data analysis
data analysis on Beyond Market Intelligence: a running collection of 60 stories we have gathered and hand-picked because they are worth your time. Every post here touches on data analysis in some way — the news, the analysis, the deep dives, and the occasional surprise find. Acme AI is the next-generation, AI-powered spreadsheet platform built to replace Excel and redefine how analysts, data scientists, and enterprise teams work with data. New stories are added to this page as we find them, so check back if you want to keep up with what is happening around data analysis, or subscribe to the RSS feed to get them as soon as they are published. Browse the collection below, or head back to the homepage to see everything Beyond Market Intelligence is covering right now.
Redefining GIS: Declarative Symbology and Collaborative Workflows in JupyterGIS
JupyterGIS 0.16 marks a significant step forward in geospatial analysis, redefining GIS workflows within the familiar Jupyter notebook environment. This release prioritizes collaborative productivity with enhanced real-time editing and robust support for large-scale datasets—including those from remote sensing. Declarative symbology streamlines visualization, while expanded R compatibility broadens accessibility. Addressing community feedback, the update also focuses on improved portability. For those seeking further performance enhancements in data processing, explore our article on how FireDucks can accelerate pandas workloads.
I Asked ChatGPT to Analyze 3 Datasets. It Made the Same Mistakes Every Time
ChatGPT's ability to analyze data is rapidly evolving, but our recent experiment revealed consistent limitations. We tasked ChatGPT with examining three distinct datasets and observed recurring errors, including an initial row count discrepancy and the endorsement of two inaccurate conclusions. This review pass successfully corrected the row count and validated the findings. Understanding these nuances is critical; as explored in "What We Miss About Missing Values," the data we observe often contains hidden assumptions that can skew analysis.
This Python Library Can Run Pandas Workloads Up to 20x Faster
Facing slowdowns with Pandas? FireDucks offers a transformative solution, accelerating your DataFrame performance by up to 20x. Leveraging lazy execution, compiler optimization, and multithreaded processing, FireDucks empowers data professionals to work faster and more efficiently. Our benchmarks demonstrate significant gains, allowing you to tackle larger datasets and complex analyses with ease. Explore the possibilities – and for further insights into optimizing AI workflows, see our article, "7 Common Python Mistakes to Avoid in AI Workflows."
Quantifying User Behavior Patterns to Build Better Predictive Features
Simply knowing a user’s clicks—like a 35-year-old male in Seattle clicking 12 times last month—reveals little about their intent. Quantifying user behavior patterns, however, unlocks powerful predictive capabilities. We move beyond superficial metrics to analyze sequences, durations, and interactions, building features that genuinely anticipate user needs. This approach transforms raw data into actionable insights, driving more effective product development and personalized experiences. For a deeper dive into understanding data assumptions, explore “What We Miss About Missing Values.”
What We Miss About Missing Values
Missing values are a ubiquitous challenge in data science, yet their implications often go unexamined. "What We Miss About Missing Values" explores the hidden assumptions embedded within the data we *do* observe—recognizing that what's absent can be just as informative as what's present. This post delves into the biases introduced by missingness and offers a framework for more thoughtful analysis. For a related perspective on navigating complexity in data systems, see "Why RAG Complexity Should Be Earned."
7 Common Python Mistakes to Avoid in AI Workflows
A clean execution in AI workflows shouldn’t be mistaken for success. While a successful run confirms the process completed, it reveals nothing about data integrity, model learning, or the reliability of saved results. To ensure robust and trustworthy AI pipelines, avoid these 7 common Python mistakes. Understanding these pitfalls is critical for data scientists, as highlighted in our recent piece, "5 AI Skills That Will Keep Data Scientists Relevant in 2027." Explore these insights and build confidence in your AI journey.
Clipto uses AI to search terabytes of video and is now valued at $250M
Clipto, a three-year-old startup, has rapidly ascended to a $250 million valuation by leveraging AI to efficiently search terabytes of video. Achieving $15 million in ARR and profitability prior to its latest $15 million funding round demonstrates a clear path to sustainable growth. This innovative approach addresses a significant need in a rapidly expanding market. For further insights into leadership transitions and product-focused strategies, explore our article, "Tim Cook’s parting message: Apple is in the hands of a product builder."
