The hottest Data science Substack posts right now

And their main takeaways
Category
Top Technology Topics

Data Science Weekly - Issue 123

Data Science Weekly Newsletter • 19 implied HN points • 31 Mar 16
🕹 Technology Data science
  1. Stories can help us understand the world, but not all stories are true. It's important to know when to trust our explanations and when to question them.
  2. Data science is vital for companies like Airbnb because it helps integrate analytics into leadership decisions. This shows how data can shape business strategies.
  3. Predictive data can enhance safety, like how Baidu uses map searches to forecast crowd behavior. It demonstrates how technology can help manage real-world situations.

Data Science Weekly - Issue 122

Data Science Weekly Newsletter • 19 implied HN points • 24 Mar 16
🕹 Technology Data science
  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 121

Data Science Weekly Newsletter • 19 implied HN points • 17 Mar 16
🕹 Technology Data science
  1. A new AI with 30 years of knowledge is finally ready to be used in the real world. This shows how far AI has come in understanding and processing information.
  2. There's a new effort to monitor police behavior using algorithms to predict misconduct. This technology aims to improve police interactions with the public.
  3. Using pie charts can be misleading; better alternatives exist for visualizing data. There are effective ways to present statistics that make information clearer.

Data Science Weekly - Issue 119

Data Science Weekly Newsletter • 19 implied HN points • 03 Mar 16
🕹 Technology Data science
  1. Data science can reveal hidden insights, like analyzing the language used in presidential debates to understand candidates better.
  2. AI is becoming more creative, as seen when Google's AI sold art for charity, showing its ability to create valuable pieces.
  3. Social media data can tell interesting stories, like an interactive map of Instagram posts in Hong Kong which shows the city's life based on user activity.

Data Science Weekly - Issue 118

Data Science Weekly Newsletter • 19 implied HN points • 25 Feb 16
🕹 Technology Data science
  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.
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Data Science Weekly - Issue 117

Data Science Weekly Newsletter • 19 implied HN points • 18 Feb 16
🕹 Technology Data science
  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
  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
  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 114

Data Science Weekly Newsletter • 19 implied HN points • 28 Jan 16
🕹 Technology Data science
  1. Machine learning can help machines understand human emotions by analyzing brain waves. This is a significant advancement in how we can interpret feelings through technology.
  2. Owen Zhang, a top data scientist, highlights the importance of learning from practical experiences in transitioning into data science from other tech roles.
  3. Kaggle projects are a good way to practice data skills, but may not be the best evidence of expertise for job applications. It's important to showcase diverse experiences on your resume.

Data Science Weekly - Issue 113

Data Science Weekly Newsletter • 19 implied HN points • 21 Jan 16
🕹 Technology Data science
  1. Analyzing different State of the Union addresses can reveal changes in language and topics over time. It's interesting to see how leaders communicate their ideas.
  2. Video games can be very useful for developing artificial intelligence. They provide specific challenges that help researchers create better AI solutions.
  3. There's a growing interest in Bayesian methods among R users, thanks to new tools that make these techniques easier to adopt. This could change how many people approach data analysis.

Data Science Weekly - Issue 112

Data Science Weekly Newsletter • 19 implied HN points • 14 Jan 16
🕹 Technology Data science
  1. The value of information is important in decision-making. Knowing how much to pay for good information can help you make better choices.
  2. AI is getting better at understanding humor. It was thought machines couldn't grasp humor, but advancements are changing that view.
  3. Participating in hackathons can fast-track your learning. Working with others on projects can teach you more than studying alone for months.

Data Science Weekly - Issue 111

Data Science Weekly Newsletter • 19 implied HN points • 07 Jan 16
🕹 Technology Data science
  1. Using machine learning can create fun things, like generating levels for video games. It's a cool way to combine tech and entertainment.
  2. Too much agreement in a decision-making process can sometimes indicate problems. It’s important to question even unanimous decisions to avoid errors.
  3. Understanding different algorithms behind systems like Netflix's recommendations can help us see the business value of data science. It shows how data can drive decisions in companies.

Data Science Weekly - Issue 110

Data Science Weekly Newsletter • 19 implied HN points • 31 Dec 15
🕹 Technology Data science
  1. Some websites offer tools and training to help you create quick data visualizations, which can be really useful if you're learning to use D3.js.
  2. It's important to highlight your personal projects on your data science resume, as they can showcase your skills and practical experience.
  3. There are many interesting articles and studies out there about data's role in health, global warming, and machine learning that can deepen your understanding of these topics.

Data Science Weekly - Issue 109

Data Science Weekly Newsletter • 19 implied HN points • 24 Dec 15
🕹 Technology Data science
  1. Children are amazing learners, and studying how they learn can help improve machine learning methods.
  2. There's a big surge in scientists promoting their work, which may lead to exaggerated claims in research.
  3. Using data to analyze behavior, like how people introduce others in emails, can reveal interesting social patterns.

