The hottest Analytics Substack posts right now

And their main takeaways
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Top Technology Topics

Data Science Weekly - Issue 122

Data Science Weekly Newsletter • 19 implied HN points • 24 Mar 16
🕹 Technology Analytics
  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 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 Analytics
  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 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 Analytics
  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.
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Data Science Weekly - Issue 105

Data Science Weekly Newsletter • 19 implied HN points • 26 Nov 15
🕹 Technology 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 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 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.

Data Science Weekly - Issue 100

Data Science Weekly Newsletter • 19 implied HN points • 22 Oct 15
🕹 Technology Analytics
  1. Tesla is using advanced machine learning to improve its autopilot technology for self-driving cars.
  2. MIT has created a system that automates big data analysis, outperforming many human teams in competitions.
  3. Data science helps cities become smarter and more efficient, which is crucial as more people move to urban areas.

Data Science Weekly - Issue 89

Data Science Weekly Newsletter • 19 implied HN points • 06 Aug 15
🕹 Technology Analytics
  1. Machine learning can reflect human biases, and we need to talk about how we use it responsibly.
  2. AI can be creative too, not just about solving math problems; it can make art and music based on patterns.
  3. Preparing for a data science training program is important to succeed and achieve your career goals.

Data Science Weekly - Issue 87

Data Science Weekly Newsletter • 19 implied HN points • 23 Jul 15
🕹 Technology Analytics
  1. Machine learning is a powerful tool that helps companies boost revenue and engagement. Big names like Google and Amazon use it to improve their services.
  2. There are tools and methods to analyze stories using sentiment and data models. These can help summarize the emotions and shapes of narratives in books and movies.
  3. Online resources and workshops are available for those wanting to learn data science. They provide hands-on experience and mentorship to help you get started.

Data Science Weekly - Issue 83

Data Science Weekly Newsletter • 19 implied HN points • 25 Jun 15
🕹 Technology Analytics
  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 Analytics
  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 78

Data Science Weekly Newsletter • 19 implied HN points • 21 May 15
🕹 Technology Analytics
  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 76

Data Science Weekly Newsletter • 19 implied HN points • 07 May 15
🕹 Technology Analytics
  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 71

Data Science Weekly Newsletter • 19 implied HN points • 02 Apr 15
🕹 Technology Analytics
  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 69

Data Science Weekly Newsletter • 19 implied HN points • 19 Mar 15
🕹 Technology Analytics
  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 Analytics
  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 65

Data Science Weekly Newsletter • 19 implied HN points • 19 Feb 15
🕹 Technology Analytics
  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 60

Data Science Weekly Newsletter • 19 implied HN points • 15 Jan 15
🕹 Technology Analytics
  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 58

Data Science Weekly Newsletter • 19 implied HN points • 01 Jan 15
🕹 Technology Analytics
  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 54

Data Science Weekly Newsletter • 19 implied HN points • 04 Dec 14
🕹 Technology Analytics
  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 39

Data Science Weekly Newsletter • 19 implied HN points • 21 Aug 14
🕹 Technology Analytics
  1. Data cleaning and preparation is really important in data science, similar to carpentry work. It's about organizing and getting the data ready for analysis.
  2. AI can discover new insights in areas like art that even experts might miss. This shows how powerful machine learning can be in uncovering hidden connections.
  3. There are lots of resources available to learn data science, like tutorials and job opportunities. It's easier than ever to get started and find ways to apply your skills.

Data Science Weekly - Issue 30

Data Science Weekly Newsletter • 19 implied HN points • 19 Jun 14
🕹 Technology Analytics
  1. Different risk types need different machine learning setups, especially when some risks require quick action while others can be analyzed more slowly.
  2. E-commerce companies like Etsy use predictive machine learning to improve various important tasks, making their services more efficient.
  3. Netflix is focused on enhancing its streaming quality using data science and has formed a specialized team to work on innovative solutions for its users.

Data Science Weekly - Issue 29

Data Science Weekly Newsletter • 19 implied HN points • 12 Jun 14
🕹 Technology Analytics
  1. Data science is a popular and exciting field, with many people wanting to learn how to become a data scientist.
  2. Using analytical techniques, like regression discontinuity, can help understand complex issues, such as the impact of services like Uber on DUI rates.
  3. Specialized tools and libraries can offer better statistical analysis capabilities than standard math libraries, making them more appealing for statisticians.

Data Science Weekly - Issue 25

Data Science Weekly Newsletter • 19 implied HN points • 15 May 14
🕹 Technology Analytics
  1. Data scientists spend a lot of time on tasks beyond just building models. Cleaning data and analyzing it are just as important.
  2. Using reliable data is crucial because bad data can lead to incorrect conclusions. If your input is flawed, the output will be too.
  3. There's a growing trend in building businesses around machine learning APIs. It's all about automating processes and using these tools to create new opportunities.

