seen as a top place for data scientists to grow their careers and for companies to build strong data teams.

Data Science Weekly - Issue 201

19 implied HN points • 28 Sep 17
🕹 Technology Data science Machine Learning AI Programming Visualization
  1. Linear programming can help optimize diets for better health. It's about finding the best balance of food for weight loss and longevity.
  2. Understanding the risk of extreme weather events, like floods, can help cities prepare better. It's important to question outdated models when they don't match recent data.
  3. AI and machine learning are changing design fields, like web design, by enabling automated creation. This could make building websites easier and more efficient.

Data Science Weekly - Issue 200

19 implied HN points • 21 Sep 17
🕹 Technology Data science Machine Learning Artificial Intelligence Programming Analytics
  1. Machine-vision drones can assist in monitoring wildlife by providing accurate population estimates in remote areas. This technology helps wildlife management efforts.
  2. Unity has introduced Machine Learning Agents that can help researchers and game developers experiment with applying machine learning in gaming scenarios. This will enhance both fields by bridging the gap between them.
  3. There are many resources available for those interested in data science, including tutorials and job listings. These can help you improve your skills and find opportunities in the data science field.

Data Science Weekly - Issue 198

19 implied HN points • 07 Sep 17
🕹 Technology Data science Machine Learning Artificial Intelligence Software Development Data Analysis
  1. Uber has developed a machine learning platform called Michelangelo that makes it easier for businesses to use AI and machine learning.
  2. Understanding how to evaluate models with imbalanced data sets is important for data scientists, specifically using precision, recall, or ROC metrics.
  3. Data journalism is evolving, and interviews with journalists and developers can reveal best practices for creating engaging digital stories.

Data Science Weekly - Issue 197

19 implied HN points • 31 Aug 17
🕹 Technology AI Data science Machine Learning Software Development Data Visualization
  1. Amazon's AI can help you find styles that suit you by using machine learning. It can even make new styles from scratch!
  2. Being a non-traditional data scientist is possible with interest and a willingness to learn. Many paths can lead you to a successful career in data science, even from diverse backgrounds.
  3. AI and machine learning are becoming essential tools in data science, expected to drive future economic growth just like past innovations such as electricity.

Data Science Weekly - Issue 196

19 implied HN points • 24 Aug 17
🕹 Technology Data science Machine Learning Artificial Intelligence Deep Learning Programming
  1. Using machine learning models, like recurrent neural networks, can enhance text editing by making it smarter and more responsive. It allows for cool features like inline autocomplete that feels very natural.
  2. When choosing between deep learning frameworks like PyTorch and TensorFlow, think about how easy they are to use and their flexibility for your specific project needs.
  3. Building a strong data science resume and portfolio is crucial to getting hired; make sure they highlight your skills and tailor them to each job you apply for.

Data Science Weekly - Issue 195

19 implied HN points • 17 Aug 17
🕹 Technology Data science Machine Learning AI Programming Statistics
  1. The OpenAI DotA 2 bot is an impressive project, but it's important to understand that it's not the revolutionary breakthrough some claim it to be. It's a significant achievement in AI, yet its implications should be viewed more critically.
  2. There are innovative tools and experiments that use machine learning to enhance how we interact with platforms like Wikipedia, making it easier to explore content effectively. This shows how technology can change our access to information.
  3. Machine learning and AI are evolving rapidly, with new techniques such as autoregressive models and advanced algorithms present in various fields. It's exciting to see how these developments are shaping technology and everyday life.

Data Science Weekly - Issue 194

19 implied HN points • 10 Aug 17
🕹 Technology Data science Machine Learning AI Software Development Data Visualization
  1. Computers can predict successful startups using AI, and they performed surprisingly well in identifying companies like Evernote and Spotify.
  2. Choosing the right data visualization style can help viewers understand information more easily, whether it's showing geographic variations or busy activity areas.
  3. Understanding different deep learning frameworks like PyTorch and TensorFlow is important for effective model building and analysis in data science.

