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 245

19 implied HN points • 02 Aug 18
🕹 Technology Data science Machine Learning Artificial Intelligence Software Development Algorithms
  1. Hiring the right people is crucial for data science teams. Companies should look for candidates who can work independently and fit well with the team culture.
  2. Understanding uncertainty in models is important. This helps in interpreting results and debugging any issues that arise in data science projects.
  3. Learning resources are abundant in data science. There are many tools and tutorials available to help beginners and advanced users improve their skills.

Data Science Weekly - Issue 244

19 implied HN points • 26 Jul 18
🕹 Technology Data science Machine Learning Artificial Intelligence Analytics Programming
  1. Companies should define data science roles using three tracks: Analytics, Inference, and Algorithms. This helps meet business needs more effectively.
  2. Google's AutoML is a tool that automates machine learning processes, tapping into transfer learning to enhance capabilities and ease of use.
  3. Multi-task learning allows machines to learn multiple tasks at once, making them smarter and better at handling complex problems, similar to how humans learn.

Data Science Weekly - Issue 243

19 implied HN points • 19 Jul 18
🕹 Technology AI Machine Learning Data science Programming Research
  1. AI might be able to replace some animal testing by predicting chemical toxicity. This could make testing faster and more ethical.
  2. Understanding what machine learning practitioners do is key to improving their training and tools. This could help more people get into the field of machine learning.
  3. The Netflix workshop highlighted that traditional recommendation methods might be outdated. New techniques are needed to keep up with changing user preferences.

Data Science Weekly - Issue 242

19 implied HN points • 12 Jul 18
🕹 Technology Data science Machine Learning Artificial Intelligence Big Data Cloud Computing
  1. There's a big focus on how artificial intelligence has evolved in the past year, with many players in the market and new trends shaping its future.
  2. Understanding the difference in approaches to machine learning is crucial for businesses, as many struggle when they don't recognize the distinctions.
  3. New methods in machine learning, like generating detailed ground views from satellite images, show how technology can create innovative solutions to complex problems.
Get a weekly roundup of the best Substack posts, by hacker news affinity:

Data Science Weekly - Issue 240

19 implied HN points • 28 Jun 18
🕹 Technology Data science Machine Learning Artificial Intelligence Analytics Programming
  1. AI has become very powerful, even beating expert humans in complex games like Dota 2. This shows how quickly technology is advancing.
  2. Data science can play a meaningful role in addressing social issues, like the problem of public human waste in cities. Mixing social science with data could lead to helpful solutions.
  3. Building a data dictionary is crucial for teams, as it helps clarify key terms and metrics. This can greatly improve communication and reduce confusion within a business.

Data Science Weekly - Issue 239

19 implied HN points • 21 Jun 18
🕹 Technology AI Machine Learning Data science Healthcare Tech Robotics
  1. AI can win arguments, but it doesn't actually understand what it's saying. This highlights the difference between human reasoning and machine processing.
  2. Researchers are working hard to make sure algorithms are fair and unbiased. This is important as more decisions are made by machines in our everyday lives.
  3. AI and robotics are making a big impact on healthcare. Experts believe they will transform how we treat and manage health issues in the future.

Data Science Weekly - Issue 238

19 implied HN points • 14 Jun 18
🕹 Technology Machine Learning Data science AI Development Statistical Analysis Data Visualization
  1. Neural networks can struggle to tell jokes if they don't have enough examples to learn from. Giving them more data might help improve their humor.
  2. Machine learning is becoming more efficient with smaller, low-power chips, which could solve many current problems. This trend is expected to grow in the future.
  3. Data cleaning takes a lot of time in data science, with up to 80% of the effort spent on it. Learning tools like Python's Pandas can really help with this task.

