The hottest Data Analytics Substack posts right now

And their main takeaways
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Top Business Topics

Data Science Weekly - Issue 411

Data Science Weekly Newsletter • 19 implied HN points • 07 Oct 21
🕹 Technology Data science Machine Learning Artificial Intelligence Data Analytics Programming
  1. Freelancing in data visualization can be difficult, and learning from others' mistakes can help avoid similar pitfalls.
  2. Using AI to restore lost art, like Klimt's paintings, shows how technology can creatively bring the past back to life.
  3. Resource constraints in smaller organizations can complicate how machine learning is developed, highlighting the need for better support and understanding in the field.

Data Science Weekly - Issue 362

Data Science Weekly Newsletter • 19 implied HN points • 29 Oct 20
🕹 Technology Data science Machine Learning Artificial Intelligence Data Engineering Data Analytics
  1. 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.
  2. 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.
  3. 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.
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ThoughtSpot - Making Data Analytics as Simple as a Google Search

Termsheet by Attack Capital • 2 HN points • 13 Jun 23
🕹 Technology Data Analytics AI Cloud Computing Startups Artificial Intelligence
  1. ThoughtSpot aims to simplify data analytics like a Google search, providing AI-driven analytics for instant insights.
  2. Co-founded by Ajeet Singh, ThoughtSpot is valued at $4.2Bn and has raised $644 Million, backed by major VC firms.
  3. ThoughtSpot's platform allows users to easily query and analyze data, with features like live-querying, governed data models, and integrations.

Data Science Weekly - Issue 246

Data Science Weekly Newsletter • 19 implied HN points • 09 Aug 18
🕹 Technology Data science Machine Learning Artificial Intelligence Data Analytics Programming
  1. Balancing quick changes and long-term planning is tough in data science, and it's important to find ways to adapt without losing sight of the bigger picture.
  2. Coca-Cola successfully used advanced technology like TensorFlow for its marketing efforts, showcasing how big companies can leverage data science for effective campaigns.
  3. Automated machine learning tools, like AutoKeras, help people without deep technical skills to use powerful machine learning models easily.

Data Science Weekly - Issue 161

Data Science Weekly Newsletter • 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 106

Data Science Weekly Newsletter • 19 implied HN points • 03 Dec 15
🕹 Technology Data science Artificial Intelligence Machine Learning Data Analytics Big Data
  1. A new gadget can listen to sounds and vibrations to diagnose problems with air conditioners. This technology helps to identify mechanical issues without needing to open the machine.
  2. Wikipedia is using AI to improve how it reviews changes made by editors. This system will help detect problematic revisions automatically, making the editorial process smoother.
  3. There are common mistakes people make when writing data science resumes. It's important to avoid these pitfalls to increase your chances of landing job interviews.

Data Science Weekly - Issue 52

Data Science Weekly Newsletter • 19 implied HN points • 20 Nov 14
🕹 Technology Data science Machine Learning Artificial Intelligence Software Development Data Analytics
  1. Personalized recommendations are really important in online shopping because they help customers discover products they might like and give sellers more exposure.
  2. Combining different techniques in data science can create powerful tools, like using machine learning and crowd input together to improve classification models.
  3. AI should be seen as a helpful tool rather than a danger; we should focus on how to use it positively instead of worrying about potential threats.

Data Science Weekly - Issue 38

Data Science Weekly Newsletter • 19 implied HN points • 14 Aug 14
🕹 Technology Data science Machine Learning Big Data Data Analytics Statistical Modeling
  1. Deep learning can be fun to explore, and there's a quick guide to help you get started with it.
  2. Data science skills are in high demand, so asking the right questions before a job offer is really important.
  3. There are great resources and tools out there for data visualization and machine learning to help you improve your skills.

Data Science Weekly - Issue 3

Data Science Weekly Newsletter • 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.

Announcing the Data Plumbers Newsletter: Unlocking Insights in Data Analytics and AI

Data Plumbers • 0 implied HN points • 30 Mar 24
🕹 Technology Data Analytics AI Newsletters Trends
  1. Staying informed in data analytics and AI is crucial for all professionals, from beginners to experts.
  2. The Data Plumbers Newsletter offers cutting-edge insights, trend spotting, and tool reviews curated by industry experts.
  3. Subscribing to the Data Plumbers Newsletter can provide valuable information to empower data enthusiasts and professionals.

