towards data science
towards data science on Beyond Market Intelligence: a running collection of 125 stories we have gathered and hand-picked because they are worth your time. Every post here touches on towards data science 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 towards data science, 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.
The Power BI Developer's Survival Guide to Microsoft Fabric
Power BI developers, a significant shift is underway. Microsoft Fabric has arrived, effectively replacing Power BI Premium. This guide provides a clear, concise overview of what’s changed—and what hasn’t—to ensure a smooth transition. We'll equip you with the essential knowledge to navigate this evolution and confidently begin leveraging Fabric's capabilities. If you're exploring the broader landscape of AI-powered development, consider "How to Solve the Right Problem in the Age of Agentic AI" for a practical framework.
How to Run 10+ Claude Code Sessions Without a Powerful Computer
Tired of hardware limitations hindering your AI agent explorations? Discover how to effectively run 10+ Claude Code sessions concurrently, even without a high-powered computer. This guide unlocks a practical approach to parallel coding agent workflows, empowering you to leverage AI's potential without significant investment. Explore strategies for optimized resource utilization and efficient session management. Interested in the broader landscape of AI agent development? See our article on Meta’s Muse Spark model for further insights into agent capabilities.
My Model Worked Perfectly. Then I Tried to Make It Useful.
Successfully deploying machine learning models can be deceptively challenging. Many data scientists achieve impressive accuracy in isolation, but translating that success into a practical, accessible service is a crucial next step. "My Model Worked Perfectly. Then I Tried to Make It Useful." details the journey of transforming a trained churn classifier into a robust FastAPI service—a vital component for integrating AI into broader software ecosystems.
How to Solve the Right Problem in the Age of Agentic AI
As agentic AI accelerates, the ability to define the *right* problem becomes paramount—and increasingly complex. Uncertainty in problem framing can lead to wasted resources and misdirected implementation. This framework offers a practical approach to proactively reduce that uncertainty, ensuring your AI investments deliver tangible value. Discover how to strategically pinpoint opportunities ripe for agentic solutions. For deeper exploration of related AI techniques, consider “Graph Neural Networks: GCN, MPNN, and GAT, Explained Simply.”
Graph Neural Networks: GCN, MPNN, and GAT, Explained Simply
Delve into the world of Graph Neural Networks (GNNs) with our visual guide, exploring the core mechanisms of Convolutional GNNs (GCNs), Message Passing Neural Networks (MPNNs), and Graph Attention Networks (GATs). We break down these powerful architectures, revealing how they process data structured as graphs—a format increasingly vital for diverse applications. Understand the underlying principles that empower GNNs to learn from relationships, not just individual data points. For a deeper dive into ensuring reliable AI responses, see "A RAG That Says ‘Not in This Document’."
A RAG That Says “Not in This Document” Has to Show Four Kinds of Evidence
Retrieval-Augmented Generation (RAG) systems must deliver more than just a “Not in This Document” response; a confident, unsupported denial is a critical bug. Enterprise Document Intelligence, Vol. 1 #B3, details why justifying negative answers is paramount, requiring four distinct pieces of evidence. This approach ensures transparency and builds trust in the system’s reasoning. For those grappling with data quality challenges, consider "Avoiding Entity Key Drift in a Data Lake," which explores similar issues of data matching and refinement.
Avoiding Entity Key Drift in a Data Lake: Step 2, When Fuzzy Matching Stops Working
Data lakes often suffer from entity key drift, a challenge that normalization alone can’t fully resolve. Our latest post, “Avoiding Entity Key Drift in a Data Lake: Step 2,” details a critical juncture where fuzzy matching proves insufficient for reliable data cleanup. We initially developed a matcher to address this, but real-world testing revealed inherent limitations. This article outlines the resulting architecture, born from setting aside the matcher and charting a new course.
A Practical Introduction to PySpark Window Functions
Traditional `groupBy` functions in PySpark offer a foundational approach to data aggregation, but often fall short when complex calculations require context beyond a single group. This practical introduction explores PySpark Window Functions—a powerful tool for performing calculations across a set of rows related to the current row. Discover how window functions empower you to derive richer insights, enabling more sophisticated data analysis and transformative reporting.
Beyond Point Predictions: A Practical Introduction to Bayesian Neural Networks
Traditional neural networks offer predictions, but often lack crucial context: the *uncertainty* surrounding those predictions. “Beyond Point Predictions: A Practical Introduction to Bayesian Neural Networks” explores a transformative approach to data analysis, enabling more informed decision-making through robust uncertainty quantification. Discover how Bayesian methods provide a clearer understanding of potential outcomes, moving beyond simple point estimates. For those navigating the complexities of AI workflows, consider "7 Common Python Mistakes to Avoid," which highlights the importance of process integrity.
5 AI Skills That Will Keep Data Scientists Relevant in 2027
## 5 AI Skills That Will Keep Data Scientists Relevant in 2027 The data science landscape is evolving rapidly. To remain valuable through 2027, focus on these five essential AI skills: Prompt Engineering, Generative AI Model Fine-Tuning, Responsible AI Implementation, Advanced Retrieval-Augmented Generation (RAG), and AI-Powered Data Synthesis. Each addresses a critical challenge – from maximizing LLM output to ensuring ethical deployment and generating synthetic datasets. Discover runnable code examples for each skill—easily pasted into your notebook—to accelerate your learning.
Why RAG Complexity Should Be Earned
RAG pipelines often escalate in complexity prematurely, introducing elements like reranking and agentic seeking before addressing fundamental retrieval issues. Our framework, detailed in "Why RAG Complexity Should Be Earned," advocates a different approach: build complexity deliberately, only in response to observed failure modes. Starting with lexical or hybrid search, we incrementally add layers as needed, ensuring each addition demonstrably improves performance.
