The hottest Analytics Substack posts right now

And their main takeaways
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Data Science Weekly - Issue 255

Data Science Weekly Newsletter • 19 implied HN points • 11 Oct 18
🕹 Technology Data science Machine Learning Artificial Intelligence Programming Analytics
  1. The ML Engineering Loop helps engineers improve their model development by following a cycle of analyzing, selecting approaches, implementing, and measuring. This cycle allows them to quickly find the best solutions.
  2. Understanding uncertainty in data visualizations is important, and integrating uncertainty estimates can improve how we interpret plots and models. This can lead to better decision-making based on data.
  3. Using tools like TensorFlow.js for practical applications, such as object recognition in games, shows how machine learning can be fun and engaging. These examples help in learning and applying complex concepts in a creative way.

Data Science Weekly - Issue 252

Data Science Weekly Newsletter • 19 implied HN points • 20 Sep 18
🕹 Technology Data science Machine Learning AI Cybersecurity Analytics
  1. A team found a surprising pattern in prime numbers, linking them to natural crystal patterns. This challenges the idea that prime numbers are completely random.
  2. DeepMind's AI is being used in Android Pie to help improve battery life, showing how AI can impact everyday technology. It's interesting to see if this actually makes a difference for users.
  3. Transfer learning makes it easier to solve problems by using knowledge from similar tasks. This approach saves time and resources in the field of deep learning.

Data Science Weekly - Issue 251

Data Science Weekly Newsletter • 19 implied HN points • 13 Sep 18
🕹 Technology AI Data science Machine Learning Automation Analytics
  1. AI systems, like Amazon's Echo, rely on many factors, including resources and labor. Understanding these can give insights into the complexity of AI.
  2. Fake news can significantly impact politics, and there's now a mathematical model to help simulate how it influences voting. This shows the power of accurate models in understanding societal effects.
  3. There are new tools and techniques in machine learning that make it easier to analyze and improve models. Resources like the 'What-If' tool let users explore machine learning without needing to code.

Data Science Weekly - Issue 250

Data Science Weekly Newsletter • 19 implied HN points • 06 Sep 18
🕹 Technology Data science Machine Learning Artificial Intelligence Analytics Programming
  1. There's a growing need for data scientists in the U.S. now that there's a shortage, which is a big change from just a few years ago when there were too many people in the field.
  2. New approaches in machine learning, like unsupervised machine translation, are making it easier to provide fast and accurate translations in many languages, helping people connect better.
  3. Researchers are looking into how small changes in images can confuse computer vision models, and they wonder if the same happens to humans, pointing out potential vulnerabilities in both AI and human vision.

Data Science Weekly - Issue 248

Data Science Weekly Newsletter • 19 implied HN points • 23 Aug 18
🕹 Technology Data science Machine Learning Artificial Intelligence Software Development Analytics
  1. AI is changing how we do business, and it's becoming more self-sufficient, meaning it could improve processes on its own without needing human input.
  2. China uses data and AI extensively for surveillance and governance, which raises questions about the balance between democracy and data-driven control.
  3. New tools and technologies are constantly emerging in data science, such as those that help improve the speed of medical procedures like MRIs and enhance gaming graphics.
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Data Science Weekly - Issue 244

Data Science Weekly Newsletter • 19 implied HN points • 26 Jul 18
🕹 Technology Data science Machine Learning Artificial Intelligence Analytics Programming
  1. Companies should define data science roles using three tracks: Analytics, Inference, and Algorithms. This helps meet business needs more effectively.
  2. Google's AutoML is a tool that automates machine learning processes, tapping into transfer learning to enhance capabilities and ease of use.
  3. Multi-task learning allows machines to learn multiple tasks at once, making them smarter and better at handling complex problems, similar to how humans learn.

Data Science Weekly - Issue 240

Data Science Weekly Newsletter • 19 implied HN points • 28 Jun 18
🕹 Technology Data science Machine Learning Artificial Intelligence Analytics Programming
  1. AI has become very powerful, even beating expert humans in complex games like Dota 2. This shows how quickly technology is advancing.
  2. Data science can play a meaningful role in addressing social issues, like the problem of public human waste in cities. Mixing social science with data could lead to helpful solutions.
  3. Building a data dictionary is crucial for teams, as it helps clarify key terms and metrics. This can greatly improve communication and reduce confusion within a business.

