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 171

19 implied HN points • 02 Mar 17
🕹 Technology Data science Machine Learning Artificial Intelligence Software Development Big Data
  1. Deep learning has evolved from basic neural networks to advanced models. This includes popular types like convolutional and recurrent neural networks.
  2. Mathematicians looking at data science should consider what aspects of the job they enjoy. Knowing your interests can help in applying to the right roles.
  3. Time series modeling is tricky because past data points can influence each other. New strategies are needed for better accuracy in this kind of data.

Data Science Weekly - Issue 170

19 implied HN points • 23 Feb 17
🕹 Technology Data science Machine Learning Artificial Intelligence Big Data Statistics
  1. You can use data and APIs to analyze music, like finding the saddest Radiohead song. This shows how data science can be fun and creative.
  2. Neural networks can change images, like making faces look older or younger. This technology is evolving and has cool applications in photography.
  3. Different approaches to statistics, like frequentist and Bayesian, can shape how we think about data. It's important to understand these methods to analyze problems better.

Data Science Weekly - Issue 169

19 implied HN points • 16 Feb 17
🕹 Technology Data science Artificial Intelligence Machine Learning Data Analysis Programming
  1. Longitudinal census data can help in predicting changes in neighborhoods, showing how data science can be applied to social issues.
  2. Deep learning is being used to develop new anticancer drugs, which demonstrates the potential of AI in medicine.
  3. There are many online resources to learn data science effectively, enabling individuals to create their own personalized learning paths.

Data Science Weekly - Issue 168

19 implied HN points • 09 Feb 17
🕹 Technology Data science Machine Learning Artificial Intelligence Big Data Statistics
  1. P-values and null hypothesis testing are often seen as problematic in scientific research, with many issues arising from their use.
  2. Joining a social network app can encourage people to exercise more, showcasing the impact of social interactions on personal habits.
  3. Machine learning is being used to predict parking difficulties, helping drivers find parking spots more efficiently.

Data Science Weekly - Issue 167

19 implied HN points • 02 Feb 17
🕹 Technology Data science Machine Learning Artificial Intelligence Deep Learning Programming
  1. There are better ways to summarize data instead of just using averages, like means and standard deviations. These alternatives are easier to understand and work better with tough data.
  2. Deep learning can create cool projects, like a neural network that rewrites rap lyrics or generates sentences in dead languages. It's amazing how machines can learn and create in new ways.
  3. Data science needs to be a core part of business for it to truly succeed. When integrated well, it can change the game, but it’s important to avoid half-hearted efforts.
Get a weekly roundup of the best Substack posts, by hacker news affinity:

Data Science Weekly - Issue 166

19 implied HN points • 26 Jan 17
🕹 Technology Data science Machine Learning Artificial Intelligence Programming Analytics
  1. Deep learning engineers need to understand hardware and optimization details, not just focus on code and algorithms. This awareness helps improve the performance of neural networks.
  2. There are many resources available for those looking to start a career in deep learning. The demand for knowledgeable engineers in this field is growing rapidly.
  3. Visualizing data can tell different stories depending on how it's presented. It's important to choose the right chart to make the data's message clear.

Data Science Weekly - Issue 164

19 implied HN points • 12 Jan 17
🕹 Technology Machine Learning Artificial Intelligence Data science Software Development Programming
  1. TensorFlow is a powerful tool for machine learning, and you can even use it to train AI to play games like MarioKart.
  2. Machine learning can help analyze things like social media behavior, even identifying tweets sent while someone was drinking.
  3. Understanding machine learning trends and best practices can help you in projects, plus there are many resources to guide you in data science.

Data Science Weekly - Issue 163

19 implied HN points • 05 Jan 17
🕹 Technology Data science Machine Learning Data Visualization Automation Programming
  1. Data visualization projects can be really impressive and help understand complex information. It's interesting to see what creative ways people use to present data.
  2. AI is making its way into the pharmaceutical industry, helping to analyze data and find insights. This shows how important data scientists are becoming in various fields.
  3. Learning about machine learning, like creating algorithms from scratch, can give you a deeper understanding of technology. It's a great way to see how these tools actually work.

Data Science Weekly - Issue 162

19 implied HN points • 29 Dec 16
🕹 Technology Data science Machine Learning Artificial Intelligence Programming Analytics
  1. Some articles highlight interesting stories in data science and research, like how bats communicate or how AI can help hide computer screens when a boss approaches.
  2. It's important to choose and master a data science tool, like R, as it remains popular even though other languages may take its place in the future.
  3. Learning about advanced topics, like Bayesian inference and deep learning techniques, can help you improve your data science skills and understanding.