4 Claude Skills Every Data Scientist Needs in 2026
Data scientists, prepare for the shift. By 2026, mastering Claude's capabilities will be essential for staying ahead. Our latest analysis identifies four key Claude skills – prompt engineering, structured output design, chain-of-thought reasoning, and agent orchestration – that will significantly enhance your workflow. Don't wait to integrate these into your toolkit; the future of data analysis demands it. Explore these vital skills today and empower your data journey. For deeper insights into the evolving AI landscape, see "Nvidia’s AI advantage is moving beyond the GPU."
NeurIPS 2026 Acceptance Calculator [P]
Navigating NeurIPS submissions can feel daunting. To help demystify the process, we’ve developed a NeurIPS 2026 Acceptance Calculator [P], a small model estimating acceptance probability based on scores and a projected acceptance rate. Explore it here: https://levilingsch.github.io/neurips-acceptance-estimator/. This tool offers a practical way to assess your submission's potential. For researchers looking to bolster their writing skills alongside their technical contributions, our "Best ML papers to pick up writing skills [D]" article provides valuable guidance.
Radar makes podcasts searchable — and usable by AI agents
Unlock the power of podcast conversations with Radar, Particle’s new podcast intelligence platform. We’ve transcribed and analyzed over 130,000 podcasts, creating a searchable web index and opening up this vast audio resource to AI agents via API and MCP. Radar transforms podcast content from passive listening into actionable data, empowering users to discover insights and integrate spoken knowledge into their workflows.
The Types of Dimensions in a Star Schema, and How to Use Them
Dimensional modeling hinges on understanding dimensions—one of its two core object types. But dimensions aren't monolithic; they encompass several distinct varieties, each serving a specific purpose in structuring data for analysis. This post explores these types, detailing how to effectively leverage them within a star schema to unlock deeper insights. We’ll clarify their roles in providing context and enabling powerful data exploration. For a related perspective on optimizing data retrieval, see "Retrieve One Row from a Table, Not the Whole Table: Row-Level Chunks for RAG."
How to Build a Career in AI: 3 Distinct Pathways
Embarking on an AI career can feel overwhelming, but the path isn't monolithic. We’ve outlined three distinct pathways – each requiring a unique skillset and offering varied opportunities. Discover how to align your existing experience with roles in AI development, research, or application. This guide clarifies the necessary skills for each orientation, providing a clear roadmap to navigate this rapidly evolving field. For deeper insights into the tools shaping AI’s future, explore our article on "Top 10 Open-Source Benchmarks for AI Coding Agents in 2026."
Finding most recent dates in carious columns of date information
Analyzing game data can quickly become complex, even for seasoned Magic: The Gathering players. If you're seeking to identify your most recently played decks from a spreadsheet with multiple date columns, you’re facing a common challenge. Our AI-native spreadsheet technology empowers you to transform this task from daunting to discoverable. To achieve this, explore utilizing advanced sorting and filtering capabilities, enabling you to rank dates across columns and pinpoint your top ten most recent plays.
Microsoft to retire the COPILOT function
Microsoft is phasing out the COPILOT function in Excel, effective September 14th—a notable departure from their usual commitment to backwards compatibility. While the broader Copilot feature remains active, this specific function will no longer be available. Users who relied on COPILOT for calculations should explore alternative formulas. This shift highlights the evolving landscape of AI-native spreadsheet technology. Curious to know: Have you utilized the COPILOT function, and if so, for what purpose?
Floating plot on evergrowing spreadsheet possible?
Many spreadsheet users face the frustration of plots becoming detached from the data they summarize as tables grow. /u/orbitolinid highlights this challenge, specifically noting the need for a "floating" plot within Microsoft Excel Professional Plus 2024 on a 14" laptop screen, where daily data additions necessitate constant manual adjustments. This common workflow limitation underscores the need for more adaptive data visualization tools.
We got tired of trying 10 ML models every time we had a new dataset [P]
Tired of the iterative grind of testing multiple machine learning models for each new dataset? We were too. That’s why we built Arcliq (https://arcliq.app), a platform designed to streamline your ML workflow. Simply upload your tabular data, and Arcliq automatically handles preprocessing, trains and compares various models, and delivers the best-performing solution. Our goal is to empower users – regardless of expertise – to rapidly move from data to working model.