Data Science Weekly - Issue 236

Data Science Weekly Newsletter • 19 implied HN points • 01 Jun 18
🕹 Technology Data science Machine Learning Artificial Intelligence Statistics Analytics
  1. Improving training data is really important for making machine learning models work well. Focusing on data quality can lead to better results than just tweaking the model itself.
  2. AI tools are making a big difference in healthcare, like the one approved for detecting wrist fractures. These technologies can help doctors diagnose patients more accurately.
  3. Google found that some tricky interview questions didn't actually help in hiring good candidates. It shows that being smart isn't just about solving brainteasers.

Data Science Weekly - Issue 236

Data Science Weekly Newsletter • 19 implied HN points • 31 May 18
🕹 Technology Data science Machine Learning Analytics Artificial Intelligence Big Data
  1. Natural disasters like Hurricane Maria can have serious health impacts, and it's hard to get an accurate death count afterward.
  2. Improving training data is key to making better machine learning models, and there are practical ways to enhance that data.
  3. Reproducibility in machine learning is important, but it can be tough to achieve and often requires careful planning and work.

Data Science Weekly - Issue 220

Data Science Weekly Newsletter • 19 implied HN points • 08 Feb 18
🕹 Technology Data science Machine Learning AI Ethics Software Development Analytics
  1. A large database helps researchers understand what makes people happy. This information can be used to improve well-being.
  2. Deep learning has some limitations, like being too simple or not always reliable. It's important to recognize these downsides as we advance in AI.
  3. There’s a need for ethical guidelines in data science because so much data is created every day. We need to ensure this data is used responsibly.

Data Science Weekly - Issue 218

Data Science Weekly Newsletter • 19 implied HN points • 25 Jan 18
🕹 Technology Artificial Intelligence Data science Machine Learning Analytics Software Development
  1. Artificial intelligence (AI) is rapidly changing many industries, similar to how electricity transformed the world. It's important to understand its potential impact on various sectors.
  2. Using data science can help create fairer political maps, a task that involves settling disagreements on what 'fair' means. This is a significant challenge in the fight against gerrymandering.
  3. Recommendation systems are not just for e-commerce; they can be used in any decision-making scenario where matching items is important. Understanding how they work can help improve their effectiveness in various applications.

Data Science Weekly - Issue 214

Data Science Weekly Newsletter • 19 implied HN points • 28 Dec 17
🕹 Technology Data science Machine Learning Artificial Intelligence Deep Learning Analytics
  1. There was a lot of cool stuff happening in data science in 2017. It's a good idea to look back and see what others accomplished that year.
  2. NVIDIA is facing competition in deep learning hardware with new products coming from AMD and Intel. It might be wise to hold off on buying new hardware until the market settles.
  3. Machine learning is getting more attention in fields like physics, showing its importance in making big discoveries. Using tools like Python is becoming essential in modern science.

Data Science Weekly - Issue 213

Data Science Weekly Newsletter • 19 implied HN points • 21 Dec 17
🕹 Technology Data science Machine Learning Programming Artificial Intelligence Analytics
  1. Machine learning can help decode animal communication, like chicken chatter, for better farming practices. This shows how AI can be useful in agriculture.
  2. Turning raw data into useful products is complex, as seen with Google Maps, which relies on a lot of behind-the-scenes work. It highlights the importance of data processing in creating useful tools.
  3. Finding exoplanets is challenging, but machine learning has made some progress in this area. It illustrates how technology is advancing our understanding of the universe.

Data Science Weekly - Issue 207

Data Science Weekly Newsletter • 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

Data Science Weekly Newsletter • 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

Data Science Weekly Newsletter • 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

Data Science Weekly Newsletter • 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.

Data Science Weekly - Issue 200

Data Science Weekly Newsletter • 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 189

Data Science Weekly Newsletter • 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 183

Data Science Weekly Newsletter • 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 177

Data Science Weekly Newsletter • 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 174

Data Science Weekly Newsletter • 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 166

Data Science Weekly Newsletter • 19 implied HN points • 26 Jan 17
🕹 Technology Data science Machine Learning Artificial Intelligence Programming Analytics
  1. Deep learning engineers need to understand hardware and optimization details, not just focus on code and algorithms. This awareness helps improve the performance of neural networks.
  2. There are many resources available for those looking to start a career in deep learning. The demand for knowledgeable engineers in this field is growing rapidly.
  3. Visualizing data can tell different stories depending on how it's presented. It's important to choose the right chart to make the data's message clear.

