The hottest Big Data Substack posts right now

And their main takeaways
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Top Technology Topics

Data Science Weekly - Issue 150

Data Science Weekly Newsletter • 19 implied HN points • 06 Oct 16
🕹 Technology Data science Machine Learning Artificial Intelligence Data Visualization Big Data
  1. Python has great tools for data visualization, like Altair. It's worth exploring these options to make your data stories clearer.
  2. Machine learning can be likened to deep frying; it might sound exciting but requires careful consideration about what you're working with. Understanding the underlying processes is key.
  3. Data analysis is an evolving field that aims to improve decision-making through experience and tools. We should keep learning to make better conclusions from our data.

Data Science Weekly - Issue 145

Data Science Weekly Newsletter • 19 implied HN points • 01 Sep 16
🕹 Technology Data science Artificial Intelligence Machine Learning Big Data Algorithms
  1. Voice recognition technology, like Siri, is having trouble understanding different regional accents, and people are changing how they speak to make it work better.
  2. Facebook decided to remove human editors from its Trending news section to eliminate bias, relying instead on algorithms to manage the content.
  3. Machine learning methods require careful debugging, and it's helpful to break down errors into different categories to effectively resolve issues in your algorithms.

Data Science Weekly - Issue 120

Data Science Weekly Newsletter • 19 implied HN points • 10 Mar 16
🕹 Technology Data science Machine Learning Artificial Intelligence Data Visualization Big Data
  1. Understanding what makes content go viral on platforms like Reddit can be tricky. People often know great posts when they see them, but predicting them is another story.
  2. The American Statistical Association has made a big change by saying no to p-values. This is a significant shift in the statistical community.
  3. Robots are getting better at learning through interaction, but they still have a long way to go to match human skills. Continuous learning and feedback can help them improve.

Data Science Weekly - Issue 110

Data Science Weekly Newsletter • 19 implied HN points • 31 Dec 15
🕹 Technology Data science Machine Learning Artificial Intelligence Statistics Big Data
  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 106

Data Science Weekly Newsletter • 19 implied HN points • 03 Dec 15
🕹 Technology Data science Artificial Intelligence Machine Learning Data Analytics Big Data
  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.
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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 102

Data Science Weekly Newsletter • 19 implied HN points • 05 Nov 15
🕹 Technology Data science Machine Learning Big Data Artificial Intelligence Data Visualization
  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 100

Data Science Weekly Newsletter • 19 implied HN points • 22 Oct 15
🕹 Technology Data science Machine Learning Big Data Artificial Intelligence 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 97

Data Science Weekly Newsletter • 19 implied HN points • 01 Oct 15
🕹 Technology Data science Machine Learning Artificial Intelligence Predictive Analytics Big Data
  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 95

Data Science Weekly Newsletter • 19 implied HN points • 17 Sep 15
🕹 Technology AI Machine Learning Data science Big Data Statistics
  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 93

Data Science Weekly Newsletter • 19 implied HN points • 03 Sep 15
🕹 Technology Data science Machine Learning Artificial Intelligence Algorithms Big Data
  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 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 82

Data Science Weekly Newsletter • 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

Data Science Weekly Newsletter • 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 74

Data Science Weekly Newsletter • 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

Data Science Weekly Newsletter • 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

Data Science Weekly Newsletter • 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

Data Science Weekly Newsletter • 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 69

Data Science Weekly Newsletter • 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 66

Data Science Weekly Newsletter • 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 59

Data Science Weekly Newsletter • 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 57

Data Science Weekly Newsletter • 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 54

Data Science Weekly Newsletter • 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 51

Data Science Weekly Newsletter • 19 implied HN points • 13 Nov 14
🕹 Technology Data science Machine Learning Artificial Intelligence Big Data Statistical Analysis
  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 48

Data Science Weekly Newsletter • 19 implied HN points • 23 Oct 14
🕹 Technology Data science Machine Learning Artificial Intelligence Big Data Computer Vision
  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 Machine Learning Artificial Intelligence Big Data Data Visualization
  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 45

Data Science Weekly Newsletter • 19 implied HN points • 02 Oct 14
🕹 Technology Data science Machine Learning Artificial Intelligence Big Data Statistics
  1. Data science is important for creating content that goes viral, as seen with BuzzFeed's strategies. Understanding what people like can help predict online trends.
  2. Machine learning can be used in real-world applications like gender detection on social media. This shows how technology can analyze and understand large amounts of user data.
  3. Making math education relevant is crucial. Teaching statistics first could help students understand data better and see its importance in everyday life.