Data Science Weekly - Issue 17

Data Science Weekly Newsletter • 19 implied HN points • 20 Mar 14
🕹 Technology Analytics
  1. Data science is being used to uncover important insights in political analysis, such as studying the speeches of leaders like President Obama.
  2. Deep learning is a rapidly growing field that could reshape the world of analytics and has attracted attention from major tech companies.
  3. There are ongoing debates about the best programming languages for data analysis, with R and Python being the top contenders among data scientists.

Data Science Weekly - Issue 16

Data Science Weekly Newsletter • 19 implied HN points • 13 Mar 14
🕹 Technology Analytics
  1. Data science jobs can be accessible, but it's important to have the right skills and knowledge. If you enjoy statistics and have a background in engineering, you might find opportunities in this field.
  2. Apache Spark is becoming very popular for handling big data and has real-world applications. Companies like Conviva and Yahoo are already using it to improve their systems.
  3. Team chemistry is essential for better performance in sports analytics. Understanding how different talents and skills blend can make a team more effective than just a group of individual stars.

Data Science Weekly - Issue 15

Data Science Weekly Newsletter • 19 implied HN points • 06 Mar 14
🕹 Technology Analytics
  1. Machine learning can be explained through clear visuals that make complex ideas easier to grasp.
  2. CART can be used effectively for predicting stock market directions by focusing on market biases.
  3. Apache Spark is a powerful tool for data scientists, offering features that support both investigative and operational analytics.

Data Science Weekly - Issue 12

Data Science Weekly Newsletter • 19 implied HN points • 13 Feb 14
🕹 Technology Analytics
  1. DataKind aims to use data science for social good, helping organizations make better decisions for humanity.
  2. Big companies like Netflix are using new algorithms and deep learning to improve product recommendations and services.
  3. Working together with computers can lead to better outcomes, instead of fearing that they will take over jobs.

Data Science Weekly - Issue 11

Data Science Weekly Newsletter • 19 implied HN points • 06 Feb 14
🕹 Technology Analytics
  1. Data visualization is important in data science, especially for large-scale projects. It helps people understand data flows and make better decisions.
  2. Bringing machine learning models from a lab to real-world applications is crucial for impact. This requires integrating tools and strategies to analyze data in production.
  3. Learning about user experience and changing tastes is key for making good product recommendations. It's important to consider what users will enjoy now and in the future.

Data Science Weekly - Issue 7

Data Science Weekly Newsletter • 19 implied HN points • 09 Jan 14
🕹 Technology Analytics
  1. Google has developed a smart neural network that can read house numbers in street views quickly and accurately, mixing tech with human-like skills.
  2. Neural networks and Machine Learning as a Service are becoming important tools for businesses, offering new ways to analyze data and make predictions.
  3. Platforms like Netflix use data in unique ways to classify movies, breaking them down into thousands of specific genres to better cater to viewer preferences.

Data Science Weekly - Issue 6

Data Science Weekly Newsletter • 19 implied HN points • 02 Jan 14
🕹 Technology Analytics
  1. Machine learning is becoming really popular in education and helps improve various fields, like online dating and data analysis. Many students at universities, like Stanford, are eager to learn about it.
  2. Deep learning models are advancing quickly, and some can now even beat human players in video games. This shows how powerful these technologies are getting.
  3. Data scientists need to have a mix of skills in business, math, and coding. This combination helps them solve problems and create better algorithms in the industry.

Data Science Weekly - Issue 4

Data Science Weekly Newsletter • 19 implied HN points • 19 Dec 13
🕹 Technology Analytics
  1. Data analysis can reveal surprising patterns, like how riders use Uber, by looking at location and time data.
  2. Machine learning is being used in innovative ways, such as predicting stock prices and improving email marketing, making processes smarter.
  3. Even in competitive sports like cycling, there's a gap in using data analytics effectively, despite having lots of available data.

Elevate the role of analytics in your company

petr@substack • 1 implied HN point • 13 Feb 24
💼 Business Analytics
  1. Many companies need to take analytics seriously, as analytics teams are often seen as second-class citizens, stuck in a negative spiral of underappreciation and lack of trust.
  2. To elevate the role of analytics in a company, data needs to be deployed into business-critical processes, creating a more impactful and essential role for analytics teams within the organization.
  3. Success in making data critical involves finding champions within the company, prioritizing technology quality, and incrementally increasing the criticality of data use cases, moving away from dashboards towards actionable workflows.

This email might break your inbox

Mehdeeka • 1 implied HN point • 19 Apr 23
💼 Business Analytics
  1. Top notch landing pages are hyper focused on getting the visitor to do one specific thing with no distractions.
  2. Good landing pages have consistent messaging with the advertisement that led the visitor to the page.
  3. Creating unique landing pages for different ads is worth the effort for better results, even if it requires more work.

What is the NFT Lore Layer?

skry • 0 implied HN points • 30 Mar 23
🔮 Crypto Analytics
  1. The NFT Lore Layer consists of on-chain assets, storytelling, and a sub-community.
  2. Lore Layer projects redefine the concept of a 'platform' in Web3 by leveraging existing communities without being tied to a specific platform.
  3. Advances in gaming technology are enabling the integration of gaming, storytelling, and community building in the Lore Layer framework.