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 11

19 implied HN points • 06 Feb 14
🕹 Technology Data science Machine Learning Big Data Visualization Analytics Artificial Intelligence
  1. Data visualization is important in data science, especially for large-scale projects. It helps people understand data flows and make better decisions.
  2. Bringing machine learning models from a lab to real-world applications is crucial for impact. This requires integrating tools and strategies to analyze data in production.
  3. Learning about user experience and changing tastes is key for making good product recommendations. It's important to consider what users will enjoy now and in the future.

Data Science Weekly - Issue 10

19 implied HN points • 30 Jan 14
🕹 Technology Data science Machine Learning AI Data Mining Statistics
  1. Data mining can help predict which countries will win medals in the Winter Olympics. It can reveal trends and reasons behind particular nations' success.
  2. Deep learning aims to make computers think like humans. It showcases the progress in teaching machines to learn and improves how they process information.
  3. Data science plays a crucial role in various industries, like Foursquare and New York's Fire Department, to analyze data and improve services or predict events.

Data Science Weekly - Issue 9

19 implied HN points • 23 Jan 14
🕹 Technology Data science Machine Learning Artificial Intelligence Algorithms Big Data
  1. Geoffrey Hinton is a key figure in AI and believes the brain stores memories like a hologram, spreading them across neurons.
  2. A math genius hacked an online dating site by using statistics to create a profile that would grab the attention of the women he liked.
  3. Big Data is starting to transform agriculture, helping farmers use data to improve their practices and increase yields.

Data Science Weekly - Issue 8

19 implied HN points • 16 Jan 14
🕹 Technology Data science Machine Learning Artificial Intelligence Big Data Bioinformatics
  1. US military scientists have figured out how to identify a small group of people who can spread messages effectively through networks. This group acts like a 'seed' to amplify the message to a larger audience.
  2. Data science is becoming crucial in various industries, like banking and healthcare, to help solve problems and improve services. Understanding data can give companies a competitive edge.
  3. Learning about data science is more accessible than ever, with resources like free eBooks and tutorials available online. This makes it easier for anyone interested to start their journey in the field.
Get a weekly roundup of the best Substack posts, by hacker news affinity:

Data Science Weekly - Issue 7

19 implied HN points • 09 Jan 14
🕹 Technology Data science Machine Learning Artificial Intelligence Analytics Programming
  1. Google has developed a smart neural network that can read house numbers in street views quickly and accurately, mixing tech with human-like skills.
  2. Neural networks and Machine Learning as a Service are becoming important tools for businesses, offering new ways to analyze data and make predictions.
  3. Platforms like Netflix use data in unique ways to classify movies, breaking them down into thousands of specific genres to better cater to viewer preferences.

Data Science Weekly - Issue 6

19 implied HN points • 02 Jan 14
🕹 Technology Data science Machine Learning Artificial Intelligence Analytics
  1. Machine learning is becoming really popular in education and helps improve various fields, like online dating and data analysis. Many students at universities, like Stanford, are eager to learn about it.
  2. Deep learning models are advancing quickly, and some can now even beat human players in video games. This shows how powerful these technologies are getting.
  3. Data scientists need to have a mix of skills in business, math, and coding. This combination helps them solve problems and create better algorithms in the industry.

Data Science Weekly - Issue 5

19 implied HN points • 26 Dec 13
🕹 Technology Data science Machine Learning Artificial Intelligence NLP
  1. Data science combines various skills and knowledge, making it important for professionals to share their experiences and lessons learned.
  2. Machine learning can be applied in surprising ways, like developing vaccines or improving image recognition, showcasing its versatility in different fields.
  3. There are valuable resources and guides available for those interested in data science, making it easier for beginners to get started in the field.