Data Science Weekly - Issue 108

Data Science Weekly Newsletter • 19 implied HN points • 17 Dec 15
🕹 Technology Data science
  1. Data science is being applied in creative ways, like analyzing rap lyrics to see what makes a hit song. It's cool to see data being used to explore music trends!
  2. Recent advances in AI are allowing machines to perform vision tasks better than humans, showing how fast technology is evolving.
  3. Understanding the differences between jobs in data science, like data scientists and machine learning engineers, can help people find the best fit for their skills.

Data Science Weekly - Issue 107

Data Science Weekly Newsletter • 19 implied HN points • 10 Dec 15
🕹 Technology Data science
  1. An algorithm can identify influential universities based on Wikipedia data, revealing some unexpected rankings.
  2. Many commonly accepted rules in statistics might not hold true, and it's crucial to question them.
  3. Machine learning can lead to significant maintenance costs despite offering quick results, known as technical debt.

Data Science Weekly - Issue 106

Data Science Weekly Newsletter • 19 implied HN points • 03 Dec 15
🕹 Technology Data science
  1. A new gadget can listen to sounds and vibrations to diagnose problems with air conditioners. This technology helps to identify mechanical issues without needing to open the machine.
  2. Wikipedia is using AI to improve how it reviews changes made by editors. This system will help detect problematic revisions automatically, making the editorial process smoother.
  3. There are common mistakes people make when writing data science resumes. It's important to avoid these pitfalls to increase your chances of landing job interviews.

Data Science Weekly - Issue 105

Data Science Weekly Newsletter • 19 implied HN points • 26 Nov 15
🕹 Technology Data science
  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
  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
  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 102

Data Science Weekly Newsletter • 19 implied HN points • 05 Nov 15
🕹 Technology Data science
  1. There are many ghost cities in China due to overdevelopment, which became clear through data mining techniques.
  2. The debate between programming languages like Python and R can distract from more important issues in the data science community.
  3. Research using social media data, like Instagram, can uncover trends such as teenage drinking that traditional surveys might miss.

Data Science Weekly - Issue 101

Data Science Weekly Newsletter • 19 implied HN points • 29 Oct 15
🕹 Technology Data science
  1. Deep neural networks can identify various elements in images, showing their usefulness in both serious applications and fun experiments.
  2. Machine learning can be effectively used in practical applications like estimating delivery times, demonstrating its potential in real-world scenarios.
  3. There's an ongoing ethical debate about how self-driving cars should be programmed, particularly regarding their decision-making in life-and-death situations.

Data Science Weekly - Issue 100

Data Science Weekly Newsletter • 19 implied HN points • 22 Oct 15
🕹 Technology Data science
  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 99

Data Science Weekly Newsletter • 19 implied HN points • 15 Oct 15
🕹 Technology Data science
  1. Scientists can use tweets to detect earthquakes very quickly, even faster than some official sources.
  2. Algorithms could potentially help treat diseases in the future, much like they currently recommend movies or products.
  3. Machine learning has many uses in finance, helping companies manage and analyze data effectively.

Data Science Weekly - Issue 98

Data Science Weekly Newsletter • 19 implied HN points • 08 Oct 15
🕹 Technology Data science
  1. Data science can help social good organizations, but they need more than just good intentions to make a real impact. Following certain principles can help these efforts succeed.
  2. The random walk hypothesis is a way to explore market behaviors and randomness. Understanding it can help in analyzing financial markets more effectively.
  3. Teaching statistics can be challenging, and it's important to make it easier for students. If students find it complicated, educators should look at their teaching methods.

Data Science Weekly - Issue 97

Data Science Weekly Newsletter • 19 implied HN points • 01 Oct 15
🕹 Technology Data science
  1. A new model using health records can predict if patients will be at home, hospitalized, or dead within a week of being admitted. It's impressive how it combines different patient data for better accuracy.
  2. Google's DeepMind AI is getting really good at video games, beating humans in 31 of them. But surprisingly, it still struggles with classic games like Pac-Man.
  3. Adaptive learning is changing how machines and humans learn together. This new wave could lead to smarter systems that can adapt in real-time.

Data Science Weekly - Issue 96

Data Science Weekly Newsletter • 19 implied HN points • 24 Sep 15
🕹 Technology Data science
  1. Job hunting in data science can be really stressful, even for the most confident candidates. It's important to talk about it and share experiences to help each other.
  2. Learning to find patterns in how data scientists work can make the job easier. This means using tools to enhance our own decision-making processes.
  3. When interviewing for data science roles, showcasing business knowledge is just as crucial as proving your technical skills. Understanding how data impacts businesses can set you apart.

Data Science Weekly - Issue 95

Data Science Weekly Newsletter • 19 implied HN points • 17 Sep 15
🕹 Technology Data science
  1. Artificial intelligence is growing and changing rapidly, with experts like Eric Schmidt discussing its future impacts.
  2. There are innovative uses of machine learning, like generating music and analyzing large datasets, showing its versatility across different fields.
  3. Resources for learning, such as cheat sheets and books on machine learning, can help anyone interested in diving deeper into data science.

