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 285

19 implied HN points • 09 May 19
🕹 Technology AI Machine Learning Data science Robotics Algorithms Data Analysis
  1. Machine learning is good at finding patterns in data, but understanding why those patterns exist is still a challenge. This breakthrough could help us understand complex systems better.
  2. Robots can avoid obstacles more effectively with a special type of camera that reduces perception delays. This can help improve how robots navigate through tricky environments.
  3. Stitch Fix uses a game called 'Style Shuffle' to quickly learn about customer preferences. This fun method helps them suggest clothes that people are more likely to buy.

Data Science Weekly - Issue 284

19 implied HN points • 02 May 19
🕹 Technology Data science Machine Learning Artificial Intelligence Analytics Programming
  1. Research on reinforcement learning is showing that agents can learn as quickly as humans by combining fast and slow learning techniques.
  2. Insurance and healthcare companies can use pictures of houses to better predict risk and improve their models.
  3. Artificial intelligence could help in designing buildings by providing new insights and alternative strategies for floor plans.

Data Science Weekly - Issue 283

19 implied HN points • 25 Apr 19
🕹 Technology Artificial Intelligence Data science Machine Learning Neural Networks Surveillance technology
  1. Training neural networks can be tricky, and it's important to understand common mistakes to get good results.
  2. AI is making big waves in various fields, including music and scientific research, showing how versatile it can be.
  3. Data scientists need to know the business and the data well, or they risk becoming bottlenecked and less effective.

Data Science Weekly - Issue 282

19 implied HN points • 18 Apr 19
🕹 Technology Data science Machine Learning Software Development Artificial Intelligence Computing
  1. Machine learning applications can be limited by a lack of computing power. Many teams have ideas they want to explore, but they can't because their current systems can’t handle the demands.
  2. Estimating the time needed for software projects is challenging and often leads to underestimating. It's important to consider statistical factors that can affect project timelines.
  3. Focusing solely on the performance of a machine learning model can be a mistake. It's better to look at how the model fits into a larger system and how it interacts with other components.
Get a weekly roundup of the best Substack posts, by hacker news affinity:

Data Science Weekly - Issue 280

19 implied HN points • 04 Apr 19
🕹 Technology AI Machine Learning Data science Algorithms Analytics
  1. AI is being developed by companies like DeepMind to create powerful technology, raising questions about who controls it. It's an important topic as AI continues to evolve.
  2. Tools like Warby Parker's virtual try-on algorithm show how technology can improve shopping experiences by using real-life simulations, making it easier for customers to make choices.
  3. Innovations in AI, like personalized travel recommendations from TripAdvisor and enhanced speech recognition for Alexa, demonstrate how machine learning can enhance user experiences in daily life.

Data Science Weekly - Issue 279

19 implied HN points • 28 Mar 19
🕹 Technology Artificial Intelligence Machine Learning Data science Algorithms Robotics
  1. Three scientists won the Turing Award for their groundbreaking work on neural networks. This award is like the Nobel Prize for computing and comes with a $1 million prize.
  2. Adversarial machine learning could pose security risks by allowing enemies to reverse-engineer AI systems. Experts urge caution as this threat could impact important technologies.
  3. The fast-food giant McDonald's is investing heavily in machine learning by acquiring a startup. This shows how businesses are increasingly using data and AI to improve operations.

Data Science Weekly - Issue 278

19 implied HN points • 21 Mar 19
🕹 Technology AI Data science Machine Learning Programming Analytics
  1. AI development can lead to positive outcomes, so it's valuable to ask what could go right instead of just focusing on the risks.
  2. New AI techniques, like using GANs, can create exciting content, such as realistic dance videos of athletes.
  3. Reducing the need for labeled data is a key challenge in deep learning, and finding ways to tackle it can enhance model training.

