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 364

19 implied HN points • 12 Nov 20

Organizing data in spreadsheets can help prevent errors and make analysis easier. It's important to keep a consistent format and to avoid leaving any empty cells.
AI is being used to create music that sounds like famous artists, which could change the music industry. This technology raises questions about copyright and authenticity.
Monitoring tools are becoming essential for data scientists to track their models for performance and integrity. These tools help ensure that models are accurate and reliable over time.

Data Science Weekly - Issue 363

19 implied HN points • 05 Nov 20

🕹 Technology Data science Machine Learning Artificial Intelligence Computer Science Software Development

Synthetic biology has gained a lot of attention over the past decade, and it's been evolving to deliver real technologies and breakthroughs.
Data poisoning is a serious concern in machine learning, as bad data can manipulate model predictions, especially with NLP models.
Managing data for machine learning projects is challenging, but using version control tools can help keep things organized and prevent unexpected issues.

Data Science Weekly - Issue 362

19 implied HN points • 29 Oct 20

🕹 Technology Data science Machine Learning Artificial Intelligence Data Engineering Data Analytics

Form extraction using AI can help important fields like journalism and medicine by accurately pulling data from documents. This can significantly improve research and decision-making.
Data engineering is crucial and involves gathering, cleaning, and shaping data before it's analyzed. It's just as important as data science, which builds on that data to create insights and models.
Dealing with data imbalance can be tricky, but using semi-supervised and self-supervised learning techniques can improve model performance. These methods help when some categories have much less data than others.

Data Science Weekly - Issue 361

19 implied HN points • 22 Oct 20

🕹 Technology Data science Machine Learning AI Programming Quantum Computing

Modern data infrastructure is becoming crucial for businesses, as they need better ways to analyze data for value. Companies are confused about the best technologies to use.
Many businesses are investing in AI, but few are actually seeing big returns on that investment. About 11% of companies report gaining significant financial benefits from AI.
There are new learning techniques in AI that allow models to learn from very few examples. This could make machine learning more accessible and reduce costs.

Data Science Weekly - Issue 360

19 implied HN points • 15 Oct 20

🕹 Technology Data science Machine Learning Artificial Intelligence Programming Computer Vision

Improving performance on GPUs is crucial for machine learning. It helps speed up both research and development, which leads to better results overall.
BMW is working on ethical guidelines for AI usage. This aims to ensure that as AI evolves, it remains focused on benefiting people.
Data discovery can be a challenge for companies. Facebook built a tool called Nemo to make it easier for engineers to find the information they need quickly.

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Data Science Weekly - Issue 359

19 implied HN points • 08 Oct 20

🕹 Technology Data science Machine Learning Artificial Intelligence Software Development Programming

Arduino is making machine learning easier for everyone by integrating TensorFlow Lite, which lets people run neural networks on Arduino boards to understand simple voice commands.
Papers with Code is now working with arXiv to connect research papers to related code, making it easier for people to see how studies are applied in practice.
Research shows that machine learning models can help automate tasks like counting craters on Mars, which saves human researchers time and effort, allowing them to focus on more complex questions.

Data Science Weekly - Issue 358

19 implied HN points • 01 Oct 20

🕹 Technology Data science Machine Learning Artificial Intelligence Software Engineering Statistics

Data quality is very important for machine learning (ML) operations. It helps ensure that ML systems produce reliable results and builds trust with stakeholders.
The State of AI Report highlights recent developments in AI, focusing on research breakthroughs, talent supply, industry applications, and future predictions.
Diversity in AI and supporting applied statistics students are crucial for improving representation and effectiveness in data science and machine learning fields.

Data Science Weekly - Issue 357

19 implied HN points • 24 Sep 20

🕹 Technology Data science Machine Learning AI Public Health Remote work

Good communication techniques are key for data and engineering teams to solve technical problems effectively. By improving how they express ideas, teams can reach better solutions faster.
Competitions like the C3.ai COVID-19 Grand Challenge encourage teams to use data science for social good. It's a great chance to make a positive impact during tough times by tackling significant challenges like the pandemic.
New tools like TensorFlow Recommenders make it easier for people to build and serve recommendation models. These tools help users get personalized suggestions for things like movies and restaurants quickly.

