The hottest Data science Substack posts right now

And their main takeaways
Category
Top Technology Topics

Data Science Weekly - Issue 84

Data Science Weekly Newsletter • 19 implied HN points • 02 Jul 15
🕹 Technology Data science
  1. Neural networks are being used to create things like text, music, and images. They're learning from examples and getting better at generating content.
  2. Machine learning can help predict crime in cities by analyzing data from various sources. This approach aims to enhance safety and efficiency in crime prevention.
  3. Getting good at machine learning requires practice and understanding. There are many resources available, like cheat sheets and books, to help beginners learn the basics.

Data Science Weekly - Issue 83

Data Science Weekly Newsletter • 19 implied HN points • 25 Jun 15
🕹 Technology Data science
  1. A neural conversational model has been developed by Google to build better chatbots that can understand and respond like humans.
  2. Data mining has uncovered surprising factors that make movies successful, challenging previous beliefs about relying only on famous actors.
  3. There has been a significant drop in death rates from heart disease due to improved emergency treatments in hospitals.

Data Science Weekly - Issue 82

Data Science Weekly Newsletter • 19 implied HN points • 18 Jun 15
🕹 Technology Data science
  1. Neural networks can learn and play video games, like Super Mario, on their own. It's cool to see machines get better at tasks we enjoy.
  2. Deep learning technology is now good enough to outperform humans on certain IQ test questions. This shows how advanced AI has become.
  3. IBM is using its Watson Analytics in unmanned coffee shops to analyze data, making business operations smoother without a lot of staff. It's a sign of how technology is changing our everyday experiences.

Data Science Weekly - Issue 81

Data Science Weekly Newsletter • 19 implied HN points • 11 Jun 15
🕹 Technology Data science
  1. Machine learning can analyze startup data to predict outcomes for new companies. This technology learns from past successes and failures.
  2. Airbnb uses big data to help hosts price their listings effectively. They guide hosts to set prices that are beneficial for both parties.
  3. Artificial intelligence can now solve complex scientific problems on its own. This marks a significant advancement in how computers contribute to research.

Data Science Weekly - Issue 80

Data Science Weekly Newsletter • 19 implied HN points • 04 Jun 15
🕹 Technology Data science
  1. Machine learning can predict future events by analyzing past data. For example, it can be used to forecast the weather based on previous weather observations.
  2. Gaze estimation is a task in computer vision where algorithms detect where a person is looking. Recent advancements allow one computer to train another to improve this recognition.
  3. Statistical significance in studies refers to the results, not the sample itself. Ensuring you have enough data is key to obtaining reliable outcomes.
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Data Science Weekly - Issue 79

Data Science Weekly Newsletter • 19 implied HN points • 28 May 15
🕹 Technology Data science
  1. Recurrent Neural Networks (RNNs) are powerful tools that can generate surprisingly good text, like image descriptions, quickly and easily.
  2. AI, like IBM's Chef Watson, is being used in creative ways, such as suggesting meals based on available ingredients, showing how tech can help with daily tasks.
  3. Google is developing tech that can analyze food photos to count calories, highlighting how machine learning can be applied to health and nutrition.

Data Science Weekly - Issue 78

Data Science Weekly Newsletter • 19 implied HN points • 21 May 15
🕹 Technology Data science
  1. Machine learning can create interesting comparisons in sports, like calculating fair distances for athletes with different strengths.
  2. Using data creatively can lead to fun projects, such as making beer recipes reflect local demographics or generating rap lyrics with algorithms.
  3. There's a shift in how we think about recommendation systems; they should focus more on user experience than just maximizing success metrics.

Data Science Weekly - Issue 77

Data Science Weekly Newsletter • 19 implied HN points • 14 May 15
🕹 Technology Data science
  1. Data scientists often come from different backgrounds, not just math or computer science. Learning some software development skills can be very helpful for data scientists.
  2. Machine learning has advanced to a point where algorithms can outperform experts in certain fields, like art history. This shows how powerful technology can be in analyzing complex data.
  3. Understanding statistical methods, like p-values, is important for good science. It's crucial to scrutinize every step of data analysis, not just the final results.

Data Science Weekly - Issue 76

Data Science Weekly Newsletter • 19 implied HN points • 07 May 15
🕹 Technology Data science
  1. Machine learning is being used to understand emoji trends on social media, showing how digital language is evolving.
  2. Companies like WePay are applying machine learning to tackle specific problems, such as preventing fraud.
  3. There are exciting advancements in using algorithms for real-time trading and data analysis, improving how we handle big data.

Data Science Weekly - Issue 75

Data Science Weekly Newsletter • 19 implied HN points • 30 Apr 15
🕹 Technology Data science
  1. A new algorithm can speed up 3-D protein structure discovery by a lot, making research faster and more efficient.
  2. Bob Ross's artwork used a consistent style that can be analyzed statistically, showing how data can help us understand artistic patterns.
  3. Automation is becoming important in data science, helping to choose and evaluate machine learning models more easily.