Data Science Weekly - Issue 277

19 implied HN points • 14 Mar 19
🕹 Technology Data science AI Research Programming Machine Learning Statistical Analysis
  1. Data science teams perform better with generalists instead of specialists. This approach helps teams adapt and innovate rather than just focusing on increasing productivity.
  2. R is a powerful programming language for data analysis, with many surprising capabilities beyond statistics. It has features that can impress even those in the computer science field.
  3. China is expected to surpass the U.S. in AI research output soon. This shift highlights the increasing importance of global competition in technology and research.

Data Science Weekly - Issue 275

19 implied HN points • 28 Feb 19
🕹 Technology Data science Artificial Intelligence Machine Learning Robotics Programming
  1. Artificial intelligence can help humans discover things we couldn't find on our own, making it a powerful tool in various fields.
  2. Creating a strong data science portfolio and tailored resume is crucial for job seekers in the data science field to stand out to potential employers.
  3. Machine learning can significantly improve the efficiency and value of renewable energy sources like wind power, showcasing its practical applications.

Data Science Weekly - Issue 274

19 implied HN points • 21 Feb 19
🕹 Technology Data science Machine Learning Artificial Intelligence Predictive Modeling Neural Networks
  1. The visual search engine project for Hayneedle shows how search can be enhanced by using images instead of words. This could make finding products easier for customers.
  2. Mathematicians are starting to understand how the design of neural networks affects their capabilities. This can help in optimizing their use for various tasks.
  3. Knowing your data thoroughly is crucial for anyone working in data science. It's essential to understand where the data comes from and what it represents.

Data Science Weekly - Issue 273

19 implied HN points • 14 Feb 19
🕹 Technology Data science AI Machine Learning Analytics Business Intelligence
  1. Curiosity is a key quality for succeeding in data science. It helps professionals think creatively and explore new ideas in their work.
  2. AI can do amazing things, like diagnosing childhood diseases better than some doctors. This shows just how powerful technology can be in healthcare.
  3. Pricing algorithms have become smarter and can now collude to raise prices. This means companies need to be careful about how they implement these systems.

Data Science Weekly - Issue 272

19 implied HN points • 07 Feb 19
🕹 Technology Artificial Intelligence Data science Machine Learning NLP Mobile Apps
  1. Neural networks have a strong impact on their performance based on their design. Researchers are uncovering how different structures affect what they can do.
  2. There's a new Android app called Live Transcribe that helps deaf or hard of hearing people have real conversations in real time. This technology can make everyday interactions much easier.
  3. CB Insights has listed 100 of the top AI companies in the world, showcasing startups that are leading in AI technology development and innovation. This is a way to highlight the most promising players in the industry.

Data Science Weekly - Issue 271

19 implied HN points • 31 Jan 19
🕹 Technology Data science Machine Learning AI Algorithms Statistics
  1. Machine learning projects can be tricky to manage because teams often struggle with setting clear goals and expectations.
  2. Data science can help predict startup valuations, revealing interesting properties and trends in how these valuations are distributed.
  3. New research in AI is making strides in speech reconstruction and facial recognition fairness, but these technologies also raise ethical concerns.

Data Science Weekly - Issue 270

19 implied HN points • 24 Jan 19
🕹 Technology Data science Artificial Intelligence Machine Learning Robotics Software Development
  1. Curiosity in data science can lead to big innovations. Instead of just focusing on improving processes, companies should give data scientists the space to explore new ideas.
  2. AI technology is advancing but can also reinforce past mistakes, especially in areas like criminal justice. It's important to use this technology wisely to avoid repeating errors.
  3. Training resources for aspiring data scientists are crucial. Guides that help build a strong portfolio and craft impressive resumes can significantly improve job prospects in this field.

Data Science Weekly - Issue 269

19 implied HN points • 17 Jan 19
🕹 Technology Artificial Intelligence Machine Learning Data science Programming Research
  1. Neural networks can be hard to understand, and researchers are exploring how to better interpret what they learn during training.
  2. In 2018, Google made significant advancements in AI research, and there's a lot for the community to reflect on and build upon going forward.
  3. Data science project flows can vary, and it's helpful for teams to structure their projects in ways that fit their unique challenges and goals.