Data Science Weekly - Issue 356

19 implied HN points • 17 Sep 20

🕹 Technology Data science Machine Learning Artificial Intelligence Software Development Programming

ICML is an important conference for those in machine learning, catering to various professionals like researchers and engineers. It's a great place to learn and share knowledge about advancements in the field.
NumPy is a key tool for scientific programming in Python, helping organize and analyze data efficiently. It's widely used and supports various other libraries for data science tasks.
The emergence of generative AI technology is changing the entertainment industry rapidly. Soon, creating movies or shows could be done at a fraction of today's production costs.

Data Science Weekly - Issue 355

19 implied HN points • 10 Sep 20

🕹 Technology Data science Machine Learning Artificial Intelligence Big Data Computing

DeepMind and Google Maps are using advanced Graph Neural Networks to improve the accuracy of travel time predictions, making them even more reliable in cities around the world.
AI is now being used to detect deepfake videos by identifying unique signals from the videos, which can help spot how they were made.
There are resources available to help people get started in data science, build their portfolios, and improve their resumes to land jobs in this field.

Data Science Weekly - Issue 354

19 implied HN points • 03 Sep 20

🕹 Technology Artificial Intelligence Machine Learning Data science Programming Software Development

A machine learning algorithm recently helped discover 50 new planets from old NASA data, showing how AI can unlock new discoveries.
There has been a noticeable drop in deep learning job postings in the past six months, revealing that many companies are reassessing the importance of this technology.
Apple has introduced a residency program for AI and machine learning, offering training and hands-on experience for those with relevant backgrounds.

Data Science Weekly - Issue 353

19 implied HN points • 27 Aug 20

🕹 Technology Machine Learning Data science AI Development Software Engineering User Experience

Effective testing is crucial for machine learning systems. It's important to understand that these systems require different testing strategies compared to traditional software.
There are hidden challenges in becoming a machine learning engineer. Many of these insights come from the experiences of those already in the field, beyond what you learn in books.
New resources and courses are constantly being developed in data science. For example, fast.ai just released a new deep learning course and libraries, which can help beginners get started.

Data Science Weekly - Issue 352

19 implied HN points • 20 Aug 20

🕹 Technology Data science Machine Learning Artificial Intelligence Algorithms Computing

minGPT is a smaller version of the GPT model that aims to be simple and easy to understand. It’s only about 300 lines of code, which makes it a good resource for learning.
Biased training data, like the CoNLL-2003 dataset, can lead AI models to perform poorly on diverse names and future data. This can cause ongoing issues with how these models recognize different groups.
Reinforcement learning has challenges in real-world applications due to assumptions that often don't hold up. Researchers need to address these challenges to make RL more practical and effective.

Data Science Weekly - Issue 351

19 implied HN points • 13 Aug 20

🕹 Technology Data science Machine Learning AI Computer Vision Analytics

Machine learning models need regular maintenance after deployment. It's important to monitor data and model behavior to avoid problems and improve performance.
Collaboration and good understanding of problems are key in AI development. This helps teams create better applications and make profits.
New tools and resources are becoming available for data science, like access to research papers on Kaggle. These can help improve machine learning techniques and open up new possibilities.

Data Science Weekly - Issue 350

19 implied HN points • 06 Aug 20

🕹 Technology AI Data science Machine Learning NLP

Language models like GPT-3 can do amazing things, such as creating human-like text and writing code, but there's still curiosity about their ability to make analogies.
Data science is increasingly being applied to many fields, like health through biomedical NLP or analyzing complex problems with graph technologies.
As companies build their data tools, there’s a trend toward developing unique solutions tailored to their specific needs, highlighting the importance of data discovery.

Data Science Weekly - Issue 349

19 implied HN points • 30 Jul 20

🕹 Technology Data science Machine Learning Artificial Intelligence Neuroscience Deep Learning

Deep learning has important ideas that have been around for a while. If you're new to it, learning these basics can really help you understand current research.
GPT-3 is creating a lot of buzz, and it's important to think critically about the hype. Understanding the difference between hype and reality helps us navigate new technologies better.
Evaluating machine learning models is similar to testing software. New methods can help us better assess how well these models work, which is key to making them reliable.