Data Science Weekly - Issue 74

Data Science Weekly Newsletter • 19 implied HN points • 23 Apr 15
🕹 Technology Data science
  1. Neural networks are becoming more effective, thanks to advances in distributed computing systems. This means they can now perform better in various applications.
  2. Algorithms can influence many aspects of our lives, and there's a need for more human-centered algorithm designs. We should think about creating algorithms that support our needs.
  3. Training in data science is important for those wanting to enter the field. Programs like workshops can provide essential skills and mentorship from experienced professionals.

Data Science Weekly - Issue 73

Data Science Weekly Newsletter • 19 implied HN points • 16 Apr 15
🕹 Technology Data science
  1. Dr. Andrew Ng is a key figure in artificial intelligence and leads research at Baidu, focusing on technologies like image recognition and speech recognition.
  2. Airbnb uses machine learning to better understand what hosts prefer, helping match guests with suitable accommodations based on hosts' past choices.
  3. Amazon is making machine learning easier to use for everyone, aiming to help non-experts develop and utilize machine learning models.

Data Science Weekly - Issue 72

Data Science Weekly Newsletter • 19 implied HN points • 09 Apr 15
🕹 Technology Data science
  1. Creating a data-driven organization can take time and requires dedication, as seen in Warby Parker's journey.
  2. Machine learning is being used effectively in large companies like American Express to improve their services and handle big data.
  3. Visual tools and tutorials can help people learn how to analyze large data sets more easily, like using Excel.

Data Science Weekly - Issue 71

Data Science Weekly Newsletter • 19 implied HN points • 02 Apr 15
🕹 Technology Data science
  1. Convolutional Networks can be easily tricked into misclassifying images with small changes that are not noticeable to humans.
  2. Hiring great data scientists involves understanding their unique backgrounds and how they can contribute to different fields.
  3. Using data in retail can greatly improve decisions on pricing, discounts, and recommendations to meet customer needs.

Data Science Weekly - Issue 70

Data Science Weekly Newsletter • 19 implied HN points • 26 Mar 15
🕹 Technology Data science
  1. Data science is more than just algorithms; real-world applications require a broad set of skills. Understanding the context and how to deal with data is crucial.
  2. Computer vision can be fooled by certain images, which raises important security concerns. This highlights the need for ongoing research in making AI more reliable.
  3. Breaking into data science can be tough because interviews often cover a wide range of topics. It's important to prepare for both programming and statistics in your job search.

Data Science Weekly - Issue 69

Data Science Weekly Newsletter • 19 implied HN points • 19 Mar 15
🕹 Technology Data science
  1. Data science projects need a clear focus on solving the right problems. It's important to check if the data is suitable and avoid hidden biases.
  2. Having technical skills like Python or R isn't enough to land a data science job. It's also helpful to learn new tools that are in demand, like BI software.
  3. Machine learning combines technology with creative thinking. Understanding how it works can give valuable insights into how we interpret data and make decisions.

Data Science Weekly - Issue 68

Data Science Weekly Newsletter • 19 implied HN points • 12 Mar 15
🕹 Technology Data science
  1. Deep learning is being used by companies like PayPal to better fight fraud. They use innovative techniques to stay ahead of clever criminals.
  2. Data scientists can make a big impact in medicine by using their skills to understand complex data about health. Their work helps in making better decisions and discoveries in the field.
  3. Algorithms are increasingly being used to predict behaviors and outcomes based on large amounts of data. It's important to consider whether this is helping or complicating our lives.

Data Science Weekly - Issue 67

Data Science Weekly Newsletter • 19 implied HN points • 05 Mar 15
🕹 Technology Data science
  1. Flickr uses deep learning to automatically label images, which helps with the huge number of daily uploads. This shows how technology can improve organization and accessibility of visual data.
  2. Data visualization is becoming essential in journalism, as it helps tell stories more effectively than traditional text and images. This shift is changing the way information is communicated to the public.
  3. Machine learning is being applied in drug discovery, showing its potential to find effective treatments for various diseases. This highlights how data science can make a significant impact on health and medicine.

Data Science Weekly - Issue 66

Data Science Weekly Newsletter • 19 implied HN points • 26 Feb 15
🕹 Technology Data science
  1. Machine learning has a rich history with key figures contributing significantly to its development. Understanding this history helps us appreciate how far the field has come.
  2. The rise of superhuman machine intelligence is viewed as a serious threat to humanity. It’s important to consider the implications of creating powerful AI systems.
  3. Data scientists are increasingly using big data to tackle real-world problems, like fraud detection and food pairing. This shows how data can lead to new insights and solutions.