Data Science Weekly - Issue 4

19 implied HN points • 19 Dec 13
🕹 Technology Data science Artificial Intelligence Machine Learning Analytics
  1. Data analysis can reveal surprising patterns, like how riders use Uber, by looking at location and time data.
  2. Machine learning is being used in innovative ways, such as predicting stock prices and improving email marketing, making processes smarter.
  3. Even in competitive sports like cycling, there's a gap in using data analytics effectively, despite having lots of available data.

Data Science Weekly - Issue 3

19 implied HN points • 12 Dec 13
🕹 Technology Data science Machine Learning Big Data Healthcare Tech Data Analytics
  1. Data science is important for understanding and predicting human behavior, especially in areas like media and health. This helps create better metrics and healthcare solutions.
  2. Big data can revolutionize industries, such as travel and sports, by analyzing large amounts of information to improve decision making and user experiences.
  3. Training and collaboration are key in data science. Courses and mentorship can help upcoming data scientists gain the skills needed to succeed in the job market.

Data Science Resume Advice - Weekly Update #5

0 implied HN points • 08 Jan 16
🚌 Education Career Advice Job Market Resume Tips Professional development Data science
  1. Talking to hiring managers can give you great tips for your resume. They have insights on what they really want to see.
  2. You don't need a PhD to get an interview in data science. There are effective ways to stand out even without it.
  3. Feel free to ask questions while putting your resume together. Getting advice can boost your confidence and improve your chances.

[in case you missed it] Data Science Weekly - Issue 299

0 implied HN points • 17 Aug 19
🕹 Technology AI Data science Machine Learning Algorithms Software Development
  1. AI is now being used to improve video gaming, like training in soccer using a new football simulator. This shows how far technology has come in understanding games.
  2. Nvidia is making big strides in AI language models, making them faster and more efficient. This means we could see better and more responsive AI conversations soon.
  3. For those wanting to become data scientists, it's smarter to get a related job first and learn on the job. Skills can be built up as you go instead of trying to learn everything at once.

[in case you missed it] Data Science Weekly - Issue 300

0 implied HN points • 25 Aug 19
🕹 Technology Data science AI Machine Learning Deep Learning Software Development
  1. There's a new AI optimizer called RAdam that can help improve the accuracy of AI models. It automatically adjusts the learning rate based on training conditions.
  2. Deep learning is an area of study that's compared to classical methods and explores various neural network models. Understanding these models can help grasp the foundations of modern AI.
  3. Data scientists are in high demand, and there are resources available to help newcomers prepare for training programs. This can lead to job opportunities in the field.

[in case you missed it] Data Science Weekly - Issue 247

0 implied HN points • 18 Aug 18
🕹 Technology Data science Machine Learning Artificial Intelligence Programming Research
  1. Coursera has a new AI tool that helps companies see what skills their employees are learning. This way, workers can find out what skills they might still need.
  2. A small team of student AI coders has achieved success that rivals Google's machine-learning code. This shows that innovative work in AI can come from anywhere, not just big companies.
  3. Facebook's chief AI scientist believes that better collaboration between industry and academia is essential for AI innovation. This partnership can lead to exciting developments in the field.

[in case you missed it] Data Science Weekly - Issue 246

0 implied HN points • 11 Aug 18
🕹 Technology Data science Machine Learning Artificial Intelligence Statistical Analysis Software Development
  1. Data science can balance fast experimentation with careful research. It's important for teams to adapt quickly while also planning for the long term.
  2. Understanding how land in the U.S. is used can highlight ways to create wealth. Different areas have various productive uses that affect the economy.
  3. Automated machine learning tools like Auto-Keras can help people without a data science background to easily access deep learning models.

[in case you missed it] Data Science Weekly - Issue 238

0 implied HN points • 16 Jun 18
🕹 Technology Machine Learning Artificial Intelligence Data science Energy Efficiency Software Development
  1. Neural networks can struggle with humor if they don't have enough examples to learn from. More data might help them learn to tell better jokes.
  2. Machine learning is expected to become more effective on smaller devices, like smartphones, thanks to energy-efficient technologies. This could solve many current problems.
  3. Data cleaning is a big part of data science, often taking up to 80% of a person's time. Using tools like Python and Pandas can help make this process easier.