Data Science Weekly - Issue 348

19 implied HN points • 23 Jul 20

🕹 Technology Data science Machine Learning Artificial Intelligence Deep Learning Software Development

Deep Learning papers can be confusing for beginners, but there's a roadmap to help you choose where to start. It's a good way to navigate through the vast amount of research out there.
Machine Learning is creating a lot of value for businesses, and it's important to understand how this value can be captured. Different companies are finding unique ways to apply ML for their needs.
New techniques in AI, like using neural networks for soundscapes, are not just tech innovations but can also help protect the environment. It shows how technology can contribute to nature conservation.

Data Science Weekly - Issue 347

19 implied HN points • 16 Jul 20

🕹 Technology Data science Machine Learning Artificial Intelligence Statistical Methods Data Infrastructure

Netflix is working on making its data usage more efficient. They have created a dashboard that helps their team understand data costs and trends better.
Using meta-augmentation in machine learning can improve performance more than just changing the model. It's important to focus on enhancing the data we use.
When building robots, the goal should be to assist humans, not replace them. This approach considers the future of robotics in various fields like transportation and healthcare.

Data Science Weekly - Issue 346

19 implied HN points • 09 Jul 20

🕹 Technology Artificial Intelligence Data science Machine Learning Neural Networks Computing

AI training costs are dropping much faster than usual, which means AI technology is becoming easier and cheaper to develop. This could lead to more companies using AI over the next decade.
Training Generative Adversarial Networks (GANs) can be tough, but there are new algorithms that help make it more stable and efficient. This is important for many applications in science and engineering.
Moving from traditional statistics to machine learning involves a different way of thinking. Understanding this shift can help those with a stats background adapt and excel in machine learning.

Data Science Weekly - Issue 345

19 implied HN points • 02 Jul 20

🕹 Technology AI Machine Learning Data science Software Development Computing

Making machine learning useful in real life is a key focus for companies like startups, especially when they provide machine learning as a service.
Documentation is important in machine learning to explain how models work and to clarify their intended use, which helps avoid misuse.
There are ongoing discussions about improving the machine learning community, addressing issues like toxicity, fairness, and the peer-review process.

Data Science Weekly - Issue 344

19 implied HN points • 25 Jun 20

🕹 Technology Data science Machine Learning AI Software Engineering Analytics

As AI systems become more common, it’s important to think about who is responsible when things go wrong. Recent incidents raise questions about how to share accountability between people, companies, and governments.
Scientists are learning more about years of small earthquakes in California, and they found that fluids moving through the ground might have caused them. This shows how understanding these events can help with studying earthquakes around the world.
There are many tools for machine learning, but the landscape is still developing. A study looked at over 200 tools to find out what works best and what challenges people face when using them.

Data Science Weekly - Issue 343

19 implied HN points • 18 Jun 20

🕹 Technology Data science Machine Learning Artificial Intelligence Software Development Algorithms

AI models can now generate images just like they generate text, thanks to advanced training methods. This shows how powerful these technologies have become in creating complex visuals.
MLOps is key for data scientists as it helps them work together better by automating tasks like testing and versioning. This makes their processes smoother and more efficient.
Regulating algorithms is important because they influence many aspects of our lives without any oversight. A new system is needed to ensure they are used fairly and responsibly.

Data Science Weekly - Issue 342

19 implied HN points • 11 Jun 20

🕹 Technology Data science Machine Learning Software Engineering Automation Artificial Intelligence

Recent studies show that there hasn't been a significant change in the types of jobs that get automated, despite the rise of new technology. It seems that many jobs remain unaffected by automation trends.
Tools like OpenAI's API allow easy integration of advanced language tasks without needing extensive data. This makes it simpler for developers to use powerful language models.
Feature engineering and managing technical debt are crucial in machine learning development. Good practices can help to avoid messy code and ensure smoother transitions from development to production.

Data Science Weekly - Issue 341

19 implied HN points • 04 Jun 20

🕹 Technology Data science Machine Learning Artificial Intelligence Computing Training

Mathematics often requires new methods to solve problems, showing how innovation is crucial in research.
GPT-3 is a massive language model that significantly improves deep learning and natural language processing capabilities.
Many people find data science jobs disappointing, and it's important to manage your expectations in any job field.