Data Science Weekly - Issue 193

19 implied HN points • 03 Aug 17
🕹 Technology AI Data science Machine Learning Natural Language Processing Software Development
  1. Salesforce is working on making artificial intelligence easier to use by automating how machine learning models are created.
  2. There's an important debate in social science about what counts as strong evidence in research, especially regarding the use of p-values.
  3. AI is being used in fun ways, like teaching machines to develop language skills and even create their own dance moves by watching games.

Data Science Weekly - Issue 192

19 implied HN points • 27 Jul 17
🕹 Technology Data science Artificial Intelligence Machine Learning Data Visualization Computer Science
  1. We need to consider the entire system when discussing data, not just the algorithms or models. This helps us understand the bigger picture and ask meaningful questions about how things work.
  2. There are many guidelines for figuring out if something causes another thing. It can be helpful to look at these through creative ways, like using comics to explain complex ideas.
  3. Robots are getting better at imitating humans, which can be a threat to democratic societies. It's important to stay aware of how these technologies can be misused.

Data Science Weekly - Issue 191

19 implied HN points • 20 Jul 17
🕹 Technology Data science Machine Learning Artificial Intelligence Data Visualization Algorithm Design
  1. Understanding your data is crucial in machine learning. Using visualization tools can help you make sense of large datasets and reveal important insights.
  2. AI can unintentionally learn biases from data, leading to unfair outcomes. It's important to know how these biases can occur and take steps to avoid them.
  3. Machine learning models require careful tuning to avoid overfitting or underfitting. Balancing complexity and performance is key to building effective models.

Data Science Weekly - Issue 190

19 implied HN points • 13 Jul 17
🕹 Technology Data science Machine Learning Artificial Intelligence Software Development Statistical Analysis
  1. Technical debt in machine learning can build up quickly and affect project timelines. Even skilled teams might struggle to manage it and can face major setbacks.
  2. The role of a data product manager is becoming important as companies rely more on data. This new position will be vital for guiding product decisions based on data insights.
  3. Using deep learning models can significantly improve tasks like diagnosing health conditions from data, often outperforming specialists in accuracy.

Data Science Weekly - Issue 189

19 implied HN points • 06 Jul 17
🕹 Technology AI Data science Machine Learning Deep Learning Analytics
  1. Machines are starting to create art that can compete with human artists. This raises interesting questions about creativity and technology.
  2. New tools are helping to improve both music and audio quality using advanced deep learning techniques. This could change how we experience sound.
  3. Companies like General Electric are using AI to enhance their operations and adapt to modern tech trends. This shows how traditional industries are evolving with technology.

Data Science Weekly - Issue 188

19 implied HN points • 29 Jun 17
🕹 Technology Data science AI Machine Learning Algorithms Software Development
  1. Amazon has been improving its recommender systems for two decades, which helps customers find products they might not have seen otherwise.
  2. New algorithms are needed to fully utilize the advanced AI chips, like NVIDIA's latest GPU, to take AI applications to the next level.
  3. There are resources available for learning data science, including step-by-step guides, video datasets, and new neural network libraries.

Data Science Weekly - Issue 2

39 implied HN points • 05 Dec 13
🕹 Technology Data science Machine Learning Big Data Predictive Analytics Statistics
  1. Visual image extraction can enhance social image searches using big data techniques. This can help businesses understand how their images are perceived online.
  2. Probabilistic programming can model complex, unseen factors in finance that influence market behavior, like investor fear. This approach provides better tools for understanding market trends.
  3. Big data technologies can analyze social media pictures to find popular locations, helping businesses discover the best spots to attract customers.

Data Science Weekly - Issue 187

19 implied HN points • 22 Jun 17
🕹 Technology Data science Artificial Intelligence Machine Learning Software Development Data Analysis
  1. Data from millions of social media photos can reveal important patterns about our clothing choices. This shows how useful data mining can be for understanding human behavior.
  2. Artificial intelligence is making strides in predicting mental health risks, like suicide. This can help save lives by allowing for timely interventions.
  3. Deep learning is useful for many different tasks, but developers often struggle to tune models. New approaches are being explored to simplify and improve the process.