AI isn’t close to curing cancer. This startup says it knows what it will take.
The pursuit of AI-driven medical breakthroughs often overstates near-term possibilities. While a cure for cancer remains distant, a new startup is focusing on a fundamental truth: it’s the data, stupid. Their approach prioritizes meticulous data curation and intelligent modeling—a pragmatic strategy for unlocking insights hidden within complex biological datasets. This emphasis on foundational data practices represents a crucial shift, mirroring the innovative techniques explored in our recent piece, "Trained an diffusion model that runs on 264KB of RAM."
Netflix Open-Sources Agentic Workflow for Causal Inference
Netflix has open-sourced an innovative agentic workflow designed to streamline Observational Causal Inference (OCI). This new system demonstrably reduces the toil associated with causal analysis, empowering data scientists to focus on insights. The agent, given observational data and a user's analysis plan, leverages an actor-critic loop to estimate causality, generate comprehensive reports, and proactively suggest next steps. For deeper insights into agent capabilities, explore our article, "How to Add Skills in Agents using LangChain."
5 Python Libraries That Make Data Cleaning More Enjoyable
Data cleaning doesn’t have to be a chore. This article introduces five Python libraries designed to transform tedious data preparation into an expressive and genuinely enjoyable process. We've compiled a list of tools that empower you to streamline workflows and unlock deeper insights from your data. Discover how these libraries can simplify complex tasks and accelerate your analysis. For those working with image classification, you might find our accompanying dataset, "Starfield Fauna," a valuable resource for practical application.
Running SQL Concurrently Across Three Remote DuckDB Servers with Quack
Explore a novel approach to data processing with "Running SQL Concurrently Across Three Remote DuckDB Servers with Quack." This experiment demonstrates a practical application of remote SQL execution, empowering users to leverage distributed resources for enhanced performance. Discover how Quack facilitates this process, offering a streamlined solution for complex queries. For those interested in building applications that accumulate understanding, consider "Designing a Persistent Knowledge Layer That Refuses to Guess," which details a vendor-neutral blueprint for RAG systems.
How to Shine as a Data Scientist in the Vibe Coding Era
The rise of AI coding tools like those explored in "How to Install Codex CLI" signals a significant shift for data scientists. Coding proficiency is increasingly becoming a commodity; the future belongs to those who leverage these tools strategically. This post outlines how to thrive in this "Vibe Coding Era," focusing on higher-level skills like problem framing, insightful analysis, and communicating data-driven narratives. Discover how to evolve beyond coding and become the indispensable data scientist of tomorrow.
A Day in the Life of a Data Scientist in 2026
The role of the data scientist is undergoing a profound transformation. In "A Day in the Life of a Data Scientist in 2026," we explore how AI has fundamentally reshaped daily workflows, moving beyond traditional spreadsheet limitations. Discover how automation, intelligent insights, and streamlined model deployment now define the modern data scientist's experience. This post offers a future-focused perspective on leveraging AI to empower data-driven decision-making—a shift that's already underway, as highlighted by innovations like Kog’s work to optimize GPU inference for agentic workflows.
Flock says its new tool will help identify police abuse, but hasn’t explained how it works
Flock’s new “Audit Assistance” tool, mandated for all customers, claims to identify police abuse—a bold assertion lacking detailed explanation. While Flock states the tool has already detected instances of misconduct, the mechanics behind its detection remain opaque, prompting legitimate questions about its efficacy. This lack of transparency warrants careful scrutiny. For those navigating complex AI workflows, understanding the nuances of different tools is crucial; consider our guide comparing LangChain and LangGraph for insights into agentic systems.
Looking for real-world examples of predictive analytics in mortgage lending [D]
Predictive analytics are transforming mortgage lending, and understanding the key variables is crucial for your graduate project. Lenders leverage a range of factors beyond just credit activity and interest rates—property appreciation, borrower life events, and debt-to-income ratios all play significant roles in predicting refinance likelihood. Successful models often incorporate a combination of these elements to achieve accuracy.