The hottest Data Analysis Substack posts right now

And their main takeaways
Category
Top Technology Topics

We really need better data

Something to Consider • 0 implied HN points • 06 Aug 24
🚌 Education Data Analysis
  1. We need better data to answer important questions about education and healthcare. Good data helps us understand what really works and what doesn't.
  2. There are big gaps in our knowledge, especially in poorer countries. Without accurate information, we can't properly assess living standards or make informed decisions.
  3. Collecting reliable data should be a priority. New technologies, like satellite data, hold promise for improving how we gather and analyze information.

Chain Of Natural Language Inference (CoNLI)

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 12 Jan 24
🕹 Technology Data Analysis
  1. There are three types of hallucinations in AI-generated text: context-free, ungrounded, and self-conflicting. Each type means there's a different way the text can be misleading.
  2. The CoNLI framework helps detect and reduce hallucinations in text responses. It can rewrite responses to improve their accuracy without needing special tuning.
  3. CoNLI works even when the user has limited control over the AI model, making it easier to ensure that the generated output aligns with correct information.

LLM-Generated Self-Explanations

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 21 Dec 23
🕹 Technology Data Analysis
  1. LLMs can make predictions and explain how they arrived at those predictions. This helps in understanding their reasoning better.
  2. Using a 'Chain of Thoughts' method can improve LLMs' ability to solve complex tasks, especially in areas like math and sentiment analysis.
  3. There's a need for better ways to evaluate the explanations given by LLMs because current methods may not accurately determine which explanations are effective.

LLM Drift

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 29 Sep 23
🕹 Technology Data Analysis
  1. LLM Drift refers to big changes in how language models respond over a short time. This means their answers can differ quite a bit unexpectedly.
  2. Studies show that the accuracy of models like GPT-3.5 and GPT-4 can go up and down significantly in just a few months. Sometimes they get worse at certain tasks.
  3. It's important to keep checking how these models behave over time because their performance can shift for many reasons, not just from minor tweaks.

What Constitutes A Large Language Model Application?

Cobus Greyling on LLMs, NLU, NLP, chatbots & voicebots • 0 implied HN points • 30 Mar 23
🕹 Technology Data Analysis
  1. Large Language Models (LLMs) are advanced AI tools that can understand and create human language. They help with tasks like writing, summarizing, and recognizing different pieces of information.
  2. There are different parts to building applications with LLMs. This includes using models, tools for development, and creating apps that end users can interact with.
  3. Prompt engineering is important for getting the best results from LLMs. It involves creating and managing prompts to guide the AI in generating useful responses.
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✅ Making sense of the UK market for corporate communications and public relations

Wadds Inc. newsletter • 0 implied HN points • 04 Mar 24
💼 Business Data Analysis
  1. A new project is starting to collect and share important job data in the UK public relations and corporate communications market. This will help people understand job trends and opportunities better.
  2. Many people in PR change jobs every year, and there are lots of freelancers, making it a mixed and active job market. The project aims to help track these changes.
  3. There will be a monthly newsletter featuring job openings in PR, and the project will gradually expand to include more data sources over time.

✅ Monday Briefing: Lockdown creative, PR reporting, Kickstart scheme, recruitment recovery, flawed ad metric, Clubhouse primer, Lockdown Unconference, and more…

Wadds Inc. newsletter • 0 implied HN points • 01 Feb 21
💼 Business Data Analysis
  1. Lockdown creative processes have evolved to include remote brainstorming and technology use, allowing teams to connect and collaborate effectively despite physical distance.
  2. The recruitment landscape is recovering, particularly in digital marketing and PR, but some areas still face challenges as the job market adjusts after lockdowns.
  3. Social media platforms like Clubhouse and Facebook are adapting to new practices, with insights on engagement and content formats that cater to different audiences and enhance user experience.

✅ Media Briefing #25: Notes by Nafisa, comms vaccination guide, my new book, linking out, words matter, coverage metrics, and more…

Wadds Inc. newsletter • 0 implied HN points • 21 Dec 20
💼 Business Data Analysis
  1. It's important to recognize and address privilege and diversity in public relations. Sharing personal stories can help highlight these issues.
  2. Choosing the right words in communication is crucial. The way we express ourselves can have a big impact, so it's good to be mindful of language.
  3. When sharing news or articles, linking back to original sources is essential. It not only gives credit but also adds credibility to the information shared.