Data Science Weekly - Issue 1

39 implied HN points • 28 Nov 13
🕹 Technology Data science Big Data Machine Learning Artificial Intelligence Programming
  1. To make big data useful, it needs to be connected to insights and actions that help decision makers. Without this connection, data can just confuse rather than clarify.
  2. Big data is being applied in many ways that can create real benefits in different areas. These applications can have a major positive impact on various industries and society.
  3. There are powerful tools like Python that data scientists use for analysis and visualization, which help in working with data effectively. It's becoming a popular choice due to its versatility and ease of use.

Data Science Weekly - Issue 186

19 implied HN points • 15 Jun 17
🕹 Technology Data science Machine Learning Artificial Intelligence Software Development Big Data
  1. Data science is key in optimizing services like Netflix, helping to deliver content efficiently worldwide.
  2. New algorithms can summarize long texts well, which can help in areas like medicine and law by making information easier to understand.
  3. Building visual maps and understanding neural networks are important steps in advancing data science and machine learning fields.

Data Science Weekly - Issue 185

19 implied HN points • 08 Jun 17
🕹 Technology Data science Machine Learning Artificial Intelligence Big Data Programming
  1. The Google Brain Residency Program allows people to work with top scientists in machine learning and deep learning for a year. It's a great opportunity to learn and network in a cutting-edge field.
  2. Natural language processing can help analyze products like wine by using descriptive language instead of traditional data. This approach can uncover unique insights about different wines.
  3. New AI features in tools like Google Sheets aim to automate tasks and improve office efficiency. These smart tools can eventually help companies work faster and smarter.

Data Science Weekly - Issue 184

19 implied HN points • 01 Jun 17
🕹 Technology AI Data science Machine Learning Algorithms Software Development
  1. Artificial intelligence is rapidly evolving and has the potential to perform tasks better than humans, raising questions about job security.
  2. There is a growing interest in explainable algorithms, especially in decision-making areas like housing and education.
  3. Deep learning and advanced technologies like Jupyter are making it easier to analyze data and transform ideas into real-world solutions.

Data Science Weekly - Issue 183

19 implied HN points • 25 May 17
🕹 Technology Data science Machine Learning Artificial Intelligence Analytics Programming
  1. AI can help name new colors, which is important because there are so many shades that we might run out of good names to give them.
  2. Machine learning competitions, like the Data Science Bowl, can be a great learning opportunity even if you don't have specific expertise in the subject.
  3. Automated machine learning tools can really boost a data scientist's productivity, especially for certain types of problems, but you still need human knowledge to set things up properly.

Data Science Weekly - Issue 182

19 implied HN points • 18 May 17
🕹 Technology Data science Machine Learning Artificial Intelligence Predictive Analytics Software Development
  1. AI in medicine is advancing, allowing devices to monitor health continuously and alert doctors to issues. This could change how we receive medical care.
  2. Companies can improve their forecasting skills by training employees in prediction methods. Everyone, regardless of their background, can learn to make better predictions.
  3. Data scientists face challenges when using laptops for resource-heavy tasks. They often have to choose between speed and complexity, which can impact their performance.

Data Science Weekly - Issue 181

19 implied HN points • 11 May 17
🕹 Technology AI Data science Machine Learning Software Development Big Data
  1. Using deep learning can significantly improve how algorithms rank content, like Twitter does with its timelines.
  2. Companies like Airbnb use A/B testing to experiment and understand how changes to their platform affect users.
  3. New technologies in AI are being developed, such as visual attribute transfer and mind-reading algorithms, which could change how machines understand and interact with the world.

Data Science Weekly - Issue 180

19 implied HN points • 04 May 17
🕹 Technology Machine Learning Data science Artificial Intelligence Software Development Programming
  1. Machine learning can help improve design tools, making them simpler without stifling creativity for designers. This can feel surprising but can enhance the design process.
  2. AI can connect and explore relationships between different fonts through an interactive map, showcasing the power of technology in creative fields.
  3. Understanding the economic value of AI is key; it's important to analyze how it reduces costs to see its overall impact on different industries.