Data Science Weekly - Issue 340

19 implied HN points • 28 May 20

🕹 Technology Data science Artificial Intelligence Machine Learning Automation Software Development

AI can be limited in business because of how it's researched, but understanding these limits can help identify new business opportunities. This means knowing the business process well can lead to better use of AI to save time and money.
There's a growing belief that humans and machines should work together rather than striving for complete automation. Collaborating with machines can often be more effective and safer than going fully automated.
Basic machine learning skills are still very important, even with all the focus on deep learning. Many companies want solid foundational knowledge rather than just the latest trends, so understanding the basics can be key to success.

Data Science Weekly - Issue 339

19 implied HN points • 21 May 20

🕹 Technology Artificial Intelligence Data science Machine Learning Ethics Software Development

AI Product Managers need special skills for managing AI products beyond traditional project management. This includes an understanding of machine learning and its real-world applications.
Technical debt in machine learning is important to manage to avoid problems later. New tools can help address this issue, highlighting the need for staying updated over time.
China is actively discussing AI ethics, contrary to popular belief. Their conversations align with global standards, and they are exploring how these principles fit into their own culture and systems.

Data Science Weekly - Issue 338

19 implied HN points • 14 May 20

🕹 Technology Data science Machine Learning Artificial Intelligence Software Development Programming

AR and machine learning can be combined to create cool tools, like cutting parts of our surroundings and pasting them into images.
Mapping the connections in the human brain can help scientists understand how our brains work and what happens when they are not healthy.
Data shows that during quarantine, people are not necessarily gaining weight or losing activity, which might surprise some people.

Data Science Weekly - Issue 337

19 implied HN points • 07 May 20

🕹 Technology Data science Machine Learning Artificial Intelligence Robotics Programming

Data scientists are in high demand, and job opportunities can be found on platforms like Vettery. It's a good time to consider a career change or advance in this field.
Regularization in linear models is important and can be understood visually. Simple explanations can help grasp how these techniques improve model performance.
Freelancing as a data scientist can be rewarding and productive. Many people share their experiences to help others understand what it's like to work independently.

Data Science Weekly - Issue 336

19 implied HN points • 30 Apr 20

🕹 Technology Data science Artificial Intelligence Machine Learning Software Development Visualization

Tornado plots are a unique way to visualize time series data, showing how values change over time. They help us understand trends in a different way than regular graphs.
Categorizing diverse products efficiently is crucial for platforms like Shopify. Proper categorization helps users find similar products faster, making shopping easier.
Blender is an open-source chatbot by Facebook AI that feels more human and engages users better. It's a leap forward for conversational AI technology.

Data Science Weekly - Issue 335

19 implied HN points • 23 Apr 20

🕹 Technology AI Machine Learning Data science Software Development Research

Specification gaming is when AI follows rules exactly but misses the main goal. It's important to design AIs that understand the true purpose of their tasks.
There's a growing need to improve how deep learning studies are reported in healthcare. This helps ensure that new AI tools are effective and trustworthy for patients.
Bias in AI language models, like Google Translate, can reflect societal issues. Efforts are being made to address these biases for fairer translations.

Data Science Weekly - Issue 334

19 implied HN points • 16 Apr 20

🕹 Technology Data science Machine Learning Artificial Intelligence Robotics

Understanding the risks of SARS-CoV-2 is important. We might see continued outbreaks in winter unless we keep social distancing measures.
There's a strong need for AI systems to be understandable. As complex algorithms are used more, we must ensure they are explainable to avoid issues.
Using data science can help improve how we find live music events. By analyzing music data, we can suggest shows that fit users' tastes.

Data Science Weekly - Issue 333

19 implied HN points • 09 Apr 20

🕹 Technology Data science Machine Learning AI Big Data Robotics

Data science roles often don't meet expectations due to issues like unclear job roles and lack of leadership.
Monitoring machine learning models in production is complex and requires careful strategies to ensure effectiveness.
Best practices in time series forecasting help improve the accuracy of predictions by utilizing advanced algorithms and example-driven approaches.