In-game Analytics Pipeline with Redpanda, ClickHouse, and Streamlit

Tributary Data • 0 implied HN points • 13 Mar 24
🕹 Technology Data Analytics Database Management Web Development
  1. In-game analytics provide insights into player behavior, helping developers make informed decisions to enhance gameplay experience and increase player retention.
  2. Redpanda, ClickHouse, and Streamlit form a robust analytics pipeline where Redpanda collects gameplay events, ClickHouse processes and organizes the data for analysis, and Streamlit enables visualization through a real-time leaderboard.
  3. By leveraging technologies like Apache Flink for preprocessing raw gameplay events, developers can further enhance insights into player behaviors and interactions to improve the gaming experience and retain players.

Generative AI Opportunities for Businesses 

Tributary Data • 0 implied HN points • 05 Mar 24
🕹 Technology AI Data Analytics Software Engineering Marketing Sales
  1. Generative AI can help businesses drive innovation, efficiency, and success by leveraging cutting-edge data analytics and AI technologies.
  2. Large Language Models like Agatha can provide conversational interfaces, streamlining access to company knowledge and insights, leading to enhanced productivity and decision-making for employees.
  3. Agatha enables automation of tasks, such as generating personalized emails, summarizing transcripts, and generating code snippets, helping save time, improve efficiency, and foster creativity across various departments.

Who is Sridhar Ramaswamy and what does this mean for Snowflake? [Substack edition]

The Orchestra Data Leadership Newsletter • 0 implied HN points • 15 Apr 24
💼 Business Technology Finance Data Analytics AI Cloud Computing
  1. Sridhar Ramaswamy takes over as Snowflake's CEO, bringing a fresh perspective after Frank Slootman's departure.
  2. Snowflake is consolidating the 'Data Plane' within their platform, offering features like anomaly detection and data quality testing.
  3. Snowflake aims to democratize AI, providing easy access to AI services using data within the Snowflake platform.

The Unstructured Data Funnel

The Orchestra Data Leadership Newsletter • 0 implied HN points • 15 Dec 23
🕹 Technology Data Engineering Cloud Computing Machine Learning Data Analytics Data processing
  1. Unstructured data, like text documents and deeply nested JSON, is a crucial component in data processing for large cloud vendors like Snowflake and Databricks. The location where unstructured data is processed within the data pipeline greatly impacts the compute costs and revenue for these companies.
  2. Processing unstructured data involves a series of stages, from data movement to storage in object storage, then to structured data warehouses. Each stage of this 'funnel' affects computational requirements and costs, with the most logical point for processing unstructured data being at the object storage level.
  3. The final step in the data funnel, data activation, involves the least computational demands as it deals with cleaned and aggregated data ready for analytical applications. Thinking strategically about the processing location of unstructured data can help optimize costs and efficiency in data workflows.

Data Engineering: A Software Engineer’s Approach

realkinetic • 0 implied HN points • 25 Jan 24
🕹 Technology Software Engineering Data Engineering Cloud Computing Data Analytics Data Infrastructure
  1. The tech industry varies in its expectations of data engineers, leading to challenges in team performance and hiring.
  2. Companies today need to be data-driven, utilizing modern data stack tools, which necessitates a blend of data engineering and software engineering skills.
  3. Data engineering benefits from adopting software engineering principles like treating systems as products, clear communication, and implementing CI/CD pipelines.

Implementing ETL on GCP

realkinetic • 0 implied HN points • 15 Jul 20
🕹 Technology Data Analytics Cloud Computing Data Warehousing
  1. ETL processes are vital for data analytics, involving extracting, transforming, and loading data for storage in a warehouse.
  2. GCP offers options like Data Fusion and Cloud Dataprep for implementing ETL pipelines, catering to varying technical skill levels and preferences.
  3. Alternative approaches on GCP for ETL include using services like Cloud Dataflow for more code-heavy processes or leveraging BigQuery for ELT if your team is SQL-focused.

Google Search Mania

Sector 6 | The Newsletter of AIM • 0 implied HN points • 02 Nov 23
🕹 Technology Search Engine AI Advertising Earnings Data Analytics
  1. Google is always trying to improve its search engine with new technology like generative AI, which helps make searches smarter and more useful.
  2. The company recently had a good quarter, making 11% more money than last year, mostly from ads related to its search services.
  3. With over $59 billion from advertising, it's clear that Google's search engine is still a major driver of its profits.