Data Science Weekly - Issue 237

19 implied HN points • 07 Jun 18
🕹 Technology Data science Machine Learning Artificial Intelligence Big Data Deep Learning
  1. Understanding how the human brain works can improve our grasp of complex environments. This knowledge helps in both neuroscience and technology applications.
  2. The future job landscape will involve more collaboration between humans and machines. Companies need to prepare for a mix of human and automated roles.
  3. Deep learning techniques are evolving, especially in object detection. Innovations in this field show how minor adjustments can lead to significant improvements in performance.

Data Science Weekly - Issue 236

19 implied HN points • 01 Jun 18
🕹 Technology Data science Machine Learning Artificial Intelligence Statistics Analytics
  1. Improving training data is really important for making machine learning models work well. Focusing on data quality can lead to better results than just tweaking the model itself.
  2. AI tools are making a big difference in healthcare, like the one approved for detecting wrist fractures. These technologies can help doctors diagnose patients more accurately.
  3. Google found that some tricky interview questions didn't actually help in hiring good candidates. It shows that being smart isn't just about solving brainteasers.

Data Science Weekly - Issue 236

19 implied HN points • 31 May 18
🕹 Technology Data science Machine Learning Analytics Artificial Intelligence Big Data
  1. Natural disasters like Hurricane Maria can have serious health impacts, and it's hard to get an accurate death count afterward.
  2. Improving training data is key to making better machine learning models, and there are practical ways to enhance that data.
  3. Reproducibility in machine learning is important, but it can be tough to achieve and often requires careful planning and work.

Data Science Weekly - Issue 235

19 implied HN points • 24 May 18
🕹 Technology Data science Machine Learning Artificial Intelligence Neural Networks Statistical Analysis
  1. Deep learning models are making it easier to categorize images, like those used in Airbnb listings.
  2. New research suggests that the brain may store information in a discrete way, which could change our understanding of brain and technology interactions.
  3. There are many resources available for learning data science, including online programs and tutorials that cover various tools and techniques.

Data Science Weekly - Issue 234

19 implied HN points • 17 May 18
🕹 Technology AI Data science Machine Learning Programming Automation
  1. Teaching AI about cause and effect can help make it smarter and more intelligent. Understanding the 'why' behind actions is crucial for progress.
  2. Self-driving technology is advancing, as seen with MIT's new car that can drive on roads it has never seen before using basic GPS and sensors.
  3. There are resources available to help people start a career in data science, including guides on building a portfolio and creating a standout resume.

Data Science Weekly - Issue 233

19 implied HN points • 10 May 18
🕹 Technology Artificial Intelligence Machine Learning Data science Software Development Engineering
  1. AI systems can learn from each other by arguing, which might help us understand their behavior better.
  2. In the future, machine learning tools may interact with us more like pets than machines, creating a collaborative experience.
  3. Despite powerful tech companies, skilled programmers can still outperform them in certain AI tasks, showing the value of human creativity.

Data Science Weekly - Issue 232

19 implied HN points • 04 May 18
🕹 Technology Data science Machine Learning Artificial Intelligence Software Development Cloud Computing
  1. Google's Teachable Machine helps people understand how to make machine learning models easier to use.
  2. Data science in startups needs strong processes for analyzing data and experimenting with models, especially when building from scratch.
  3. There's a powerful method for deep learning that works well with tabular data, and it's starting to be used by many big companies.

Data Science Weekly - Issue 232

19 implied HN points • 03 May 18
🕹 Technology Data science Machine Learning Artificial Intelligence Deep Learning Programming
  1. Using machine learning can be made easier and more accessible through tools like Google's Teachable Machine, which provides useful UX insights.
  2. Deep learning techniques are being adapted for different types of data, including enhancing performance in models working with tabular data.
  3. Focusing on good data practices and proper processes is key for startups looking to build a strong data science platform.

Data Science Weekly - Issue 231

19 implied HN points • 26 Apr 18
🕹 Technology AI Data science Machine Learning Statistics Education
  1. The efficiency of the human brain surpasses AI due to its ability for massive parallel processing, which is an interesting aspect of studying intelligence.
  2. Using qualitative methods in data science projects can lead to better outcomes by ensuring crucial features are not overlooked before jumping into data analysis.
  3. There are ongoing debates about the reliability of p-values in statistical testing, and some researchers are reconsidering their use in studies.

