The hottest Statistical Analysis Substack posts right now

And their main takeaways
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Top Technology Topics

Data Science Weekly - Issue 50

Data Science Weekly Newsletter • 19 implied HN points • 06 Nov 14
🕹 Technology Data science Machine Learning Artificial Intelligence Statistical Analysis Visualization
  1. Learning about neural networks can start from the basics before diving into complex topics. It's helpful to understand the core concepts first.
  2. Visualizing data is important for understanding text data better. There are interactive tools available that can help with this.
  3. Choosing the right statistical analysis method is crucial for data science. There are guides that can help you figure out which analysis to use based on your data.

Data Science Weekly - Issue 44

Data Science Weekly Newsletter • 19 implied HN points • 25 Sep 14
🕹 Technology Data science Machine Learning Big Data Artificial Intelligence Statistical Analysis
  1. There's a big data event called Strata Conference + Hadoop World happening in New York. It's a great place for anyone interested in data science and big data to learn and network.
  2. Many researchers are working on cool projects like predicting NYC taxi tips and detecting anomalies in building energy usage. These projects show the real-life applications of data science.
  3. There are various resources available for learning and improving skills in data science, including books, online courses, and articles. It's a good time to dive in and explore!

Data Science Weekly - Issue 41

Data Science Weekly Newsletter • 19 implied HN points • 04 Sep 14
🕹 Technology Data science Machine Learning Big Data Statistical Analysis Tech Conferences
  1. The Strata + Hadoop World event is a big deal for people in data science and business. It's a great place to connect and learn about using big data effectively.
  2. Using Bayesian models can help solve unique problems, like predicting where Uber riders are headed. This shows how math can be applied in real-world scenarios.
  3. Choosing the right data scientist for your team is crucial. A good hire can make a big difference, while a poor one can lead to costly mistakes.

Data Science Weekly - Issue 33

Data Science Weekly Newsletter • 19 implied HN points • 10 Jul 14
🕹 Technology Data science Machine Learning Big Data Statistical Analysis Data Visualization
  1. Random forests are a powerful tool in data science that can help understand how different parts of the algorithm work and improve its use.
  2. There are two main approaches to statistics: frequentism and Bayesianism, and they can lead to different solutions for data analysis problems.
  3. Data visualization is important for making complex information easier to understand, and there are many great tools available to help with this.

Data Science Weekly - Issue 31

Data Science Weekly Newsletter • 19 implied HN points • 26 Jun 14
🕹 Technology Data science Machine Learning Artificial Intelligence Programming Statistical Analysis
  1. Extreme Learning Machines are a way to train neural networks using a concept called reservoir computing. This method can improve learning efficiency.
  2. Pandas is a Python tool that makes it easier for businesses to do statistical analysis, similar to what universities do. This bridge helps teams communicate and analyze data better.
  3. Understanding the differences between AI, machine learning, and data mining is essential. These fields each have unique roles in data analysis and applications.
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Data Science Weekly - Issue 23

Data Science Weekly Newsletter • 19 implied HN points • 01 May 14
🕹 Technology Data science Machine Learning Artificial Intelligence Statistical Analysis Programming
  1. Becoming a Data Scientist is more challenging than many people think. It's not just about completing an online course; real skills and experience are necessary.
  2. Building a successful Data Science team can be very difficult. Companies often struggle to find the right talent and create an environment where Data Scientists can be productive.
  3. Understanding why some images gain popularity online can help in predicting their success. Researchers are exploring the factors that contribute to an image's view count.

How to Do a Proper Self-Experiment, and Why Your “N” Doesn’t Technically Equal “1”

Harnessing the Power of Nutrients • 19 implied HN points • 11 Sep 11
🏥 Health & Wellness Self-experimentation Statistical Analysis
  1. For a proper self-experiment, the number of repeated observations is crucial to demonstrate cause and effect.
  2. Randomization in experiments helps control for unknown variables and factors, ensuring more accurate results.
  3. Maintaining consistency in experimental conditions and using statistical tests can help in determining if there is a significant difference between responses.

[in case you missed it] Data Science Weekly - Issue 316

Data Science Weekly Newsletter • 0 implied HN points • 14 Dec 19
🕹 Technology Data science Machine Learning Programming Artificial Intelligence Statistical Analysis
  1. NeurIPS 2019 saw a huge increase in submissions, with an acceptance rate of 21.6%. This highlights the growing interest and importance of data science research.
  2. Many data science teams use both R and Python, which can create challenges. Finding ways to combine these languages is key to enhancing team collaboration.
  3. Training projects like predicting dog adoption outcomes and understanding game strategies show the real-world applications of data science in improving lives and decision-making.