Data Science Weekly - Issue 268

19 implied HN points • 10 Jan 19
🕹 Technology Data science Machine Learning Artificial Intelligence Deep Learning Software Development
  1. Being a specialist is important in data science. It's better to focus on a specific area rather than trying to know a little about everything.
  2. Machine learning research often takes a long time to reach actual industries. Many cutting-edge advancements are not quickly applied in real-world scenarios.
  3. Understanding practical skills is crucial for success in machine learning jobs. Many candidates lack essential skills that aren't taught in standard courses.

Data Science Weekly - Issue 267

19 implied HN points • 03 Jan 19
🕹 Technology Data science Artificial Intelligence Machine Learning Statistics Data Visualization
  1. Understanding probability and statistics can be made easier with visual tools, like those offered by Seeing Theory.
  2. Machine learning has significant potential in healthcare, including improving diagnoses and assisting doctors with data.
  3. There's a strong link between social mobility and family background, suggesting our parents' status can greatly impact our own opportunities.

Data Science Weekly - Issue 266

19 implied HN points • 27 Dec 18
🕹 Technology Data science Machine Learning Artificial Intelligence Algorithms Big Data
  1. Netflix's data team often clashes with the content team, highlighting the importance of balancing data insights with creative decisions.
  2. Teaching AI to write generates funny results, showcasing the difficulties of making machines understand human language.
  3. Data is not just raw information; it is influenced by human judgment and context, making it essential to analyze it carefully.

Data Science Weekly - Issue 265

19 implied HN points • 20 Dec 18
🕹 Technology Data science Artificial Intelligence Machine Learning Deep Learning Computer Vision
  1. AlphaZero is a powerful AI that learns board games like chess and Go from scratch, showing how quickly it can master complex games without prior knowledge.
  2. Building a deep learning system requires careful choice of hardware, and it's important to avoid overspending on unnecessary components.
  3. Collaboration between data science and engineering has challenges, but understanding these tension points can improve teamwork and model deployment.

Data Science Weekly - Issue 264

19 implied HN points • 13 Dec 18
🕹 Technology Artificial Intelligence Machine Learning Data science Neuroscience Computer Science
  1. Understanding how biological intelligence works can help us create better AI. It’s all about connecting different fields like neuroscience and psychology.
  2. Laughter in the workplace can boost team success. Measuring laughter might actually help improve innovation in projects.
  3. New methods in AI allow for training models while keeping data private. This could make using sensitive information like medical records safer.

Data Science Weekly - Issue 263

19 implied HN points • 06 Dec 18
🕹 Technology AI Data science Machine Learning Analytics Deep Learning
  1. Deep learning is rapidly evolving, and it's important to track these changes to stay updated in the field.
  2. AI is changing jobs; while some roles may vanish, there is a growing demand for skilled professionals who can work with AI.
  3. Machine learning is being used in creative ways, like predicting grocery item availability and generating addresses from satellite images.

Data Science Weekly - Issue 262

19 implied HN points • 29 Nov 18
🕹 Technology Data science Artificial Intelligence Machine Learning Deep Learning Healthcare Tech
  1. GANPaint allows you to create art by controlling specific objects in a scene using AI. It's an innovative way to draw, making it easier to express complex ideas visually.
  2. Uber AI has made significant progress in teaching AI to play challenging video games like Montezuma's Revenge. This shows how AI can learn and improve in tough scenarios without much human help.
  3. Amazon Comprehend Medical uses AI to understand medical language, which can help healthcare professionals work better. It's designed to help with everything from medical terms to complex procedures.

Data Science Weekly - Issue 261

19 implied HN points • 22 Nov 18
🕹 Technology AI Data science Machine Learning Programming Resources
  1. AI tools are becoming more accessible, and new tools will help make AI more like general computing. This change could allow more people to work with AI easily.
  2. There's a strong need for better testing in data science, similar to what software developers do. Good testing can help avoid big problems from data errors.
  3. Deep learning is being explored in exciting new ways, such as detecting diseases in X-rays. These advancements could lead to better healthcare solutions.