Data Science Weekly - Issue 161

19 implied HN points • 22 Dec 16
🕹 Technology Data science Machine Learning Artificial Intelligence Big Data Data Analytics
  1. Machine learning can solve big social problems, but it's important to be careful about potential misuse. We should focus on using it wisely to get the best results.
  2. There is a free resource for learning deep learning that makes advanced concepts accessible to everyone. It’s great for beginners who want to get into AI without too much complexity.
  3. XGBoost is a popular tool because it is very effective for classification problems in data science. People should consider using it in their projects for better accuracy.

Data Science Weekly - Issue 160

19 implied HN points • 15 Dec 16
🕹 Technology AI Machine Learning Data science Software Development Algorithms
  1. Neural networks are improving at recognizing drawings, and they will soon be able to analyze them more effectively. This could lead to exciting new developments in how we understand art and creativity.
  2. Deep learning technology is enhancing hearing aids, allowing users to better distinguish voices in noisy environments. This can significantly improve the quality of life for those with hearing difficulties.
  3. AI and machine learning need centralized repositories of information for learning, similar to historical libraries. This is essential for advancing technology and knowledge sharing.

Data Science Weekly - Issue 159

19 implied HN points • 08 Dec 16
🕹 Technology Data science Machine Learning Artificial Intelligence Software Tools Analytics
  1. Deep learning made significant progress in 2016, impacting the field of machine learning greatly. Many organizations are focusing on ensuring that these new technologies are used positively.
  2. There are fun experiments exploring how neural networks can predict handwriting strokes. This shows the creative side of using AI in everyday tasks.
  3. Understanding data's role in infrastructure can highlight where big investments are needed. Maps illustrating America's infrastructure can prepare us for large-scale projects.

Data Science Weekly - Issue 158

19 implied HN points • 01 Dec 16
🕹 Technology Data science Machine Learning Artificial Intelligence Programming Analytics Data Visualization
  1. Machine intelligence is making predictions cheaper, which can create big economic changes. This technology is becoming essential in many fields.
  2. Retailers can use machine learning to manage fresh food stock better, avoiding waste and shortages. This helps them save money and serve customers better.
  3. AI is starting to impact medicine, like an AI that can detect eye diseases as well as human doctors. This could change how we approach healthcare.

Data Science Weekly - Issue 157

19 implied HN points • 24 Nov 16
🕹 Technology AI Machine Learning Data science Big Data Algorithms
  1. AI struggles to fight fake news on platforms like Facebook and Google. This issue raises important questions about how machines can distinguish truth from lies online.
  2. Machine learning can be applied to simple everyday tasks. It shouldn't just be for complex problems; it can help make regular activities easier too.
  3. There are significant challenges in using statistics correctly in data science. Learning from mistakes in statistical reasoning can improve the quality of research.

Data Science Weekly - Issue 156

19 implied HN points • 17 Nov 16
🕹 Technology Data science Machine Learning Artificial Intelligence Automation Algorithms
  1. Mathematicians are working to understand the perfect cup of coffee, using complex calculations about how coffee is extracted from beans. This research could improve how we brew coffee at home or in cafes.
  2. There are concerns about how social media algorithms, like those on Facebook, may spread misinformation and increase political division. This raises important questions about the role of technology in shaping public opinion.
  3. Automating tasks is important for data scientists to reduce mental strain and improve efficiency. Many data scientists can benefit from spending more time on automation instead of handling repetitive tasks manually.

Data Science Weekly - Issue 155

19 implied HN points • 10 Nov 16
🕹 Technology AI Data science Machine Learning Software Development Statistics
  1. AI technology is becoming more accessible, with tools being developed to enhance video communication and creativity directly through mobile apps.
  2. Machine learning is being applied in innovative ways, like LipNet, which helps the hard of hearing by accurately interpreting lip movements.
  3. There's a growing emphasis on the integration of AI in various fields, such as pharmaceutical research, urban transit design, and gaming, showcasing its versatility and impact.

Data Science Weekly - Issue 154

19 implied HN points • 03 Nov 16
🕹 Technology Data science Machine Learning Statistics Programming AI Analytics
  1. A/B testing can go wrong if you check results too often. It's important to avoid stopping tests too soon based on p-values.
  2. Many data science projects fail due to misunderstandings and poor planning. Recognizing common pitfalls can help ensure better outcomes.
  3. Using advanced techniques like neural networks can enhance tasks like image resolution. This shows how technology is evolving in data science.