Data Update 1 for 2021: A (Data) Look Back at a Most Forgettable Year (2020)!

Musings on Markets • 0 implied HN points • 09 Jan 21
💰 Finance Data Analysis
  1. Data is most valuable when it's unique and exclusive. If everyone has access to the same data, it loses its worth.
  2. It's important to look at the big picture with data to avoid tunnel vision. By understanding industry norms, investors can better judge individual stocks.
  3. Data can expose misinformation and challenge common beliefs. Relying on facts rather than opinions helps clarify the truth in financial discussions.

Data Update 2 for 2020: Retrospective on a Disruptive Decade

Musings on Markets • 0 implied HN points • 27 Jan 20
💰 Finance Data Analysis
  1. The past decade saw strong growth in stocks, with the S&P 500 nearly tripling in value and a notable rise in bond returns as well. It was a great time for investors, especially those who held onto their portfolios.
  2. Interest rates dropped significantly during this period, influenced by both global economic conditions and central bank actions. Many believe these low rates are here to stay as the economy's fundamentals support them.
  3. Tech companies, particularly the FAANG group, led the stock market's rise, drastically increasing their market capitalization. This shift shows how important tech has become compared to traditional industries like energy.

Data Update 1 for 2020: Setting the table

Musings on Markets • 0 implied HN points • 13 Jan 20
💰 Finance Data Analysis
  1. Accessing raw data for companies is easy now, but choosing the right data sources and how to analyze it is important. It's like picking the best ingredients for a recipe.
  2. Using different types of data, like macro and micro data, helps provide a clearer picture of a company's financial health. Each type of data tells a part of the company's story.
  3. Data can be biased and misused, so it's important to look beyond just numbers. Making decisions based on data should include critical thinking and understanding the context.

January 2019 Data Update 8: Dividends and Buybacks - Fact and Fiction

Musings on Markets • 0 implied HN points • 08 Feb 19
💰 Finance Data Analysis
  1. Companies are spending a lot more on stock buybacks compared to dividends. This trend has been growing since the 1980s, with more than 60% of cash returned to shareholders coming from buybacks in recent years.
  2. There's a debate about whether buybacks are good for the economy. Some say they help shareholders while others believe the money should be reinvested in businesses or used to increase wages for workers.
  3. Not all companies use buybacks in the same way. Larger, mature companies tend to buy back more stocks, but many smaller or high-growth companies are still focused on building their businesses instead.

Damodaran Online: There is an App for that!

Musings on Markets • 0 implied HN points • 05 Mar 18
💼 Business Data Analysis
  1. The app named 'Damodaran Online' gathers all materials from his website, blog, and YouTube into one place for easy access on Apple devices.
  2. He is currently on sabbatical, enjoying a break from regular teaching but continuing to share knowledge through various classes and external workshops.
  3. His research and writing projects include updating his book on valuing tough companies and exploring the difference between pricing and valuing assets.

January 2018 Data Update 7: Growth and Value

Musings on Markets • 0 implied HN points • 27 Jan 18
💰 Finance Data Analysis
  1. Profitability is measured using various profit margins, which help assess how well a company is doing. It’s important to choose the right measure based on what you're analyzing, like gross margin for efficiency or net margin for overall profitability.
  2. Excess returns show how much a company earns above its cost of capital, and most companies struggle to achieve this. Many firms aren't making enough money to cover their investments, highlighting a risk in company performance.
  3. Regional, sector, and size factors influence company profits. For instance, smaller companies often perform worse than larger ones, and certain industries, like technology, can produce high returns while others, like retail, may struggle.

January 2018 Data Update 2: The Buoyancy of US Equities

Musings on Markets • 0 implied HN points • 09 Jan 18
💰 Finance Data Analysis
  1. US stocks had a strong performance in 2017, achieving a 21.65% return, which surprised many experts. This shows that the equity market can thrive even with various economic and political concerns.
  2. Despite a good year for stocks, the fundamentals improved, with earnings and dividends rising. This suggests that the stock prices are supported by healthier financials.
  3. Looking ahead, there's potential for Treasury bond rates to rise, which could impact equity performance. Investors need to watch changes in tax laws and overall economic conditions as these factors may influence the market.