Data Science Weekly - Issue 260

19 implied HN points • 15 Nov 18
🕹 Technology Data science Machine Learning Artificial Intelligence Software Development Analytics
  1. There are great resources available for learning machine learning, making it easier to find information without re-searching. A collection of favorite resources can be helpful for quick reference.
  2. Seasonality in markets can impact demand, and companies like Lyft develop tools to encourage usage during peak times. Predicting when to activate these tools can help balance the supply of drivers and passengers.
  3. Making the shift from graduate student to data scientist can be challenging, but perseverance and learning from setbacks are crucial. Many find success by staying focused and adapting their skills to the job market.

Data Science Weekly - Issue 259

19 implied HN points • 08 Nov 18
🕹 Technology Data science Machine Learning AI Analytics Epidemiology
  1. Seattle and Houston provided large amounts of email metadata quickly, but Seattle's request came with a twist that led to an accidental extensive data collection.
  2. A machine-learned model called FINDER is being tested to detect foodborne illnesses in real-time using web search and location data.
  3. There are innovative projects like 'dankstimate' which aim to create a cannabis price estimator similar to Zillow's home price estimates.

Data Science Weekly - Issue 258

19 implied HN points • 01 Nov 18
🕹 Technology Data science Machine Learning Artificial Intelligence Programming Software Development
  1. Reinforcement learning agents can now explore better with curiosity-driven methods, helping them perform beyond human levels in certain games.
  2. Machines can simulate dreaming by recognizing patterns like the human brain, allowing them to create unique visual outputs without direct input.
  3. Choosing the right data science projects is crucial; a good strategy can lead to valuable results while a poor one may just waste resources.

Data Science Weekly - Issue 257

19 implied HN points • 25 Oct 18
🕹 Technology Data science Machine Learning Artificial Intelligence Data Visualization Big Data
  1. Neural networks can help create fun and unique Halloween costumes. Using AI for creative tasks can lead to new ideas we might not think of ourselves.
  2. Uber processes massive amounts of data very quickly, showing how big data can improve services and make operations smoother. Their platform manages over 100 petabytes of information.
  3. Learning data science can be made easier with mentorship and flexible payment options. Programs like Springboard's help you get job-ready skills while supporting your career journey.

Data Science Weekly - Issue 256

19 implied HN points • 18 Oct 18
🕹 Technology Data science Machine Learning Artificial Intelligence Programming Health tech
  1. The Big Mac Index, which used to be calculated manually, is now done using the R programming language. This change promotes transparency in how data is gathered and shared in journalism.
  2. Compression might become a key application for machine learning on devices like phones. Many people are surprised to learn that it can significantly improve performance in this area.
  3. There is a growing trend of AI chatbots providing medical advice, which raises questions about their effectiveness compared to human doctors.

Data Science Weekly - Issue 255

19 implied HN points • 11 Oct 18
🕹 Technology Data science Machine Learning Artificial Intelligence Programming Analytics
  1. The ML Engineering Loop helps engineers improve their model development by following a cycle of analyzing, selecting approaches, implementing, and measuring. This cycle allows them to quickly find the best solutions.
  2. Understanding uncertainty in data visualizations is important, and integrating uncertainty estimates can improve how we interpret plots and models. This can lead to better decision-making based on data.
  3. Using tools like TensorFlow.js for practical applications, such as object recognition in games, shows how machine learning can be fun and engaging. These examples help in learning and applying complex concepts in a creative way.

Data Science Weekly - Issue 254

19 implied HN points • 04 Oct 18
🕹 Technology Data science Machine Learning AI Data Visualization Deep Learning
  1. You can calculate the age of the universe using SQL to analyze data from various databases. It's easier than it sounds and can lead to interesting insights.
  2. Training deep learning models on phones and other small devices is now possible but still challenging. There are teams making it work, but the tools available aren't very user-friendly yet.
  3. Big data is starting to change genetic research a lot. New techniques are creating huge amounts of data, which helps scientists discover new things but also keeps them busy trying to catch up.