Data Science Weekly - Issue 153

19 implied HN points • 27 Oct 16
🕹 Technology AI Machine Learning Data science Autonomous Vehicles Cloud Computing
  1. Self-driving cars are not fully ready for everyday use yet, so we should be cautious when thinking about how they will change transportation.
  2. Artificial intelligence has the potential to transform various industries, similar to how electricity changed the world.
  3. Data is becoming a vital part of decision-making in many areas, including sports like basketball, changing how teams operate.

Data Science Weekly - Issue 152

19 implied HN points • 20 Oct 16
🕹 Technology Data science Machine Learning Artificial Intelligence Statistics Programming
  1. Statistics can sometimes make it hard for people to feel empathy. When faced with numbers, they might not connect emotionally with the human stories behind them.
  2. Using tools like R isn't just for big business tasks; they can also be handy in personal life, such as estimating the value of your own vehicle.
  3. There are new advancements in speech recognition that are reaching accuracy levels similar to humans. This could really change how we interact with technology through conversation.

Data Science Weekly - Issue 151

19 implied HN points • 13 Oct 16
🕹 Technology Data science Machine Learning AI Programming
  1. Machines are getting better, but humans still have unique abilities that machines can't replicate, especially in creative and critical thinking tasks.
  2. There's a growing demand for open data, but different groups have different expectations and definitions of what 'open' means.
  3. Sharing your side projects online can really benefit your career; it makes your GitHub profile a great part of your résumé and lets others contribute to your work.

Data Science Weekly - Issue 150

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

Data Science Weekly - Issue 149

19 implied HN points • 29 Sep 16
🕹 Technology Data science Machine Learning Artificial Intelligence Statistics Programming
  1. Machine learning can solve real problems effectively. Proper techniques can really change how we predict things like delivery times.
  2. There's a history of debate around the impact of smoking. A famous statistician once argued against the idea that smoking causes cancer, saying that quick conclusions can be misleading.
  3. Data science can help analyze cultural trends. For example, researchers used data to explore how cars are represented in rap music, showing how data analysis can reveal interesting insights.

Data Science Weekly - Issue 148

19 implied HN points • 22 Sep 16
🕹 Technology Data science Machine Learning Artificial Intelligence Statistics Programming
  1. Blind people process numbers using a part of their brain usually linked to images. This shows how math can be visualized differently for those who can't see.
  2. No one really understands how modern AI neural networks work, which can lead to unpredictable problems. This raises concerns about their reliability in the future.
  3. Venn diagrams are often used to explain data science, but the field still lacks a clear definition. People have different ways of describing their roles in this diverse area.

Data Science Weekly - Issue 147

19 implied HN points • 15 Sep 16
🕹 Technology AI Machine Learning Data science Computing Automation
  1. Deep learning works well not just because of math but also due to physics, which helps reduce complexity in models.
  2. AI is a tool, similar to a calculator or smartphone, and we need to adapt to its presence in our lives rather than fear it will replace us.
  3. Machine learning can be learned quickly, and even a total beginner can start applying it in a work setting with some dedication.

Data Science Weekly - Issue 146

19 implied HN points • 08 Sep 16
🕹 Technology Data science Machine Learning Artificial Intelligence Software Development Web Development
  1. Understanding causality is important in data science. It helps in analyzing data and making better decisions about what affects what.
  2. Machine learning can be applied in many surprising areas, like farming. For instance, a farmer used deep learning to sort cucumbers, showcasing how tech can help everyday tasks.
  3. A/B testing is common in tech companies to improve products, but it can be tricky. If not done carefully, it can lead to biased results, especially in dynamic systems like ride-sharing.

Data Science Weekly - Issue 145

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

Data Science Weekly - Issue 144

19 implied HN points • 25 Aug 16
🕹 Technology Data science Machine Learning Artificial Intelligence Programming Data Visualization Statistical Analysis
  1. Neural networks are inspired by how our brain's neurons work and help simulate intelligent behavior. They have a long history and have evolved significantly over time.
  2. Counting can be surprisingly difficult in data science, often requiring more effort than expected. Even experienced data scientists face challenges with counting tasks.
  3. Data-driven decision making is important, but we must be cautious. Ignoring the nuances can lead to pitfalls, so it's crucial to stay aware and informed.

Data Science Weekly - Issue 143

19 implied HN points • 18 Aug 16
🕹 Technology Data science Machine Learning Artificial Intelligence Programming Visualization
  1. Machine learning can help analyze personal health data, like weight, by tracking various factors that affect it. Keeping a simple record, like a CSV file, can make this process easier.
  2. There are creative ways to visualize data, like global shipping traffic or Olympic medals, which can make insights more engaging. Using tools like GIFs can bring data to life.
  3. Combining different programming languages, like Python and R, can enhance data science work instead of arguing about which one is better. Each has its strengths and can be used together effectively.