Business (Un)Intelligence

Sector 6 | The Newsletter of AIM • 0 implied HN points • 26 Feb 23
💼 Business Business Intelligence Technology Trends Market Analysis Data Analytics Software Tools
  1. Many business intelligence tools haven’t evolved much and are falling behind modern trends and technologies.
  2. This lack of improvement is resulting in a decline in useful insights from these tools, leading to what's called 'business (un)intelligence.'
  3. Microsoft is performing well in this space, possibly attracting users away from competitors like Tableau due to its established ecosystem and offerings.

✅ Monday briefing: Waddscon on AI in PR, issues radar, Google Analytics switch, GPT3 Google docs, TikTok for text, IP overreach, British Gas fury, and more...

Wadds Inc. newsletter • 0 implied HN points • 06 Feb 23
🕹 Technology AI Tools Media Trends Data Analytics
  1. Waddscon is an event focused on how AI is changing public relations and marketing. It will feature experts discussing tools and the future of work.
  2. Google Analytics will switch to GA4 soon, and users need to set it up to avoid automatic changes. It's important to take control of your data tracking.
  3. A new app called Artifact is like TikTok but for text, helping users discover and read articles based on their interests.

✅ Monday briefing: ESG opportunity, PR weak, bounce rate bounced, presenting data, hiding likes, considerate Twitter and more...

Wadds Inc. newsletter • 0 implied HN points • 10 May 21
💼 Business Public Relations Advertising Media industry Data Analytics Social media
  1. ESG stands for Environmental, Social, and Governance, and it's becoming important for public relations to focus on this. It helps businesses look at their impact on the planet and society.
  2. Bounce rate is replaced with engagement rate in Google Analytics. This new way of measuring shows how much users interact with your content, which is more helpful than just seeing if they leave a page.
  3. Instagram now lets users choose if they want to hide likes on their posts. This change is meant to help people feel less pressure about how many likes their content gets.

Finding Trends With Approximate Embedding Clustering

aspiring.dev • 0 implied HN points • 29 Apr 23
🕹 Technology Data Analytics AI Machine Learning Algorithms Big Data
  1. Clustering similar data helps to identify trends and categories quickly. This is important for analyzing things like shopping habits or AI tasks.
  2. K-Means++ is a method that improves the speed and accuracy of finding cluster centers, which helps in managing data without needing too much preparation.
  3. Using approximate clustering techniques allows for faster processing of data and keeps up with changing trends, making it useful for things like tracking popular text-to-speech messages.

[in case you missed It] Data Science Weekly - Issue 459

Data Science Weekly Newsletter • 0 implied HN points • 11 Sep 22
🕹 Technology Data science Machine Learning Artificial Intelligence Software Development Data Analytics
  1. Organizations should work on improving their data quality because it directly impacts their success and competitive edge. Creating better data can lead to better decisions and outcomes.
  2. The modern data stack's activation layer is crucial for turning data into actionable insights. This allows companies to go beyond just looking at data and actually use it to improve their products and services.
  3. Using the right tools, like ONNX for model deployment, can help make machine learning models more portable and less tied to specific programming environments. This makes it easier to run models across different programming languages.

Big Data and the Entertainment Industry

Joshua Gans' Newsletter • 0 implied HN points • 04 Sep 16
💼 Business Entertainment Data Analytics Digital economy Book review Empirical Studies
  1. The book 'Streaming Sharing Stealing' by Mike Smith and Rahul Telang offers valuable lessons in the digital economy, particularly in the entertainment industry, emphasizing the importance of understanding and utilizing data properly.
  2. Entertainment executives often made critical errors due to not trusting data analytics for decision-making, relying instead on outdated assumptions and untested suppositions.
  3. Studies, like Sandra Barbosu's research, show that big data can provide valuable insights to industries like movie studios, helping them predict box office success and avoid producing movies that underperform.

[in case you missed it] Data Science Weekly - Issue 378

Data Science Weekly Newsletter • 0 implied HN points • 21 Feb 21
🕹 Technology Data science Machine Learning Artificial Intelligence Data Analytics Software Development
  1. Creating robots that can think morally is similar to parenting. Teaching them right from wrong can be approached in the same way we teach children.
  2. Transformers are important in both language and image processing. Understanding how to use them can help with many tasks in data science.
  3. Building systems for data quality and observability is essential. By using tools like SQL, we can keep track of how our data changes and ensure it stays reliable.