Data Science Weekly - Issue 253

19 implied HN points • 27 Sep 18
🕹 Technology Data science Machine Learning Artificial Intelligence Software Development Programming
  1. Uber uses forecasting with machine learning and deep learning to enhance its products and services. This means they can predict customer needs better and improve their offerings based on accurate data.
  2. Deep learning is changing software development by requiring fewer lines of code. Instead of writing complicated rules, developers set a foundation and let the system learn from examples.
  3. AI is being influenced by how we sense smell, leading to advancements in both biology and technology. Understanding chemical information can help create more sophisticated AI systems.

Data Science Weekly - Issue 252

19 implied HN points • 20 Sep 18
🕹 Technology Data science Machine Learning AI Cybersecurity Analytics
  1. A team found a surprising pattern in prime numbers, linking them to natural crystal patterns. This challenges the idea that prime numbers are completely random.
  2. DeepMind's AI is being used in Android Pie to help improve battery life, showing how AI can impact everyday technology. It's interesting to see if this actually makes a difference for users.
  3. Transfer learning makes it easier to solve problems by using knowledge from similar tasks. This approach saves time and resources in the field of deep learning.

Data Science Weekly - Issue 251

19 implied HN points • 13 Sep 18
🕹 Technology AI Data science Machine Learning Automation Analytics
  1. AI systems, like Amazon's Echo, rely on many factors, including resources and labor. Understanding these can give insights into the complexity of AI.
  2. Fake news can significantly impact politics, and there's now a mathematical model to help simulate how it influences voting. This shows the power of accurate models in understanding societal effects.
  3. There are new tools and techniques in machine learning that make it easier to analyze and improve models. Resources like the 'What-If' tool let users explore machine learning without needing to code.

Data Science Weekly - Issue 250

19 implied HN points • 06 Sep 18
🕹 Technology Data science Machine Learning Artificial Intelligence Analytics Programming
  1. There's a growing need for data scientists in the U.S. now that there's a shortage, which is a big change from just a few years ago when there were too many people in the field.
  2. New approaches in machine learning, like unsupervised machine translation, are making it easier to provide fast and accurate translations in many languages, helping people connect better.
  3. Researchers are looking into how small changes in images can confuse computer vision models, and they wonder if the same happens to humans, pointing out potential vulnerabilities in both AI and human vision.

Data Science Weekly - Issue 249

19 implied HN points • 30 Aug 18
🕹 Technology Data science Machine Learning Artificial Intelligence Big Data Software Development
  1. Netflix is using notebooks for development and collaboration, helping manage many scheduled jobs more effectively.
  2. Understanding the world in 3D is challenging, especially for extending successful technologies like convolutional networks.
  3. There's a creative idea to enhance shopping experiences for color-blind clients by pairing their selections with personalized music.

Data Science Weekly - Issue 248

19 implied HN points • 23 Aug 18
🕹 Technology Data science Machine Learning Artificial Intelligence Software Development Analytics
  1. AI is changing how we do business, and it's becoming more self-sufficient, meaning it could improve processes on its own without needing human input.
  2. China uses data and AI extensively for surveillance and governance, which raises questions about the balance between democracy and data-driven control.
  3. New tools and technologies are constantly emerging in data science, such as those that help improve the speed of medical procedures like MRIs and enhance gaming graphics.

Data Science Weekly - Issue 246

19 implied HN points • 09 Aug 18
🕹 Technology Data science Machine Learning Artificial Intelligence Data Analytics Programming
  1. Balancing quick changes and long-term planning is tough in data science, and it's important to find ways to adapt without losing sight of the bigger picture.
  2. Coca-Cola successfully used advanced technology like TensorFlow for its marketing efforts, showcasing how big companies can leverage data science for effective campaigns.
  3. Automated machine learning tools, like AutoKeras, help people without deep technical skills to use powerful machine learning models easily.