Data Science Weekly - Issue 179

19 implied HN points • 27 Apr 17
🕹 Technology Data science Artificial Intelligence Machine Learning Computer Vision Deep Learning
  1. Robots are getting smarter and might make their own choices, raising questions about their moral decisions. We need to think about what it means for a machine to behave morally.
  2. Creating effective Optical Character Recognition involves advanced technologies like deep learning and computer vision, showcasing how complex tech solutions can be in modern projects.
  3. Machines can analyze data in ways we may not fully understand, challenging our long-held beliefs about knowledge and order. This raises interesting points about how we trust these systems.

Data Science Weekly - Issue 178

19 implied HN points • 20 Apr 17
🕹 Technology Data science Machine Learning Artificial Intelligence Career development Programming
  1. There are helpful guides for jumping into data science, which can save time and provide a clear path for learning. These guides focus on figuring out what you need to learn, building a strong portfolio, and creating an impressive resume.
  2. AI and machine learning are making amazing advancements, like predicting heart attacks better than doctors and developing chatbots that can show emotions. These technologies are changing how we interact with machines and can improve our lives significantly.
  3. Resources like courses, articles, and books about data science are available to help people grow their skills. Whether it’s learning about deep learning tools or understanding statistical concepts, there's plenty of information out there.

Data Science Weekly - Issue 177

19 implied HN points • 13 Apr 17
🕹 Technology Machine Learning Data science Artificial Intelligence Software Development Analytics
  1. Machine learning is evolving, and analyzing trends over time can give insights into its growth and changes. It helps us understand what areas are becoming more popular or useful.
  2. Deploying machine learning models into real business settings is challenging, often requiring teamwork and effective communication between data scientists and other roles.
  3. AI is influencing how companies are structured and operate, pushing leaders to rethink their business strategies and workflows.

Data Science Weekly - Issue 176

19 implied HN points • 06 Apr 17
🕹 Technology Artificial Intelligence Data science Machine Learning Computer Vision Robotics
  1. Image style transfer can turn famous impressionist paintings into more realistic photos, helping us see the world through the artist's eyes.
  2. DeepMind claims to have made a breakthrough in artificial general intelligence, which could have significant impacts on the future of AI.
  3. One-shot imitation learning allows robots to learn new tasks quickly and without needing a lot of examples, making them more adaptable.

Data Science Weekly - Issue 175

19 implied HN points • 30 Mar 17
🕹 Technology Data science Artificial Intelligence Machine Learning Big Data Data Visualization
  1. Deep learning is becoming important for various parts of companies like Facebook. It's not just a special skill; it's useful everywhere from messaging to ads.
  2. Nvidia is focusing on making chips that can help improve healthcare through AI. They see medicine as a big chance to apply their technology.
  3. Data visualization is crucial for understanding information. Tools like Pandas and Seaborn help people make sense of data easily.

Data Science Weekly - Issue 174

19 implied HN points • 23 Mar 17
🕹 Technology Data science Machine Learning AI Programming Analytics
  1. Data science is becoming more essential in industries, helping to match customer preferences with the right products, like how Stitch Fix connects clients with styles they love.
  2. Machine learning is expanding beyond digital environments, making real-world applications like internet delivery through balloons a possibility.
  3. Choosing the right GPU can significantly speed up deep learning experiments, making it important for those working with AI to understand their options.

Data Science Weekly - Issue 173

19 implied HN points • 16 Mar 17
🕹 Technology Data science Machine Learning AI Software Development Health Technology
  1. Pi is important because it represents the idea of infinity and the beauty found in mathematics. It has endless digits that seem random, showing a unique balance between order and chaos.
  2. Voice technology is booming in the tech world, with devices like Amazon's Echo leading the charge. This shift brings both opportunities and challenges for developers and users.
  3. Data science is becoming more accessible with practical examples and applications emerging in real-world scenarios. Companies are using data science to improve their products and daily operations.

Data Science Weekly - Issue 172

19 implied HN points • 09 Mar 17
🕹 Technology Machine Learning Data science Artificial Intelligence Software Development Robotics
  1. Debugging machine learning models is hard because you often can't easily see what went wrong. It can take a lot of time and effort to improve the performance of these models.
  2. Machine learning can help predict events like earthquakes in a lab setting, which is exciting for the future of real-world prediction abilities.
  3. New technologies like generative networks are being developed to address issues caused by existing models, aiming for better and safer outcomes.