Data Science Weekly - Issue 230

19 implied HN points • 19 Apr 18
🕹 Technology Data science Machine Learning Artificial Intelligence Programming Algorithms
  1. You can learn how to become a data scientist with specific guides focused on gaps in knowledge, portfolio building, and resume writing.
  2. There are fun projects in AI, like training models to recognize dogs or create cartoons, showing how diverse applications of data science can be.
  3. Bias in machine learning models is a big issue, and it's important to understand how these biases can affect results in various tasks.

Data Science Weekly - Issue 229

19 implied HN points • 12 Apr 18
🕹 Technology Data science Machine Learning Artificial Intelligence Deep Learning Software Engineering
  1. Using mathematical methods like Markov Decision Processes can help find the best strategies to play games like 2048.
  2. Uber AI Labs has introduced a technique called differentiable plasticity, which allows AI to adapt and learn better over time.
  3. Automating canary analysis, as done by Netflix with their Kayenta platform, can improve testing of new software changes quickly and efficiently.

Data Science Weekly - Issue 228

19 implied HN points • 05 Apr 18
🕹 Technology Data science Machine Learning Artificial Intelligence Software Development Data Visualization
  1. Using just $1 of hardware, you can turn a MacBook into a touchscreen with some clever computer vision. It shows how innovative ideas can come from simple solutions.
  2. There's a debate about whether we need new programming languages specifically for machine learning. Current languages are being adapted, but new ones might be better suited for future AI developments.
  3. The NIH is pushing to use data science and AI to improve healthcare initiatives. They’re looking for public input to create a strategy around data science in health and research.

Data Science Weekly - Issue 226

19 implied HN points • 22 Mar 18
🕹 Technology Data science Machine Learning AI Software Development Big Data
  1. A Senior Data Scientist's role is often unclear and expectations can vary widely. It can be helpful to define what skills and responsibilities are actually needed.
  2. Digital evolution in AI can show surprising creativity that doesn't always match our expectations. This means evolution can create new ideas in unexpected ways.
  3. There's a big conversation about AI and responsibility. When AI causes harm, it's tough to figure out who should be accountable for it.

Data Science Weekly - Issue 225

19 implied HN points • 15 Mar 18
🕹 Technology AI Machine Learning Data science Robotics Automation
  1. Machine learning can create completely new sounds by learning from existing ones, which is really cool for music-making.
  2. AI has a problem where it sometimes sees or hears things that aren't there, which makes using it tricky.
  3. Robots might be the future of farming, helping to automate growing food from start to finish for better efficiency.

Data Science Weekly - Issue 224

19 implied HN points • 08 Mar 18
🕹 Technology Data science Machine Learning AI Software Engineering Information Technology
  1. Success is influenced by both talent and luck. Sometimes, even the most talented individuals don’t succeed without a bit of luck.
  2. Humans can learn faster than AI because we have background knowledge and experience that help us understand new things more quickly.
  3. AI should enhance our conversations, not limit them. It’s important for AI to strive for interesting and meaningful dialogue rather than just following simple paths.

Data Science Weekly - Issue 223

19 implied HN points • 01 Mar 18
🕹 Technology Data science AI Machine Learning Algorithms Programming
  1. AI still struggles with creativity and emotional understanding in music, meaning it can't fully replace human DJs and playlist makers.
  2. Female characters are underrepresented in superhero comics, and their portrayal is important to analyze as well.
  3. Containerization is a complex topic for data scientists, and balancing their autonomy with the need for engineering support is essential for success.