Data Science Weekly - Issue 162

Data Science Weekly Newsletter • 19 implied HN points • 29 Dec 16
🕹 Technology Data science Machine Learning Artificial Intelligence Programming Analytics
  1. Some articles highlight interesting stories in data science and research, like how bats communicate or how AI can help hide computer screens when a boss approaches.
  2. It's important to choose and master a data science tool, like R, as it remains popular even though other languages may take its place in the future.
  3. Learning about advanced topics, like Bayesian inference and deep learning techniques, can help you improve your data science skills and understanding.

Data Science Weekly - Issue 159

Data Science Weekly Newsletter • 19 implied HN points • 08 Dec 16
🕹 Technology Data science Machine Learning Artificial Intelligence Software Tools Analytics
  1. Deep learning made significant progress in 2016, impacting the field of machine learning greatly. Many organizations are focusing on ensuring that these new technologies are used positively.
  2. There are fun experiments exploring how neural networks can predict handwriting strokes. This shows the creative side of using AI in everyday tasks.
  3. Understanding data's role in infrastructure can highlight where big investments are needed. Maps illustrating America's infrastructure can prepare us for large-scale projects.

Data Science Weekly - Issue 158

Data Science Weekly Newsletter • 19 implied HN points • 01 Dec 16
🕹 Technology Data science Machine Learning Artificial Intelligence Programming Analytics Data Visualization
  1. Machine intelligence is making predictions cheaper, which can create big economic changes. This technology is becoming essential in many fields.
  2. Retailers can use machine learning to manage fresh food stock better, avoiding waste and shortages. This helps them save money and serve customers better.
  3. AI is starting to impact medicine, like an AI that can detect eye diseases as well as human doctors. This could change how we approach healthcare.

Data Science Weekly - Issue 154

Data Science Weekly Newsletter • 19 implied HN points • 03 Nov 16
🕹 Technology Data science Machine Learning Statistics Programming AI Analytics
  1. A/B testing can go wrong if you check results too often. It's important to avoid stopping tests too soon based on p-values.
  2. Many data science projects fail due to misunderstandings and poor planning. Recognizing common pitfalls can help ensure better outcomes.
  3. Using advanced techniques like neural networks can enhance tasks like image resolution. This shows how technology is evolving in data science.

Data Science Weekly - Issue 130

Data Science Weekly Newsletter • 19 implied HN points • 19 May 16
🕹 Technology Data science Machine Learning Programming Statistics Analytics
  1. Transcribing long-term rental ads can provide valuable insights into housing price trends. This type of data collection helps inform discussions on affordable housing.
  2. Data natives, or people who grew up using technology, expect smart systems that adapt to their preferences seamlessly. This shift is changing how we interact with data and technology.
  3. Power analysis is important for scientists planning experiments. It helps them understand if an experiment will be effective and what data they need to collect.

Data Science Weekly - Issue 129

Data Science Weekly Newsletter • 19 implied HN points • 12 May 16
🕹 Technology Data science Machine Learning Artificial Intelligence Programming Analytics
  1. Machine learning can help understand emoji usage and trends on social media. It's exciting to see how technology can analyze emotions expressed through simple icons.
  2. There's a growing idea that future AI may not be about creating more AI but about building platforms for people to design their own AI. This could make technology more personal and user-friendly.
  3. Automating data science processes can save time and make it easier for everyone to use machine learning effectively. Tools that simplify these tasks can be really useful for beginners.

Data Science Weekly - Issue 124

Data Science Weekly Newsletter • 19 implied HN points • 07 Apr 16
🕹 Technology Data science Machine Learning Artificial Intelligence Software Development Analytics
  1. Data science is important for startups and should be integrated early to help in decision-making and culture building.
  2. Machine learning can enhance user experiences, like preventing movie spoilers or predicting bus arrival times.
  3. Learning opportunities, like functional programming and specific data science skills, are available for those looking to enter the field.