January 2017 Data Update 1: The Promise and Perils of "Big Data"!

Musings on Markets • 0 implied HN points • 09 Jan 17
💼 Business Data Analysis
  1. Numbers can seem super precise, but they often aren't. How we calculate them can really change the results, so we should always be careful with our interpretations.
  2. Data isn't always objective; it can carry biases just like stories do. It’s important to look at different ways a number can be presented to get a clearer picture.
  3. Just having data doesn't mean it will lead to profits. For data to be valuable, it needs to be exclusive or actionable, which isn't always the case.

The Tax Story in 2015: Myths, Misconceptions and Reality Checks

Musings on Markets • 0 implied HN points • 19 Jan 15
💰 Finance Data Analysis
  1. Many people think they pay their fair share of taxes while believing that others don't. It helps to look at real data to see how taxes are actually paid.
  2. Even though the U.S. has a high corporate tax rate, companies in the U.S. pay a significant portion of their income in taxes, similar to or higher than companies in other countries.
  3. There's talk of changing the corporate tax code in the U.S. to make it simpler and fairer. Suggestions include lowering the tax rate and only taxing foreign income at local rates.

Numbers Time! Data update for 2014

Musings on Markets • 0 implied HN points • 09 Jan 14
💰 Finance Data Analysis
  1. Data access has changed a lot over the years. In the past, it was hard to find data unless you were at a university or bank, but now it's way easier and more global.
  2. The reason for sharing this data is partly self-interest. It helps the creator make better investment decisions and save time throughout the year.
  3. When using this data, remember that it reflects personal judgments and can include errors. It's important to verify details and be cautious when making decisions based on the numbers.

Data Update 2013: The Dark Side of Numbers

Musings on Markets • 0 implied HN points • 13 Jan 13
💰 Finance Data Analysis
  1. Some people use complex numbers to scare others into agreeing with them. You can fight this by sticking to common sense and focusing on the main idea.
  2. Data can be twisted to support a certain viewpoint by only showing what fits. Always check for the full picture before believing claims.
  3. Many analysts hide behind data instead of making tough decisions. It's better to personalize and adapt data to your own understanding rather than rely on generic numbers.

Moneyball and Investing: Data, Information and my 2012 Update

Musings on Markets • 0 implied HN points • 26 Jan 12
💰 Finance Data Analysis
  1. Investing should focus more on data and numbers rather than just gut feelings or stories from analysts. Just like in baseball, using hard data can lead to better investment choices.
  2. Data is useful, but it’s important to understand that all numbers are estimates. This means they can have errors and should be used carefully.
  3. To make good investment decisions, combine data analysis with sensible stories. Numbers are a starting point, but having a narrative helps make better choices.

Equity Risk Premiums and the Fear of Catastrophe

Musings on Markets • 0 implied HN points • 09 Mar 10
💰 Finance Data Analysis
  1. The equity risk premium is what investors expect to earn above a safe rate like treasury bonds for taking on the risk of stocks. It helps explain stock market behavior over time.
  2. Using historical data for equity risk premiums can be misleading because it looks back rather than forward. A better method is to calculate an implied premium based on current stock prices and expected future cash flows.
  3. Fear of economic disasters strongly affects equity risk premiums. During crises, fear increases and affects investors' expectations, leading to quick shifts in the premium values.

Data Update for 2010

Musings on Markets • 0 implied HN points • 08 Jan 10
💰 Finance Data Analysis
  1. The author updates datasets for companies from different regions each year, focusing on risk, profitability, and debt measures.
  2. This year's updates include new data for Indian and Chinese companies, expanding the coverage of the datasets.
  3. Future blog posts will discuss what these updates reveal about global companies and markets.

Skip the joins with Semantic ABI

HyperArc • 0 implied HN points • 26 Jun 24
🕹 Technology Data Analysis
  1. Semantic ABI helps organize data from Ethereum transactions better. Instead of dealing with lots of confusing tables, it allows you to get a clear view of the data directly.
  2. By using Semantic ABI, you can easily combine data from different sources without complex joins. This saves time and makes analysis simpler.
  3. The library supports features like adding extra meaning to data and finding matches in transactions more efficiently. It's designed to help with analyzing Web3 data easily.