[in case you missed it] Data Science Weekly - Issue 377

Data Science Weekly Newsletter • 0 implied HN points • 14 Feb 21
🕹 Technology Data science Machine Learning Artificial Intelligence Software Development Data Analytics
  1. Using Active Learning can save time and effort in machine learning. It allows models to learn with less labeled data by letting them ask questions about unclear data.
  2. There is a growing shift from Excel to Python in many industries. This change is driven by the need for more advanced data analysis and the capabilities Python offers.
  3. Understanding the importance of machine learning in healthcare is crucial. Innovations like AI systems that can identify smells may lead to new diagnostic tools and enhance medical practices.

Data Deep Dive: Web Mining Shows Inflections in Apparel Sector Results

The Data Score • 0 implied HN points • 27 Jul 23
💼 Business E-commerce Data Analytics Retail Marketing Risk management
  1. Monitoring price trends is crucial for understanding and analyzing competitive value and market strategies of businesses.
  2. Web-mined pricing data can provide early insights into companies' pricing strategies and their ability to execute them.
  3. Analyzing web-mined pricing data requires proper cleansing, enrichment, and interpretation, considering limitations such as data gaps and differences between online and offline prices.

[in case you missed it] Data Science Weekly - Issue 294

Data Science Weekly Newsletter • 0 implied HN points • 13 Jul 19
🕹 Technology Artificial Intelligence Machine Learning Data science Data Analytics Software Development
  1. A new poker bot has learned strategies to beat skilled players, showing the advancements in AI technology. This could change how games are played and studied.
  2. Generative adversarial networks, which are known for creating deepfakes, may have positive uses in medical fields like cancer diagnosis. Before, they were mainly seen in a negative light.
  3. San Francisco is trying to use AI to reduce bias in the prosecution process, aiming to make the legal system fairer. This could help in addressing racial discrimination in the courtroom.

Building a data-aware culture for impact investing | Invest-NL

nonamevc • 0 implied HN points • 20 Feb 24
💼 Business Investing Data Strategy Data Analytics
  1. Impact investing is crucial in addressing global challenges like poverty and climate change by transforming resource allocation for a sustainable future.
  2. One key challenge in impact investing is the lack of standardized measurements and balancing financial returns with social impact.
  3. Developing a data strategy, aligning investment philosophy with mission, and employing quantitative models are vital for successful impact investing.

Code vs. Cells

Reflective Software Engineering • 0 implied HN points • 08 Jun 23
🕹 Technology Coding Python Data Analytics Automation Problem Solving
  1. Modeling everyday problems using test-driven development with a Python tool instead of spreadsheets can lead to better results and easier adaptability.
  2. Creating simple Python tools with scripting languages can automate mundane tasks, improve problem-solving skills, and potentially lead to open-source contributions.
  3. Writing code can be enjoyable and effective in automating repetitive tasks, enhancing problem-solving abilities, and potentially growing into valuable tools for others.

10x Controlling Project Skies

10xManager • 0 implied HN points • 06 Feb 24
🕹 Technology Engineering Leadership Project management Data Analytics Software Development
  1. Visibility is crucial for effective engineering leadership, just like air traffic controllers oversee busy airspace.
  2. Gaining visibility into software development processes helps in anticipating challenges and optimizing team performance.
  3. Engineering leaders can benefit from tools that offer comprehensive visibility and insights for managing projects successfully.

Why you need to ditch dbt for SQLMesh today

Three Data Point Thursday • 0 implied HN points • 15 Jun 23
🕹 Technology Data Engineering AI Data Analytics Product Development Community Engagement
  1. Building products with LLMs is challenging and requires addressing multiple issues.
  2. PandasAI offers AI-powered features for data analysis, focusing on integrating LLMs smartly into products.
  3. Consider switching to SQLMesh from dbt, especially if you are a data engineer or data scientist needing a more developer-focused analytics tool.

Measure What Matters! Our Simple Data Stack To Understand Open Source Adoption

Sonal’s Newsletter • 0 implied HN points • 18 Apr 23
🕹 Technology Data Analytics User Interaction
  1. Learn how people are discovering your product, such as through direct interactions, website traffic, and testimonials.
  2. Understand how users are using your product, like the platform they run it on, scalability, and frequency of use.
  3. Utilize a simple data stack to track open source adoption and product usage, collecting data manually to understand growth and user behavior.