Data Science Weekly Newsletter

The Data Science Weekly Newsletter provides detailed insights on data science, machine learning, AI, and data engineering. It covers trends, tools, practical applications, and industry developments, emphasizing data quality, visualization, AI ethics, and career tips. Interviews and updates on evolving technologies are also highlighted.

Data Science Machine Learning Artificial Intelligence Data Engineering Data Visualization AI Ethics Career Development Data Tools and Techniques

The hottest Substack posts of Data Science Weekly Newsletter

And their main takeaways

Data Science Weekly - Issue 92

19 implied HN points • 27 Aug 15
🕹 Technology Data science Machine Learning Big Data Algorithms Statistics
  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

19 implied HN points • 20 Aug 15
🕹 Technology AI Data science Machine Learning Statistics
  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

19 implied HN points • 13 Aug 15
🕹 Technology Data science Machine Learning Artificial Intelligence Programming Statistics
  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 88

19 implied HN points • 30 Jul 15
🕹 Technology Data science Machine Learning Programming Statistical Analysis Artificial Intelligence
  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.
Get a weekly roundup of the best Substack posts, by hacker news affinity:

Data Science Weekly - Issue 87

19 implied HN points • 23 Jul 15
🕹 Technology Machine Learning Data science Artificial Intelligence Programming 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 86

19 implied HN points • 16 Jul 15
🕹 Technology Data science Machine Learning Artificial Intelligence Software Development Data Visualization
  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

19 implied HN points • 09 Jul 15
🕹 Technology Data science Machine Learning Artificial Intelligence Data Analysis Statistics
  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.

Data Science Weekly - Issue 84

19 implied HN points • 02 Jul 15
🕹 Technology Data science Machine Learning Artificial Intelligence Deep Learning Predictive Analytics
  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

19 implied HN points • 25 Jun 15
🕹 Technology Data science Machine Learning AI Analytics Computing
  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

19 implied HN points • 18 Jun 15
🕹 Technology Data science Machine Learning Artificial Intelligence Big Data 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 81

19 implied HN points • 11 Jun 15
🕹 Technology Data science Machine Learning Artificial Intelligence Big Data Computer Vision
  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

19 implied HN points • 04 Jun 15
🕹 Technology Data science Machine Learning Artificial Intelligence Predictive Analytics Algorithms
  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.

Data Science Weekly - Issue 79

19 implied HN points • 28 May 15
🕹 Technology Data science Machine Learning Artificial Intelligence Neural Networks Image Recognition
  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

19 implied HN points • 21 May 15
🕹 Technology Data science Machine Learning Algorithms Analytics Software Development
  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

19 implied HN points • 14 May 15
🕹 Technology Data science Machine Learning Software Development Statistics Artificial Intelligence
  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

19 implied HN points • 07 May 15
🕹 Technology Data science Machine Learning Algorithms Artificial Intelligence 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 75

19 implied HN points • 30 Apr 15
🕹 Technology Data science Machine Learning Algorithms Statistics Automation
  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

19 implied HN points • 23 Apr 15
🕹 Technology Data science Machine Learning Artificial Intelligence Software Engineering Big Data
  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

19 implied HN points • 16 Apr 15
🕹 Technology Machine Learning Data science Artificial Intelligence Algorithms Big Data
  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

19 implied HN points • 09 Apr 15
🕹 Technology Data science Machine Learning Artificial Intelligence Big Data Data Visualization
  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

19 implied HN points • 02 Apr 15
🕹 Technology Data science Artificial Intelligence Machine Learning Analytics Big Data
  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

19 implied HN points • 26 Mar 15
🕹 Technology Data science Machine Learning Artificial Intelligence Computer Vision Quantitative Analysis
  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

19 implied HN points • 19 Mar 15
🕹 Technology Data science Machine Learning Artificial Intelligence Big Data 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

19 implied HN points • 12 Mar 15
🕹 Technology Data science Machine Learning Artificial Intelligence Algorithms 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 67

19 implied HN points • 05 Mar 15
🕹 Technology Data science Machine Learning Data Visualization AI Statistics
  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

19 implied HN points • 26 Feb 15
🕹 Technology Data science Machine Learning Artificial Intelligence Big Data Ethics
  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

19 implied HN points • 19 Feb 15
🕹 Technology Data science AI Machine Learning Analytics Software Development
  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

19 implied HN points • 12 Feb 15
🕹 Technology Machine Learning Data science Artificial Intelligence Statistics Algorithms
  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.

Data Science Weekly - Issue 63

19 implied HN points • 05 Feb 15
🕹 Technology Data science Machine Learning AI Algorithms Programming
  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

19 implied HN points • 29 Jan 15
🕹 Technology Data science Machine Learning Artificial Intelligence Software Development Data Analysis
  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

19 implied HN points • 22 Jan 15
🕹 Technology Data science Machine Learning Programming Artificial Intelligence Software Development
  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

19 implied HN points • 15 Jan 15
🕹 Technology Data science Machine Learning Artificial Intelligence Programming 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 59

19 implied HN points • 08 Jan 15
🕹 Technology Data science Machine Learning Artificial Intelligence Big Data Statistics
  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

19 implied HN points • 01 Jan 15
🕹 Technology Data science Machine Learning AI Programming 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 57

19 implied HN points • 25 Dec 14
🕹 Technology Data science Machine Learning Artificial Intelligence Data Analysis Big Data
  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

19 implied HN points • 11 Dec 14
🕹 Technology Data science Machine Learning Artificial Intelligence E-commerce Programming
  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

19 implied HN points • 04 Dec 14
🕹 Technology Data science Machine Learning Artificial Intelligence Big Data 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 53

19 implied HN points • 27 Nov 14
🕹 Technology Data science Machine Learning Programming Statistics Innovation
  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.