Data Science Weekly - Issue 65

Data Science Weekly Newsletter • 19 implied HN points • 19 Feb 15
🕹 Technology Data science
  1. Researchers are using neural networks based on monkey brains to help recognize human faces better. This approach shows how similar our brain processes can be to those of monkeys.
  2. Automating data analysis might make things easier for companies. New software can find patterns in data and create reports, which can save time and improve decision-making.
  3. Robo-advisers are changing how people invest their money. They are becoming popular for managing wealth and could change the financial industry significantly.

Data Science Weekly - Issue 64

Data Science Weekly Newsletter • 19 implied HN points • 12 Feb 15
🕹 Technology Data science
  1. There are algorithms that can recognize beauty in portraits, showing how technology can analyze aesthetic qualities. This could change how we view photography and art.
  2. Machine learning isn't just for tech; it can help in fields like journalism and social work, making tasks easier and spreading important information.
  3. You don't need heavy math skills to be a data analyst. There are many roles where you can contribute without being a math expert.

OLTP vs OLAP: The Core of Data Miscommunication

Data Products • 2 HN points • 23 Jun 23
🕹 Technology Data science
  1. The difference between OLTP and OLAP systems can cause miscommunication among data producers and consumers.
  2. OLTP systems focus on serving end users quickly with specific product features, while OLAP systems handle complex analytics by scanning large amounts of data.
  3. Empathy and communication between OLTP and OLAP teams are crucial to building scalable data products.

Data Science Weekly - Issue 63

Data Science Weekly Newsletter • 19 implied HN points • 05 Feb 15
🕹 Technology Data science
  1. Visual mapping helps understand the fast-growing communities on platforms like Twitch. It's a fun way to see how different groups connect.
  2. Data science can offer new ways to evaluate business risks, making it easier for startups to succeed. Using data helps to make better decisions.
  3. In data science portfolios, quality is often more important than quantity. Employers want to see impactful work rather than just a long list of projects.

Data Science Weekly - Issue 62

Data Science Weekly Newsletter • 19 implied HN points • 29 Jan 15
🕹 Technology Data science
  1. Machine learning is getting more important for businesses, especially as they deal with bigger data sets. Companies need to improve how they analyze data to stay competitive.
  2. A strong portfolio is key for landing a data science job. Showing off relevant skills in a well-organized way can really help you stand out to employers.
  3. Data science knowledge is becoming essential across different fields. Professionals are seeing high demand and good pay, making it a smart career choice for many.

Data Science Weekly - Issue 61

Data Science Weekly Newsletter • 19 implied HN points • 22 Jan 15
🕹 Technology Data science
  1. Deep learning is really effective, as shown in a talk by Yann LeCun, the head of Facebook AI Research. It's a big part of how we process data today.
  2. Choosing between Python and R for data jobs can be tricky. Both programming languages have their strengths, so it helps to know what you want to do beforehand.
  3. Data science jobs have different levels like junior, mid-level, and senior. It's important to understand these levels when applying for jobs in this field.

Data Science Weekly - Issue 60

Data Science Weekly Newsletter • 19 implied HN points • 15 Jan 15
🕹 Technology Data science
  1. R programming is gaining more popularity in data analysis. Many companies are using it for their projects and applications.
  2. Machine learning can help detect fraud in real-time transactions. Stripe has developed a system that blocks many fraudulent charges before they happen.
  3. Data visualization is essential for understanding complex information. A good example is a graphic that shows population density across different cities in detail.

Data Science Weekly - Issue 59

Data Science Weekly Newsletter • 19 implied HN points • 08 Jan 15
🕹 Technology Data science
  1. Nvidia is showcasing cool technology that lets computers recognize objects in real-time using deep learning.
  2. There's a new field emerging that focuses on how humans interact with data, emphasizing the need for better ethics in data use.
  3. Creating a strong data science portfolio is important, and there are many project ideas and techniques you can use to get started.

Data Science Weekly - Issue 58

Data Science Weekly Newsletter • 19 implied HN points • 01 Jan 15
🕹 Technology Data science
  1. Data science is becoming essential across many industries like sports, retail, and healthcare, driving innovation and insights.
  2. Understanding the difference between correlation and causation is challenging, and researchers are still figuring out how to measure the real impact of certain actions, like changing a coach.
  3. New programming languages and techniques, like Julia and knowledge distillation for deep learning models, are improving how we approach data science and artificial intelligence.

Data Science Weekly - Issue 57

Data Science Weekly Newsletter • 19 implied HN points • 25 Dec 14
🕹 Technology Data science
  1. There are many great resources available to learn about data science. It can be helpful to start with recommended websites, books, and helpful tools.
  2. Data scientists are in high demand, with companies looking for specific skills like R, Python, and SQL. Knowing the right tools can give you an edge in getting a job.
  3. Big data is impacting various fields, including music and sports. Understanding how to analyze this data can lead to fresh insights and opportunities.