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

Data Science Weekly Newsletter • 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 356

Data Science Weekly Newsletter • 0 implied HN points • 20 Sep 20
🕹 Technology Data science Machine Learning Artificial Intelligence Programming Statistical Analysis
  1. The ICML conference is a big deal for machine learning professionals, bringing together people from different backgrounds to share ideas.
  2. Apache Arrow is an essential library for data processing that aims to improve how we handle and share data efficiently.
  3. Transformers, a popular type of neural network, are closely related to Graph Neural Networks and have made significant contributions to natural language processing.

So you want to be an amateur election forecaster? Here's step zero.

Matt’s Five Points • 0 implied HN points • 31 Oct 18
🇺🇸 U.S. Politics Election forecasting Public Opinion Voting behavior Statistical Analysis
  1. You can start forecasting elections easily using a simple Excel simulation tool. Just change the win probabilities for Senate races, and the simulation will quickly show you different election outcomes.
  2. Good election forecasting requires gathering data and creating win probabilities, which can be a fun challenge. Getting started is much easier than you might think, so don't be intimidated.
  3. While simple models are easy to run, accurate forecasting can be more complex. Serious models account for many details in how elections work, but you can still enjoy basic modeling without being an expert.

Nerd(s)

Matt’s Five Points • 0 implied HN points • 01 Nov 11
🎭️ Culture Family life Childhood Holidays Social Dynamics Statistical Analysis
  1. The total efficiency of trick-or-treating was impressive with 43 approaches per hour in a neighborhood with many families.
  2. Anna had a candy haul rated at 445 points, but the variety was lacking in higher-end candies.
  3. The estimated candy consumption plan suggests the kids will run out of candy by early January, but realistically, it might be much sooner.

DCF Myth 3.2: If you don't look, its not there!

Musings on Markets • 0 implied HN points • 23 May 16
💰 Finance Valuation Investing Risk management Statistical Analysis
  1. Using simulations for financial valuations helps capture uncertainty. Instead of just using one guess for numbers, you can use a range to see different possible outcomes.
  2. Probability distributions are important in understanding risks and making better financial decisions. They can show how likely different outcomes are, which is essential for planning.
  3. Modern tools like Excel add-ons make simulations easier to run. You can use programs that help visualize the potential values of an investment based on various inputs.

Alternatives to the CAPM: Part 2: Proxy Models

Musings on Markets • 0 implied HN points • 29 Apr 11
💰 Finance Investment Theory Market behavior Statistical Analysis Financial Metrics Investing Risk management Economic Theory Market Analysis Quantitative Analysis
  1. Proxy models move away from traditional finance theories like CAPM, focusing instead on how markets actually price investments. They try to explain returns based on observable factors rather than assumptions about investor behavior.
  2. Research by Fama and French found that factors like market capitalization and price-to-book ratios are better at explaining stock returns than the original CAPM betas. This means smaller companies and those with lower price-to-book ratios tend to have higher returns.
  3. While proxy models can improve expected return calculations, they come with risks like data mining and standard error problems. This means the results may not always be reliable or may misrepresent the true risk involved.

[in case you missed it] Data Science Weekly - Issue 444

Data Science Weekly Newsletter • 0 implied HN points • 29 May 22
🕹 Technology Data science Machine Learning Artificial Intelligence Software Development Statistical Analysis
  1. Good ML systems need careful design and planning. It's important to know the difference between research and real-world applications.
  2. Data isn't always the best way to make decisions. Sometimes relying too much on data can lead to worse outcomes.
  3. New AI technologies are changing how we think about intellectual property. We might need new laws to keep up with inventions created by machines.

[in case you missed it] Data Science Weekly - Issue 411

Data Science Weekly Newsletter • 0 implied HN points • 10 Oct 21
🕹 Technology Data science Machine Learning Artificial Intelligence Data Visualization Statistical Analysis
  1. Freelancing in data visualization can be tricky. It's important to learn from mistakes and adjust strategies for better outcomes.
  2. Combining AI with art can bring lost masterpieces back to life. Using algorithms to mimic an artist's style can recreate vibrant colors in old, damaged artworks.
  3. Building a strong data team is essential for businesses. Companies need to focus on data strategy, governance, and analytics to harness the power of data effectively.