[in case you missed it] Data Science Weekly - Issue 311

0 implied HN points • 09 Nov 19
🕹 Technology Artificial Intelligence Machine Learning Data science Healthcare Tech Software Development
  1. Neural networks are becoming smarter by mimicking biological strategies. This could be key to creating truly intelligent machines.
  2. When regulating AI, we should focus on its real-world effects rather than just on fears surrounding the technology.
  3. There are significant challenges in applying AI to healthcare, and we need more examples of successful technology implementation in clinical practice.

[in case you missed it] Data Science Weekly - Issue 319

0 implied HN points • 04 Jan 20
🕹 Technology Data science Machine Learning AI Programming Research
  1. Becoming an independent researcher can be tough, but it may open up new paths for publishing. There's a balance to consider between freedom and potential challenges.
  2. AI is making strides in reading medical images like mammograms. This tech might help doctors find signs of cancer earlier and more accurately than before.
  3. Working on data projects is not just good for learning; it's super useful for impressing future employers. Showing what you've done can set you apart in job applications.

[in case you missed it] Data Science Weekly - Issue 321

0 implied HN points • 18 Jan 20
🕹 Technology Data science Machine Learning Artificial Intelligence Analytics Programming
  1. Hiring smart people can be tricky because many recruiters rely on strict rules and fancy degrees. There’s a chance to find great talent if you look beyond the typical criteria.
  2. As machine learning gets better, it can sometimes cause mistakes in human decision systems, known as the 'Uncanny Valley.' It's important to design these systems carefully to avoid problems.
  3. TinyML is an exciting area of machine learning that lets small devices analyze data using minimal power. This means everyday items like printers and cars can now perform complex tasks with smarter tech.

[in case you missed it] Data Science Weekly - Issue 336

0 implied HN points • 02 May 20
🕹 Technology Data science Artificial Intelligence Machine Learning Natural Language Processing Software Development
  1. Tornado plots are a unique way to display time series data, showing how values change over time in a more dynamic way. They help visualize trends in a more engaging format.
  2. An open-source chatbot named Blender, developed by Facebook, is designed to be more human-like in conversations. It is the largest chatbot model available and can be used by other researchers.
  3. The use of machine learning (ML) for optimizing chip design is becoming important as hardware needs to keep up with advancing technology. It could help speed up the design process significantly.

[in case you missed it] Data Science Weekly - Issue 340

0 implied HN points • 31 May 20
🕹 Technology Artificial Intelligence Machine Learning Data science Automation Software Development
  1. AI has some issues that limit its usefulness in businesses. By understanding these problems, businesses can find ways to effectively use AI and even save money.
  2. Human and machine cooperation is essential, and fully automating processes might not be the best approach. We should find ways for machines and people to work better together.
  3. Learning about basic machine learning models is still very important. Many companies don't need advanced techniques, so knowing the basics can help you in real-world jobs.

[in case you missed it] Data Science Weekly - Issue 343

0 implied HN points • 21 Jun 20
🕹 Technology AI Data science Machine Learning Algorithms Software Development
  1. Image GPT can create images just like large language models create text. This means we can now generate detailed images by understanding pixel patterns.
  2. MLOps helps data scientists work better together by automating tasks like testing and version control. This makes it easier to manage machine learning projects.
  3. There is no proper regulation for algorithms that affect our daily lives. A group of citizens should help oversee how these algorithms are used to ensure fairness and accountability.

[in case you missed it] Data Science Weekly - Issue 280

0 implied HN points • 06 Apr 19
🕹 Technology Artificial Intelligence Data science Machine Learning Big Data Software Development
  1. DeepMind is a big player in AI, and there's tension now that Google owns it. The question of who really controls AI is important.
  2. Warby Parker used an algorithm to help people try on glasses virtually, making shopping easier and more fun.
  3. MIT is experimenting with AI to create new types of food, showing that technology can change the way we think about flavors.