Data Science Weekly - Issue 55

Data Science Weekly Newsletter • 19 implied HN points • 11 Dec 14
🕹 Technology Data science
  1. Books can be great gifts, especially the one called 'Data Scientists At Work' which offers insights from leading experts.
  2. Machine learning is evolving, and understanding its challenges, like how deep neural networks can be misled, is important.
  3. Conducting experiments, like those at companies such as Airbnb, helps improve decision-making in business and can teach valuable lessons.

Data Science Weekly - Issue 54

Data Science Weekly Newsletter • 19 implied HN points • 04 Dec 14
🕹 Technology Data science
  1. Learning from mistakes in data science can help improve future projects. It's important to know what to avoid.
  2. Open data can change how we see and interact with our cities. With the right insights, people can push for better policies.
  3. New technology in big data is being used for good causes, including environmental conservation. Data can play a big role in saving the planet.

Data Science Weekly - Issue 53

Data Science Weekly Newsletter • 19 implied HN points • 27 Nov 14
🕹 Technology Data science
  1. Teaching creativity through programming can be fun, as shown by a class project where students made Twitter bots.
  2. Research from Yahoo Labs helps us understand creativity in short videos like Vine, revealing new ways to analyze creative content.
  3. Using social media data can provide insights into complex topics, like unemployment trends, in a more cost-effective way than traditional methods.

Data Science Weekly - Issue 52

Data Science Weekly Newsletter • 19 implied HN points • 20 Nov 14
🕹 Technology Data science
  1. Personalized recommendations are really important in online shopping because they help customers discover products they might like and give sellers more exposure.
  2. Combining different techniques in data science can create powerful tools, like using machine learning and crowd input together to improve classification models.
  3. AI should be seen as a helpful tool rather than a danger; we should focus on how to use it positively instead of worrying about potential threats.

Data Science Weekly - Issue 51

Data Science Weekly Newsletter • 19 implied HN points • 13 Nov 14
🕹 Technology Data science
  1. Data science often blends different fields like statistics and machine learning. This combination helps us solve complex problems and make better predictions.
  2. Understanding both text and images is key to getting a complete view of information. Analyzing them together gives us a clearer picture of reality.
  3. There's a strong demand for data scientists, and many companies struggle to find qualified candidates. This shows how important this skill set is becoming in today's job market.

Data Science Weekly - Issue 50

Data Science Weekly Newsletter • 19 implied HN points • 06 Nov 14
🕹 Technology Data science
  1. Learning about neural networks can start from the basics before diving into complex topics. It's helpful to understand the core concepts first.
  2. Visualizing data is important for understanding text data better. There are interactive tools available that can help with this.
  3. Choosing the right statistical analysis method is crucial for data science. There are guides that can help you figure out which analysis to use based on your data.

Data Science Weekly - Issue 49

Data Science Weekly Newsletter • 19 implied HN points • 30 Oct 14
🕹 Technology Data science
  1. Getting into data science can be tricky, especially for those coming from academia. It's helpful to have guidance on how to make that transition.
  2. Machine learning can be used to identify negative behaviors online, which demonstrates the power of data science in addressing social issues.
  3. Trusting data sources too much can lead to problems. It's important to be skeptical and question how the data is collected and used.

Data Science Weekly - Issue 48

Data Science Weekly Newsletter • 19 implied HN points • 23 Oct 14
🕹 Technology Data science
  1. Deep learning is making exciting advancements, like AI mastering games such as Space Invaders in remarkable ways.
  2. Companies like Disney are using supercomputers to handle complex tasks in animated films, showing how tech can manage big projects.
  3. Data science is being used in various industries, including news organizations, to analyze data for better decision-making and audience engagement.

Data Science Weekly - Issue 47

Data Science Weekly Newsletter • 19 implied HN points • 16 Oct 14
🕹 Technology Data science
  1. Data science can help improve services, like reducing fraud in microfinance, showing its real-world impact.
  2. Mathematical models can predict disease outbreaks, but it's challenging to get them perfectly accurate.
  3. Machine learning tools, like those in Google Sheets, are making it easier to analyze data and make predictions.

Data Science Weekly - Issue 46

Data Science Weekly Newsletter • 19 implied HN points • 09 Oct 14
🕹 Technology Data science
  1. Machine learning is now a central part of data science, similar to the role algorithms played in computing 15 years ago. It's becoming essential for many fields.
  2. Deep learning has made significant advancements, especially in tasks like speech recognition and handwriting recognition. This technology is becoming a go-to for complex pattern recognition.
  3. Data science is not just about numbers; it involves understanding human behavior and data that relates to people. Many data scientists focus on human data for their work.