Coming soon: Unmoderated Insights

Unmoderated Insights • 0 implied HN points • 26 May 23
🕹 Technology Data Analysis
  1. The blog focuses on breaking down complex topics into simple explanations. It's meant for people who like understanding things without the confusion.
  2. It emphasizes the importance of data over beliefs, especially regarding social technologies and their impact on our lives.
  3. The author invites readers to subscribe and share the blog with others who might enjoy it or benefit from it.

Product Hunt aftermath 🔥

André Casal's Substack • 0 implied HN points • 28 Aug 24
💼 Business Data Analysis
  1. Engaging with the right audience is key. It's important to connect with active Product Hunt users before launching to increase votes.
  2. Collecting emails can help build interest. Adding a newsletter signup on the landing page could capture potential buyers' information.
  3. Learning from each experience is vital. Reflecting on what can be improved helps for better results in future launches.

[in case you missed it] Data Science Weekly - Issue 464

Data Science Weekly Newsletter • 0 implied HN points • 16 Oct 22
🕹 Technology Data Analysis
  1. Building a community of R users can greatly enhance collaboration and knowledge sharing, especially in specialized fields like pharmaceuticals.
  2. Generating research ideas often starts with identifying gaps in existing literature, which can be guided by specific frameworks to improve the quality of ideas.
  3. Data cleaning is crucial for model accuracy, and its success relies on effective ETL processes and organizational commitment to maintaining high-quality data.

[in case you missed it] Data Science Weekly - Issue 463

Data Science Weekly Newsletter • 0 implied HN points • 09 Oct 22
🕹 Technology Data Analysis
  1. To explore a large CSV file, you should use handy tools and methods to quickly understand the data without getting overwhelmed.
  2. AI can help convert messy unstructured text into organized data, speeding up tasks that would usually take a long time manually.
  3. Building a career in data science involves learning not just the technical skills but also how to navigate job opportunities and project management.

[in case you missed it] Data Science Weekly - Issue 398

Data Science Weekly Newsletter • 0 implied HN points • 11 Jul 21
🕹 Technology Data Analysis
  1. Data science projects can analyze unique datasets, like personal music streaming from Apple Music, helping us understand our listening habits better.
  2. Language affects how cultures understand color, with some languages having fewer words for colors, which is interesting for studying cultural differences.
  3. Using advanced techniques like causal inference can help businesses make better pricing decisions, improving their competitiveness in the market.

[in case you missed it] Data Science Weekly - Issue 390

Data Science Weekly Newsletter • 0 implied HN points • 16 May 21
🕹 Technology Data Analysis
  1. AI can solve complex puzzles better than humans, but humans still have unique skills. Don't give up on challenging word games just yet!
  2. Defining trees in biology is tricky because many plants don't fit into clear categories. It's surprising how many things that look like trees actually aren't.
  3. New technology makes searching through large image databases easier. With smart algorithms, you can quickly find the pictures you're looking for without remembering file names.

[in case you missed it] Data Science Weekly - Issue 366

Data Science Weekly Newsletter • 0 implied HN points • 29 Nov 20
🕹 Technology Data Analysis
  1. Pinterest improved its data infrastructure by moving from Lambda to Kappa architecture to better handle its visual signals for machine learning. This change aimed to streamline costs and enhance signal availability.
  2. When building machine learning models, companies like DoorDash face huge data challenges. Choosing the right feature store is crucial for managing this data effectively, ensuring performance without overspending.
  3. Differentially private learning still faces challenges in performance compared to traditional models. For effective results, more private data or improved features from public data may be necessary.

S2 E1: Solving Self-Serve Analytics

CAUSL Effect • 0 implied HN points • 02 Oct 23
🕹 Technology Data Analysis
  1. Self-serve analytics lets non-analysts access and analyze data without always needing help from an analytics team. This can help speed up decision-making and reduce bottlenecks.
  2. The goal is to create tools and provide education for everyday users so they can do their own analytics easily. Training and tutorials will be essential to help users become comfortable with these tools.
  3. The focus is on keeping users engaged and motivated to use self-serve analytics. Understanding what stops people from doing analytics themselves is key to improving the program.