Data Science Weekly - Issue 222

19 implied HN points • 22 Feb 18
🕹 Technology Data science Artificial Intelligence Machine Learning Neural Networks Deep Learning
  1. A moth's brain can learn to recognize odors faster than AI can, showing a fascinating aspect of how natural intelligence works.
  2. There's a shortage of AI talent, with only around 22,000 people worldwide having the necessary skills, which is a big challenge for the industry.
  3. New AI technologies are learning to be creative by understanding rules and then finding ways to break them, which could lead to innovative solutions.

Data Science Weekly - Issue 221

19 implied HN points • 15 Feb 18
🕹 Technology Data science Artificial Intelligence Machine Learning Data Visualization Software Development
  1. Deep learning can be implemented in simple tools like Google Sheets, making it more accessible for everyone.
  2. Reinforcement learning in trading could be a valuable research area, similar to training AI for multiplayer games.
  3. The use of AI tools is growing rapidly, impacting fields like data visualization and criminal justice decision-making.

Data Science Weekly - Issue 220

19 implied HN points • 08 Feb 18
🕹 Technology Data science Machine Learning AI Ethics Software Development Analytics
  1. A large database helps researchers understand what makes people happy. This information can be used to improve well-being.
  2. Deep learning has some limitations, like being too simple or not always reliable. It's important to recognize these downsides as we advance in AI.
  3. There’s a need for ethical guidelines in data science because so much data is created every day. We need to ensure this data is used responsibly.

Data Science Weekly - Issue 219

19 implied HN points • 01 Feb 18
🕹 Technology Data science Machine Learning AI Deep Learning Big Data
  1. Deep learning education needs a common way to explain why different layers exist. Right now, it’s taught differently than other technical fields.
  2. You can create autonomous driving models using simulation environments like AirSim. This lets you train a model to steer a car just with camera input.
  3. Learning matrix calculus helps in understanding deep learning better. This knowledge is crucial for mastering the training of deep neural networks.

Data Science Weekly - Issue 218

19 implied HN points • 25 Jan 18
🕹 Technology Artificial Intelligence Data science Machine Learning Analytics Software Development
  1. Artificial intelligence (AI) is rapidly changing many industries, similar to how electricity transformed the world. It's important to understand its potential impact on various sectors.
  2. Using data science can help create fairer political maps, a task that involves settling disagreements on what 'fair' means. This is a significant challenge in the fight against gerrymandering.
  3. Recommendation systems are not just for e-commerce; they can be used in any decision-making scenario where matching items is important. Understanding how they work can help improve their effectiveness in various applications.

Data Science Weekly - Issue 217

19 implied HN points • 18 Jan 18
🕹 Technology Data science Machine Learning Artificial Intelligence Software Development Deep Learning
  1. Deep learning can help automate front-end design by turning design mockups into code. This could make web development faster and easier for developers.
  2. Cloud AutoML is making AI technology more available to businesses that don't have a lot of machine learning experts. This tool can help them create their own high-quality models.
  3. A new recommendation method using a tree-based model can learn user preferences better than traditional methods. This could lead to smarter and more personalized recommendations for users.

Data Science Weekly - Issue 216

19 implied HN points • 11 Jan 18
🕹 Technology AI Data science Machine Learning Deep Learning Computer Vision
  1. A cat named Oscar is surprisingly good at predicting when terminally ill patients are going to die, showing that sometimes animals can have abilities we don't understand yet.
  2. Researchers are making AI systems that can recognize when they are uncertain about something. This could help them make better decisions and avoid mistakes.
  3. There are new tricks used in AI, like AlphaGo Zero, that show how deep learning can improve by learning from its own experiences and using fewer resources.

Data Science Weekly - Issue 215

19 implied HN points • 04 Jan 18
🕹 Technology Data science Machine Learning AI Career transition Ethics
  1. Many data scientists come from different backgrounds, both academic and non-academic. It can be helpful for those in academia to learn from others who successfully transitioned to the industry.
  2. Algorithms used in various fields can reflect our biases, which creates ethical issues. Understanding these biases in data processing is crucial to avoid unfair outcomes.
  3. Reflecting on advancements in AI and deep learning over the past year can inspire new ideas and projects. It's a good practice to review and learn from previous developments.