[in case you missed it] Data Science Weekly - Issue 346

0 implied HN points • 12 Jul 20
🕹 Technology AI Machine Learning Data science Programming Analytics
  1. A workshop at the Santa Fe Institute explored the meaning and understanding in AI, involving participants from different fields to discuss how machines might understand like living beings.
  2. The cost of training AI is dropping much faster than expected, making it easier for companies to adopt AI technology in the coming years.
  3. Training Generative Adversarial Networks (GANs) presents challenges, but new algorithms are being developed to improve stability and performance in machine learning.

[in case you missed it] Data Science Weekly - Issue 347

0 implied HN points • 19 Jul 20
🕹 Technology Data science Machine Learning AI Robotics Software Development
  1. Netflix is improving its data efficiency by using a dashboard that helps everyone see costs and usage trends. This way, decision-makers can make better choices based on clear information.
  2. Creating a strong portfolio and resume is really important for landing a data science job. Focus on showcasing your best skills and experiences to attract employers.
  3. There's a shift in building robots to assist humans instead of replacing them. The future should focus on robots that enhance our capabilities rather than take over our jobs.

[in case you missed it] Data Science Weekly - Issue 350

0 implied HN points • 09 Aug 20
🕹 Technology AI NLP Data science Machine Learning Software Development
  1. GPT-3 can create very human-like text and it can even write computer programs with just a few examples. This shows how advanced AI language models are becoming.
  2. Many languages are spoken around the world, but most natural language processing work has focused only on English. It's important to include other languages in research.
  3. Graph technologies are being used to solve complex business problems, such as making recommendations and detecting fraud. They are becoming essential tools in data science.

[in case you missed it] Data Science Weekly - Issue 351

0 implied HN points • 16 Aug 20
🕹 Technology Data science Machine Learning Artificial Intelligence Computer Vision Natural Language
  1. The Mona Lisa Effect is a fun digital experience where a portrait's eyes seem to follow you. You can try it by using your webcam.
  2. Maintaining machine learning models in production is challenging, but there are practical ways to manage issues like data contamination and model misbehavior.
  3. AI economics are important to understand, especially for long-tailed data distributions, so that machine learning teams can create better and more profitable AI applications.

[in case you missed it] Data Science Weekly - Issue 251

0 implied HN points • 15 Sep 18
🕹 Technology Data science Machine Learning Artificial Intelligence Software Development Research
  1. AI systems, like Amazon's Echo, rely on a mix of resources including technology, labor, and nature to work effectively.
  2. Fake news can influence people's voting choices, and there's a mathematical way to show how it happens.
  3. Machine learning tools are becoming easier to use, allowing people to explore and understand models without needing to write code.

[in case you missed it] Data Science Weekly - Issue 257

0 implied HN points • 27 Oct 18
🕹 Technology Data science Machine Learning Artificial Intelligence Big Data Software Development
  1. Neural networks can help create new ideas, like unique Halloween costumes. This shows how AI can spark creativity in fun ways.
  2. Uber has built a massive data platform that handles over 100 petabytes of data quickly. This helps them manage and analyze huge amounts of information efficiently.
  3. There are new ways to learn data science, such as hands-on courses with mentoring and payment plans that let you pay after getting a job. This makes it easier for more people to get into the field.

Data Science Weekly - Issue 208

0 implied HN points • 17 Nov 17
🕹 Technology Data science Machine Learning Artificial Intelligence Software Development Conferences
  1. Neural networks are changing how we develop software, not just being another tool in machine learning. They represent a big shift towards 'Software 2.0' which impacts many projects.
  2. Evolution strategies are a method in machine learning that can be explained visually, making it easier for people to understand how they work.
  3. There is a growing interest in how AI can be used in creative ways, such as in cooking or video games, showcasing its potential beyond traditional applications.