Data Science Weekly - Issue 142

19 implied HN points • 11 Aug 16
🕹 Technology Data science Artificial Intelligence Machine Learning Data Analysis Data Visualization
  1. Data analysis can be used to understand patterns, like analyzing tweets to see how they reflect someone's personality.
  2. Artificial intelligence is developing, but there are still limitations in how machines understand human language.
  3. Using technology like NASA imagery and machine learning can help improve agricultural predictions and trading.

Data Science Weekly - Issue 141

19 implied HN points • 04 Aug 16
🕹 Technology Data science Machine Learning Artificial Intelligence Ethics Statistics
  1. Algorithms play a big role in our daily lives, but we need to make sure they are responsible and fair in how they impact us.
  2. It's important to think about ethics in data science, including how algorithms affect people and how to create them thoughtfully.
  3. Machine learning can reveal valuable insights from data, like analyzing hotel reviews or even facial data from Twitter, but it still has its limitations.

Data Science Weekly - Issue 140

19 implied HN points • 28 Jul 16
🕹 Technology Data science Machine Learning Artificial Intelligence Programming Statistics
  1. Data cleaning takes a lot of time for data scientists, often more than half of their work hours. It's essential to prepare the data before using machine learning models.
  2. New technology is allowing researchers to transform sketches into realistic photos. This shows how AI can reverse processes, not just create images.
  3. There's a growing interest in applying data science to understand complex topics, like U.S. Supreme Court cases, making information more accessible to the public.

Data Science Weekly - Issue 139

19 implied HN points • 21 Jul 16
🕹 Technology Data science Artificial Intelligence Machine Learning Statistics Software Engineering
  1. A neural network can write creative stories, like a funny version of a Harry Potter book. It's a cool way to see how artificial intelligence can create new and entertaining content.
  2. Creating a beer recommendation engine can help people find new beers they might like. It combines personal taste with data science to make better choices for beer lovers.
  3. Understanding bias in data is super important for getting accurate results. Even simple mistakes can lead to huge errors, so being careful with data analysis is key.

Data Science Weekly - Issue 138

19 implied HN points • 14 Jul 16
🕹 Technology Data science Machine Learning Artificial Intelligence Programming Statistics
  1. There's ongoing research in AI that allows it to write its own code. This could change how we view software development.
  2. Data from mobile phones can help understand literacy rates in developing countries. It's a surprising new way to gather important social data.
  3. Machine learning can identify signs of depression by analyzing speech patterns. This could help in early diagnosis and better mental health support.

Data Science Weekly - Issue 137

19 implied HN points • 07 Jul 16
🕹 Technology Data science Machine Learning Artificial Intelligence Programming Statistics
  1. There are six basic emotional arcs that make up all stories, which can be found through data mining novels.
  2. The Toronto Raptors are using IBM’s Watson to help them make smart decisions in drafting players for their basketball team.
  3. Python is a slower programming language because it is dynamically typed and interpreted, which affects how data is stored and processed.

Data Science Weekly - Issue 136

19 implied HN points • 30 Jun 16
🕹 Technology Data science Machine Learning Artificial Intelligence Statistics Computer Vision
  1. Correlation does not mean one thing causes another, but understanding what does imply causation is important. It's a key part of interpreting data correctly.
  2. Machine learning can help improve hiring processes by making predictions based on data rather than following fixed rules. This could lead to better hiring decisions.
  3. Algorithms can have biases because they are influenced by human behavior and decisions. It's essential to recognize this to ensure fairness in technology.

Data Science Weekly - Issue 135

19 implied HN points • 23 Jun 16
🕹 Technology Machine Learning Artificial Intelligence Data science Software Development Programming
  1. Machine learning is becoming crucial for businesses, and understanding its implications can help you stay ahead. It's important to keep learning about new tech like machine learning instead of just focusing on the latest trends.
  2. Companies like Google are adapting to prioritize machine learning in their products. This means they are training their programmers to better integrate AI into their work.
  3. Real-world applications of data science show it isn't just theory. Companies use data science to improve their operations and products, making it more understandable for everyone.

Data Science Weekly - Issue 134

19 implied HN points • 16 Jun 16
🕹 Technology Data science Machine Learning Artificial Intelligence Statistics Data Visualization
  1. Neural networks are becoming essential for solving many tough computer problems, like identifying faces or understanding speech.
  2. Netflix believes its recommendation system is incredibly valuable, saving the company over $1 billion a year by personalizing user suggestions.
  3. R programming has recently surpassed SAS in scholarly use, showing a shift in data analysis preferences among researchers.