Data Science Weekly - Issue 214

19 implied HN points • 28 Dec 17
🕹 Technology Data science Machine Learning Artificial Intelligence Deep Learning Analytics
  1. There was a lot of cool stuff happening in data science in 2017. It's a good idea to look back and see what others accomplished that year.
  2. NVIDIA is facing competition in deep learning hardware with new products coming from AMD and Intel. It might be wise to hold off on buying new hardware until the market settles.
  3. Machine learning is getting more attention in fields like physics, showing its importance in making big discoveries. Using tools like Python is becoming essential in modern science.

Data Science Weekly - Issue 213

19 implied HN points • 21 Dec 17
🕹 Technology Data science Machine Learning Programming Artificial Intelligence Analytics
  1. Machine learning can help decode animal communication, like chicken chatter, for better farming practices. This shows how AI can be useful in agriculture.
  2. Turning raw data into useful products is complex, as seen with Google Maps, which relies on a lot of behind-the-scenes work. It highlights the importance of data processing in creating useful tools.
  3. Finding exoplanets is challenging, but machine learning has made some progress in this area. It illustrates how technology is advancing our understanding of the universe.

Data Science Weekly - Issue 212

19 implied HN points • 14 Dec 17
🕹 Technology AI Machine Learning Data science NLP Statistics
  1. Neural networks are being designed to improve memory, similar to how humans remember important things and forget the rest. This helps machines learn more efficiently.
  2. Stitch Fix is using advanced algorithms to improve online shopping by predicting the right sizes for customers without measuring them. This makes the shopping experience better and more personal.
  3. AI is being developed to combat fake news by identifying suspicious stories. However, this also raises concerns about an ongoing battle between true and false information.

Data Science Weekly - Issue 211

19 implied HN points • 07 Dec 17
🕹 Technology Data science Machine Learning Artificial Intelligence Deep Learning
  1. A new library of 3-D images can help robots better navigate in homes by recognizing different furniture. This means robots could become more helpful around the house.
  2. Deep learning continues to evolve, and some algorithms are now as good as expert doctors in diagnosing diseases. This could greatly impact healthcare and how we approach medical diagnoses.
  3. Effective data science management is crucial for the success of organizations. Understanding how to scale and manage data science teams can lead to more valuable outcomes.

Data Science Weekly - Issue 210

19 implied HN points • 30 Nov 17
🕹 Technology Data science Machine Learning Artificial Intelligence Deep Learning Computer Vision
  1. Computer Vision has seen many advancements recently, making a big impact on society. It's important to keep a balance when discussing potential future outcomes.
  2. The idea of an intelligence explosion is challenged by claims that it misunderstands how intelligence and self-improving systems work. Concrete examples support this perspective.
  3. A study showed that many comments about net neutrality might have been faked using natural language processing, raising concerns about online authenticity.

Data Science Weekly - Issue 209

19 implied HN points • 24 Nov 17
🕹 Technology AI Machine Learning Data science Computer Vision Algorithms
  1. Flies have a unique way of recognizing and categorizing odors, which inspired a new computer algorithm for searching similar images online.
  2. AI can now identify art forgeries just by analyzing brushstrokes, making the detection process easier and less expensive.
  3. Apple is still catching up in the AI field, despite previous promises to collaborate more with researchers and improve their technology.

Data Science Weekly - Issue 208

19 implied HN points • 16 Nov 17
🕹 Technology Data science Machine Learning Artificial Intelligence Software Development Innovations
  1. Neural networks are changing how we develop software, not just a simple tool for machine learning tasks. They represent a major new approach in programming.
  2. Evolution strategies can be visually explained, making it easier to understand this concept in AI. This approach helps simplify complex algorithms.
  3. There are new tools, like TensorFlow Lite, that make machine learning work better on mobile devices. This makes it easier to create smart applications that run quickly.