Data Science Weekly - Issue 332

19 implied HN points • 02 Apr 20

🕹 Technology Data science Machine Learning Artificial Intelligence Deep Learning Computing

Agent57 is a new deep learning agent that can beat human scores in all Atari games. It's a big step forward in how we measure AI performance.
During the COVID-19 crisis, it's important to approach data honestly and with curiosity. This helps individuals responsibly discuss topics outside their expertise.
ACM is offering free access to their digital library to support research and learning during the pandemic. This allows more people to access valuable computing resources.

Data Science Weekly - Issue 331

19 implied HN points • 26 Mar 20

🕹 Technology Data science Artificial Intelligence Remote work Machine Learning Software Engineering

The AI field has a serious gender imbalance that can lead to inequalities in AI systems. It's important to address this issue to avoid harming underrepresented groups.
Remote work can be tough for data science teams due to challenges in communication and feelings of isolation. It's crucial to create effective systems to keep the team engaged and productive.
New data-sharing approaches, like HealthMap for coronavirus monitoring, can greatly enhance our ability to respond to public health crises. This represents a shift in how we collect and share important data.

Data Science Weekly - Issue 330

19 implied HN points • 19 Mar 20

🕹 Technology Data science Machine Learning Epidemiology Software Development Analytics

COVID-19 spreads very quickly, especially without measures to control it. Understanding how outbreaks work can help people take action sooner.
Data and models are essential to understanding how COVID-19 will affect local areas. People should act decisively based on available information.
New tools and research in data science are helping track and analyze the impact of COVID-19. These resources are making it easier to study and respond to the pandemic.

Data Science Weekly - Issue 329

19 implied HN points • 12 Mar 20

🕹 Technology Machine Learning Data science AI Wearables Software Development Visualization

Google has developed a new shoe insole that uses machine learning to analyze soccer players' movements, helping them improve their game in real-time.
Human-in-the-Loop Machine Learning is beneficial in many ways, such as avoiding bias, maintaining accuracy, and making processes easier and safer by involving humans in decision-making.
Reinforcement learning is being explored to optimize trading strategies and financial concepts, showcasing its ability to learn and adapt in complex environments.

Data Science Weekly - Issue 328

19 implied HN points • 05 Mar 20

🕹 Technology Data science Machine Learning Artificial Intelligence Neuroscience Natural Language Processing

The brain is not like a computer. Many scientists believe we might be misunderstanding how our brains work by using this comparison.
BERT models are widely used in language processing, but we still need to learn more about how they really function.
Understanding machine learning doesn't have to be complicated. There are resources that explain it in simple terms with practical examples for everyone.

Data Science Weekly - Issue 327

19 implied HN points • 27 Feb 20

🕹 Technology Artificial Intelligence Data science Machine Learning Ethics Software Development

AI startups might not be as promising as they seem and should be closely evaluated. A recent review suggests there's a big difference between AI investments and traditional software investments.
Deep learning is being used to discover new antibiotics, which is crucial due to the rise in antibiotic-resistant bacteria. This shows the real-life applications of AI in solving global health issues.
Ethics in AI is becoming more important, especially with autonomous systems. Companies need to think carefully about the implications of their AI technologies and how they are used.

Data Science Weekly - Issue 326

19 implied HN points • 20 Feb 20

🕹 Technology AI Data science Machine Learning Software Development Computing

AI businesses operate differently than traditional software companies and can seem more like service companies.
Spotify Wrapped is a big marketing campaign that shares users' listening habits over the past year, showcasing engineering efforts to handle data.
Addressing algorithmic bias in AI is becoming more important, and companies are working on ways to make AI fairer and more transparent.

Data Science Weekly - Issue 325

19 implied HN points • 13 Feb 20

🕹 Technology AI Machine Learning Data science Innovation Computing

AI is being closely studied for its effects on the economy, including job creation and productivity. Experts are discussing how to ensure the benefits of AI are widely shared.
Machine learning researchers are advised to choose their problems wisely and manage their time effectively. Simple guidance can help them advance in their careers.
New technologies like brain implants are emerging to restore vision in blind individuals. This innovation shows the potential for technology to enhance human capabilities.