Data Science Weekly - Issue 94

Data Science Weekly Newsletter • 19 implied HN points • 10 Sep 15
🕹 Technology Data science
  1. Data science combines skills from statistics and computer science to analyze and interpret complex data. It's a growing field that's seen as crucial for modern businesses.
  2. Neural networks are important in deep learning, allowing computers to identify patterns and make predictions. They can be complex but are essential for many applications like image and speech recognition.
  3. Understanding foundational topics, like probability and linear algebra, is key for anyone wanting to succeed in data science. There are plenty of resources available to help learn these subjects.

Data Science Weekly - Issue 93

Data Science Weekly Newsletter • 19 implied HN points • 03 Sep 15
🕹 Technology Data science
  1. Artificial intelligence can create stunning artwork, using deep learning to mimic famous styles. This technology opens new doors for creativity and raises questions about artistic ownership.
  2. Machine learning is becoming essential in the sharing economy to optimize pricing strategies, like those used by Airbnb. Smart algorithms help businesses set prices that reflect demand more accurately.
  3. Deep learning is drastically improving computational processes, making tasks like training neural networks much faster. This helps expand the potential applications of AI in various fields.

Data Science Weekly - Issue 92

Data Science Weekly Newsletter • 19 implied HN points • 27 Aug 15
🕹 Technology Data science
  1. Google is developing new algorithms, called 'Thought Vectors,' that could allow computers to understand logic and have natural conversations.
  2. There's an article showing how data can prove which songs from the 90s remain timeless by comparing their Spotify plays over the years.
  3. Machine learning and statistics aim to solve similar problems but use different methods, highlighting the important distinctions between the two fields.

Data Science Weekly - Issue 91

Data Science Weekly Newsletter • 19 implied HN points • 20 Aug 15
🕹 Technology Data science
  1. Artificial Intelligence is growing fast, with 855 companies and $8.75 billion in funding. It plays a big role in different markets today.
  2. Principal Component Analysis can help analyze images, like fashion designs, by breaking them down into key features. This technique is useful in various fields.
  3. Data science can assist in city planning by using data to revitalize struggling neighborhoods. This approach helps cities manage resources better.

Data Science Weekly - Issue 90

Data Science Weekly Newsletter • 19 implied HN points • 13 Aug 15
🕹 Technology Data science
  1. Sorting algorithms can be visualized in a fun way through animations, making it easier to understand how they work.
  2. AI tools, like Baidu's medical robot, can help provide quick health advice based on symptoms, improving access to healthcare.
  3. Machine learning techniques are being used in diverse fields, from predicting wine prices to improving speech recognition systems.

Data Science Weekly - Issue 89

Data Science Weekly Newsletter • 19 implied HN points • 06 Aug 15
🕹 Technology Data science
  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 88

Data Science Weekly Newsletter • 19 implied HN points • 30 Jul 15
🕹 Technology Data science
  1. Hadley Wickham is a famous statistician known for his work with R, a programming language. He has made a big impact in the stats community, and people admire his contributions.
  2. Computers are moving beyond just calculations; they can now assess human character. This development raises questions about how we see technology's role in our lives.
  3. The concept of Dropout is key in modern neural networks, and there are simple ways to implement it in Python. Learning this can help improve machine learning projects.

Data Science Weekly - Issue 87

Data Science Weekly Newsletter • 19 implied HN points • 23 Jul 15
🕹 Technology Data science
  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.

Machine learning is not just statistics

The Palindrome • 2 implied HN points • 09 Aug 23
🔬 Science Data science
  1. Machine learning heavily utilizes statistics, but it is not just applied statistics.
  2. Probability enables reasoning about uncertainty, while statistics quantifies and explains it.
  3. Probability theory provides tools to deal with missing information and formulate models with likelihood measures.

Data Science Weekly - Issue 86

Data Science Weekly Newsletter • 19 implied HN points • 16 Jul 15
🕹 Technology Data science
  1. A simple neural network can be built in just 11 lines of Python code, showcasing how backpropagation works in machine learning.
  2. There's interesting data visualization in sports that shows how team performance changes over time, affecting how we view their success.
  3. Data science can be used for social good, and there are many ways to get involved in projects that make a positive impact on the world.

Data Science Weekly - Issue 85

Data Science Weekly Newsletter • 19 implied HN points • 09 Jul 15
🕹 Technology Data science
  1. PhD candidates often struggle to apply for data science jobs, but understanding industry expectations can help them succeed.
  2. AI tools are evolving quickly, with projects teaching machines to analyze and classify complex data, like galaxy images and social media content.
  3. There's a growing need for data scientists to address big issues, like obesity, by using available health data to create innovative solutions.