Data Science Weekly - Issue 122

Data Science Weekly Newsletter • 19 implied HN points • 24 Mar 16
🕹 Technology Data science Machine Learning Artificial Intelligence Analytics Statistics
  1. P-values are often seen as the gold standard for determining if a study is good, but experts warn they can be misleading and overused.
  2. Machine learning is changing how businesses operate and is being used in various real-world applications, showing its growing importance.
  3. Deep learning techniques are making big strides in generative models, helping to produce and understand complex data like images and text.

Data Science Weekly - Issue 118

Data Science Weekly Newsletter • 19 implied HN points • 25 Feb 16
🕹 Technology Data science Machine Learning Artificial Intelligence Software Development Analytics
  1. Netflix uses special computer programs to suggest shows to viewers, helping them find stories they love. This helps Netflix connect with more people around the world.
  2. The eating habits in Britain have changed a lot over the last 50 years, with traditional foods being replaced by more modern options. There are tools online that let you see these changes over time.
  3. Airbnb is working to make sure their hiring practices are fair and that they have a more diverse team. They're using research and testing to understand and improve their interview processes.

Data Science Weekly - Issue 117

Data Science Weekly Newsletter • 19 implied HN points • 18 Feb 16
🕹 Technology Data science Machine Learning Artificial Intelligence Analytics Programming
  1. Understanding causality could be more important than just focusing on deep learning. It's a key concept that can help data scientists make sense of their work.
  2. Effective data science teams have clear markers of success. It's important to figure out what these teams do well and how they train their members.
  3. Data scientists often do basic arithmetic, which is actually very valuable. Simplifying complex data tasks can lead to meaningful insights.

Data Science Weekly - Issue 116

Data Science Weekly Newsletter • 19 implied HN points • 11 Feb 16
🕹 Technology Data science Machine Learning Artificial Intelligence Programming Analytics
  1. Kaggle is a great place for data scientists to learn and share ideas. They have a huge collection of machine learning models that can help you improve your skills.
  2. Genetic algorithms can solve tough problems by mimicking natural evolution. They work by selecting the best solutions and mixing them to create new ones.
  3. Understanding data ethics is important for data scientists. People often trust numbers too much, so it's crucial to think about how data is used responsibly.

Data Science Weekly - Issue 115

Data Science Weekly Newsletter • 19 implied HN points • 04 Feb 16
🕹 Technology Data science Machine Learning Artificial Intelligence Analytics Robotics
  1. Bird migration patterns can now be visualized, showing how millions of birds move across the Western Hemisphere. This helps us understand nature better.
  2. Machine learning is being used alongside social media data to identify flooded areas quickly and accurately. It's an innovative way to respond to natural disasters.
  3. The importance of model interpretability in data science is highlighted. Being able to explain complex models is crucial, especially when working with non-technical teams.

Data Science Weekly - Issue 105

Data Science Weekly Newsletter • 19 implied HN points • 26 Nov 15
🕹 Technology Data science Machine Learning Artificial Intelligence Big Data Analytics
  1. Machine learning can be used in unexpected ways, like analyzing real-time video feeds to understand what is being seen. This shows the creative side of data science.
  2. It's important to acknowledge that the hardest part of data science isn’t just building models or collecting data. Instead, it’s about figuring out what problems to solve and how to measure success.
  3. There’s a big difference in how people respond to the same foods, and data science can help us understand these differences, leading to better nutrition solutions for individuals.

Data Science Weekly - Issue 104

Data Science Weekly Newsletter • 19 implied HN points • 19 Nov 15
🕹 Technology Data science Machine Learning Artificial Intelligence Programming Analytics
  1. Using Python with tools like Hadoop and Spark helps make data analysis easier in big projects.
  2. A new algorithm can make computational modeling much faster, helping researchers get results quicker.
  3. Machine learning is being used in cool ways, like studying galaxy formation and creating images from random noise.

Data Science Weekly - Issue 103

Data Science Weekly Newsletter • 19 implied HN points • 12 Nov 15
🕹 Technology Data science Machine Learning Artificial Intelligence Programming Analytics
  1. Facebook's AI has made big improvements, showcasing its capabilities in smart technology. This shows how AI is becoming more advanced and useful in everyday life.
  2. There are lots of resources available for learning about data science and machine learning. This includes articles, webinars, and books that can help both beginners and experienced users.
  3. Finding junior data scientist jobs can be tricky because many companies seek senior candidates. It's important for newcomers to explore different strategies and networks to find entry-level opportunities.