[in case you missed it] Data Science Weekly - Issue 359

0 implied HN points • 11 Oct 20
🕹 Technology Data science Machine Learning Artificial Intelligence Software Development Open Source
  1. Arduino is making it easier for everyone to use machine learning by providing resources to get started quickly. You can learn to set up voice recognition on devices like the Arduino Nano.
  2. TensorSensor is a new tool that helps programmers understand and debug deep learning code easier by visualizing tensor operations. This can be really helpful for those new to coding in this area.
  3. Papers with Code now links machine learning research with relevant code, making it easier to access both studies and their implementations for better understanding and usage.

[in case you missed it] Data Science Weekly - Issue 293

0 implied HN points • 06 Jul 19
🕹 Technology AI Machine Learning Data science Analytics Programming
  1. The State of AI Report 2019 highlights how fast AI is growing and important developments from the past year.
  2. Machine learning is now being used to translate languages that were previously lost, opening up new ways to understand history.
  3. There are many resources and guides available for those wanting to get started in data science, covering everything from building a portfolio to writing a great resume.

[in case you missed it] Data Science Weekly - Issue 303

0 implied HN points • 14 Sep 19
🕹 Technology AI Data science Machine Learning Automation Quantum Computing
  1. Stitch Fix is using machine learning to help customers pick outfits that match their style. It shows how technology can personalize shopping experiences.
  2. There's a push to protect workers from being replaced by automation. Some suggest taxing companies that use robots to keep people employed.
  3. AI is transforming fields like biology, especially in analyzing images. It highlights how technology is changing research and discovery in science.

[in case you missed it] Data Science Weekly - Issue 363

0 implied HN points • 08 Nov 20
🕹 Technology Data science Machine Learning Artificial Intelligence Software Development Computing
  1. Synthetic biology has advanced significantly in its second decade, showcasing real achievements beyond just hype from the first decade.
  2. Data poisoning attacks can seriously impact machine learning models by manipulating their predictions, so it's important to use trusted data.
  3. Building a strong data science portfolio and tailoring your resume are key steps in landing a data science job.

[in case you missed it] Data Science Weekly - Issue 308

0 implied HN points • 20 Oct 19
🕹 Technology Machine Learning Data science AI Robotics Software Development
  1. Neural networks can solve real-world problems like a robot hand solving a Rubik's Cube. This shows they can learn and adapt in unpredictable situations.
  2. There's a shift happening in machine learning tools, with more researchers choosing PyTorch over TensorFlow. While TensorFlow is still popular in the industry, this could change soon.
  3. Companies can use a smart model to find the best regions for hiring offshore talent. This helps them build stronger remote teams by targeting specific skills.

[in case you missed it] Data Science Weekly - Issue 369

0 implied HN points • 20 Dec 20
🕹 Technology AI Machine Learning Data science Programming Analytics
  1. Companies are now changing how they present information because machines and AI read their reports too. They're trying to make it easier for algorithms to understand, sometimes even avoiding negative words that might confuse them.
  2. Monitoring machine learning in production is crucial. It's important to catch any unusual patterns or changes in how models behave to ensure they keep performing well.
  3. Artificial intelligence is being developed to better interact with humans. By using virtual environments, researchers are teaching AI to mimic human behaviors and improve interaction quality.

[in case you missed it] Data Science Weekly - Issue 370

0 implied HN points • 27 Dec 20
🕹 Technology AI Data science Machine Learning Programming Analytics
  1. 2020 saw significant advancements in AI, especially with neural volume rendering and models that can learn rules themselves.
  2. Data scientists are in high demand, and platforms like Vettery can help job seekers connect with employers.
  3. Resources are available to help aspiring data scientists improve their skills, build portfolios, and create impactful resumes.