Writing Useful Performance Reviews: Evaluating the data and writing the review

It Depends / Nimble Autonomy • 0 implied HN points • 19 May 24
💼 Business Data Analysis
  1. Collect all relevant data before writing a performance review. This includes past reviews, feedback, and notes so you have a complete view of the person's performance.
  2. Be clear and honest when writing the review. Avoid vague language or trying to balance out negatives with positives; it’s important for the person to understand their true performance.
  3. After writing the reviews, check for patterns or biases. Make sure each review makes sense and supports your conclusions about each person's performance.

Measurement noise: when 1000*1000 isn't a million

filterwizard • 0 implied HN points • 03 Oct 24
🕹 Technology Data Analysis
  1. Measurement noise can make it seem like you need very high accuracy to get correct results, but you might actually need less than you think.
  2. For measuring small signals accurately, the required dynamic range isn't as extreme as multiplying the signal by itself; practical calculations can simplify this.
  3. For specific accuracy requirements in noisy environments, using embedded microcontroller ADCs can be a good solution to achieve realistic signal-to-noise ratios.

How linear-phase filters can still cause phase distortion

filterwizard • 0 implied HN points • 14 Sep 24
🕹 Technology Data Analysis
  1. Even though linear-phase filters are supposed to keep the phase of signals the same, they can still cause unexpected phase changes. This can happen especially at stopband frequencies where the phase might jump abruptly.
  2. Using simple filters, like box-car filters, can lead to problems because they may not completely block unwanted frequencies. This can result in the output signal being inverted or misinterpreted, especially when analyzing important data trends.
  3. It's important to choose the right filter. Either use filters that effectively block unwanted frequencies or ones that don’t cause abrupt phase changes, to avoid messing up the signals you are trying to interpret.

Prediction and negative-delay filters: five things you should know

filterwizard • 0 implied HN points • 19 Aug 24
🕹 Technology Data Analysis
  1. Filters can delay signals as they take time to process inputs and produce outputs. It's important to understand this delay, especially when working with different types of signals.
  2. While you can't completely eliminate delay in filters, you can create compensating filters to achieve zero or even negative group delay at certain frequencies. This can improve the accuracy of your system responses.
  3. Negative-delay filters can actually predict future values of a signal based on its current ramping behavior. This can be really useful in control systems and financial data analysis.

Level Up Your RevOps Career

beyondrevenueoperations • 0 implied HN points • 19 Oct 24
💼 Business Data Analysis
  1. RevOps is key to business success, bringing sales, marketing, and customer success teams together to grow revenue. Choosing the right career path in RevOps can greatly influence your impact.
  2. There are two main paths in RevOps: the technical path, which focuses on data analysis and tools, and the strategic path, which emphasizes revenue strategy and leadership. Each path offers unique opportunities and challenges.
  3. Combining technical and strategic skills can create a powerful professional. This 'T-shaped' skillset helps you make better decisions and improve business outcomes.

HN blogs - 16/10/24

HackerNews blogs newsletter • 0 implied HN points • 16 Oct 24
🕹 Technology Data Analysis
  1. Using Strace can help you track specific system calls instead of every single one, making it easier to debug problems.
  2. Technical leaders should be aware of common decision-making mistakes that can affect their teams and projects.
  3. Understanding the right way to use string parameters in coding can improve your programming practices and avoid confusion.

Mastering DataFrame Joins in Spark: A Comprehensive Guide with Examples

DataSketch’s Substack • 0 implied HN points • 23 Jul 24
🕹 Technology Data Analysis
  1. DataFrames in Spark are like tables for big data. They help people work with large datasets efficiently across different computers.
  2. There are several types of joins in Spark, such as inner, left, right, and full outer joins. Each type has a specific way of combining data from two DataFrames.
  3. Setting up Spark is easy. You can install it, write a few lines of code to create DataFrames, and start joining data for analysis.

Choosing the Right SQL Technique to Transform Your Data Analysis

DataSketch’s Substack • 0 implied HN points • 24 Jun 24
🕹 Technology Data Analysis
  1. CTEs help make complex queries easier to read and are good for breaking down hierarchical data. But be careful not to use them too much, as they can slow things down.
  2. Subqueries are useful for filtering and aggregating data, but they can be hard to read and slow if used in a complicated way. They work best for specific tasks in a query.
  3. Temporary views are great for creating reusable logic that only lasts for the session. However, they can't be used outside of that session, so plan accordingly.