Data Science Weekly - Issue 44

Data Science Weekly Newsletter • 19 implied HN points • 25 Sep 14
🕹 Technology Data science Machine Learning Big Data Artificial Intelligence Statistical Analysis
  1. There's a big data event called Strata Conference + Hadoop World happening in New York. It's a great place for anyone interested in data science and big data to learn and network.
  2. Many researchers are working on cool projects like predicting NYC taxi tips and detecting anomalies in building energy usage. These projects show the real-life applications of data science.
  3. There are various resources available for learning and improving skills in data science, including books, online courses, and articles. It's a good time to dive in and explore!

Data Science Weekly - Issue 41

Data Science Weekly Newsletter • 19 implied HN points • 04 Sep 14
🕹 Technology Data science Machine Learning Big Data Statistical Analysis Tech Conferences
  1. The Strata + Hadoop World event is a big deal for people in data science and business. It's a great place to connect and learn about using big data effectively.
  2. Using Bayesian models can help solve unique problems, like predicting where Uber riders are headed. This shows how math can be applied in real-world scenarios.
  3. Choosing the right data scientist for your team is crucial. A good hire can make a big difference, while a poor one can lead to costly mistakes.

Data Science Weekly - Issue 40

Data Science Weekly Newsletter • 19 implied HN points • 28 Aug 14
🕹 Technology Data science Machine Learning Artificial Intelligence Big Data Software Development
  1. Building an online resource like RoboBrain can help robots access important information and AI tools easily. This could make robots smarter and more capable.
  2. Data scientists are using vast amounts of data from major tech companies to improve fields like healthcare. This work shows how valuable data can be in solving real-world problems.
  3. Amazon's shopping data gives it a unique advantage for advertising. By knowing what people buy, Amazon can target ads more effectively than competitors like Google.

Data Science Weekly - Issue 38

Data Science Weekly Newsletter • 19 implied HN points • 14 Aug 14
🕹 Technology Data science Machine Learning Big Data Data Analytics Statistical Modeling
  1. Deep learning can be fun to explore, and there's a quick guide to help you get started with it.
  2. Data science skills are in high demand, so asking the right questions before a job offer is really important.
  3. There are great resources and tools out there for data visualization and machine learning to help you improve your skills.

Data Science Weekly - Issue 33

Data Science Weekly Newsletter • 19 implied HN points • 10 Jul 14
🕹 Technology Data science Machine Learning Big Data Statistical Analysis Data Visualization
  1. Random forests are a powerful tool in data science that can help understand how different parts of the algorithm work and improve its use.
  2. There are two main approaches to statistics: frequentism and Bayesianism, and they can lead to different solutions for data analysis problems.
  3. Data visualization is important for making complex information easier to understand, and there are many great tools available to help with this.

Data Science Weekly - Issue 32

Data Science Weekly Newsletter • 19 implied HN points • 03 Jul 14
🕹 Technology Data science Machine Learning Algorithms Visualization Big Data
  1. Visualization helps explain algorithms better. It's not just about graphs; it's about showing how logical rules work.
  2. Research shows there are ideal lengths for online content, like tweets and titles. Keeping things concise can improve engagement.
  3. Big data can have problems like inaccuracies and outdated info. This makes it challenging for companies and researchers to get reliable insights.

Data Science Weekly - Issue 30

Data Science Weekly Newsletter • 19 implied HN points • 19 Jun 14
🕹 Technology Data science Machine Learning Artificial Intelligence Big Data 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 27

Data Science Weekly Newsletter • 19 implied HN points • 29 May 14
🕹 Technology Data science Machine Learning Artificial Intelligence Big Data Software Engineering
  1. Deep neural networks have surprising flaws that go against what we usually believe, which can affect their performance.
  2. Hedge funds are now analyzing Twitter for trading clues, similar to how they look at market data.
  3. Companies are using R programming for various applications in data analysis, highlighting its growing popularity in the industry.

Data Science Weekly - Issue 25

Data Science Weekly Newsletter • 19 implied HN points • 15 May 14
🕹 Technology Data science Machine Learning AI Big Data 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 24

Data Science Weekly Newsletter • 19 implied HN points • 08 May 14
🕹 Technology Data science Machine Learning Artificial Intelligence Big Data Predictive Analytics
  1. R is a valuable tool for businesses, especially for those wanting to harness data effectively.
  2. Neural networks can tackle complex problems like wine classification by analyzing many different features.
  3. Creating a data-driven organization requires understanding customer needs, good training, and strong infrastructure.

Data Science Weekly - Issue 21

Data Science Weekly Newsletter • 19 implied HN points • 17 Apr 14
🕹 Technology Data science Machine Learning AI Big Data Programming
  1. Quantum machine learning has the potential to speed up data processing significantly compared to classical methods. This could lead to major advancements in how we analyze big data.
  2. Deep learning is gaining popularity for its effectiveness, but it remains a 'black box' where we can't easily understand why it makes certain decisions. This is a challenge that needs to be addressed.
  3. Companies like Netflix are using data science to better understand their content needs and customer preferences. This helps them make smarter decisions about what to create and acquire.