FAQ as RAG: When You Get to Design the Corpus
Traditional Retrieval-Augmented Generation (RAG) pipelines are fundamentally rethought in "FAQ as RAG." This innovative approach, detailed in Vol.1 #B2, prioritizes corpus design, simplifying parsing and transforming retrieval into a caching mechanism. Critically, few-shot prompting is redefined as a retrieval challenge. This represents a significant shift for enterprise document intelligence. Explore this transformative model and discover how it empowers more efficient and accurate AI applications – a concept further explored in "Your LLM Can Return Perfect JSON and Still Be Wrong."
Your LLM Can Return Perfect JSON and Still Be Wrong
Large Language Models (LLMs) excel at producing seemingly flawless JSON outputs, yet these structures can still mask underlying inaccuracies when dealing with real-world, incomplete data. Recent exploration reveals a critical distinction: perfect formatting doesn’t guarantee factual correctness. This post dives into that nuance, examining how structured outputs can mislead and offering insights for more robust data validation. For a broader perspective on AI's impact on technological landscapes, consider "Nvidia’s $3.5B MediaTek bet reveals its plan for tackling Big Tech’s AI chip buildout."
Context Engineering Is Changing. Here’s What It Means for Data Scientists
The landscape of data science is evolving, and context engineering is at the forefront of this shift. This article explores the latest guidelines reshaping how data scientists work, moving beyond traditional approaches to unlock deeper insights. Discover practical applications of these advancements to streamline your workflows and elevate your data analysis. If you're curious about the evolving role of AI coding agents, consider “When to Use Claude Code and When to Use Codex” for further exploration of this related topic.
When to Use Claude Code and When to Use Codex
Choosing between Claude Code and Codex can be confusing. Both are powerful coding agents, but their strengths differ. Codex excels at translating natural language into code, particularly for established languages and frameworks. Claude Code shines with complex reasoning, debugging, and collaborative coding tasks, especially in newer or less-documented environments. Understanding these distinctions empowers you to select the optimal tool for your project.
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."
Connecting My LangGraph AI Agent to Postgres
Connecting your LangGraph AI agent to a Postgres database unlocks powerful capabilities for data-driven workflows. This post details how to establish that connection, offering clear guidance for both local development and cloud deployment. We’ll explore setting up the backend using Docker for streamlined local testing, and then outline strategies for scaling to the cloud. For those tackling complex enterprise workflows, consider the recent exploration of an 8B AI model mirroring Claude Opus—a relevant challenge in managing substantial data sets.
Why Claude Code Time Estimates Are Poor
Large language models like Claude often provide inaccurate time estimates when generating code. This discrepancy stems from their probabilistic nature and limitations in fully simulating execution environments. Consequently, relying on these estimates can lead to unrealistic project timelines and frustrated developers. Learn why Claude's code time predictions fall short and, more importantly, how to become a more effective communicator when working with LLMs for programming tasks. For a deeper dive into related AI infrastructure challenges, see our article, "Connecting My LangGraph AI Agent to Postgres."
Stop Giving Your AI Agent a Search Box and Start Giving It Typed Tools, Hard Bounds, and a Gate It Cannot Talk Past
Traditional AI agents relying on search boxes often stumble, lacking precision and control. A more effective approach involves equipping them with typed tools, hard boundaries, and a definitive gate—preventing unauthorized outputs. Our latest post explores this transformative shift, detailing how restricting context and enabling knowledge graph navigation within strict limits impacts performance. Through analysis of four models and a single critical misprediction, we reveal whether this method unlocks substantial improvements. Learn more about practical applications in "How to Work with AI Coding Agents."
How to Work with AI Coding Agents
AI coding agents promise better code, not just *more* code, and mastering their use is essential for modern data professionals. This practical guide explores how to effectively collaborate with these agents, maximizing their potential to streamline development and improve code quality. Discover strategies for prompting, evaluating outputs, and integrating AI assistance into your existing workflows. For a deeper understanding of the evolving roles of humans and AI in analytics, explore "Agentic AI Is Rewriting The Analytics Stack."
I Trained Six Models for Fraud Detection, and the Best One Isn't in Production
My final-year project involved training six distinct models for fraud detection, revealing a surprising disconnect between evaluation metrics and real-world production decisions. While one model demonstrably outperformed the others during testing, it remains untapped in our current system. This experience illuminated the critical gap between rigorous evaluation and practical implementation—a challenge many data scientists face. Interested in similar explorations of AI’s practical application? Check out "Catching bugs in scikit-learn [D]" for a deep dive into model reliability.
Agentic AI Is Rewriting The Analytics Stack But There's One Skill It Still Can't Touch
Agentic AI is rapidly reshaping the analytics stack, automating tasks previously requiring significant human effort. However, a critical distinction remains: strategic oversight. While agents excel at execution, humans retain the irreplaceable ability to define nuanced goals and adapt to unforeseen complexities. Understanding where agent capabilities best align with human judgment—and why—is paramount for maximizing productivity and mitigating risk. As Gravitee highlights in "Enterprise AI's real risk isn't autonomous agents," managing the interactions *between* agents is key.
Is Agentic AI Just Automation?
The rise of "Agentic AI" has sparked considerable excitement, but a critical question remains: is it truly transformative, or simply sophisticated automation? Many current agents operate as complex flowcharts, limiting their adaptability and problem-solving capabilities. This post explores why this architecture falls short and outlines a more effective approach to building genuinely intelligent agents. Delve deeper into maximizing coding agent performance with our guide, "How to Effectively Solve 100+ Tasks with Claude Code," for practical strategies.
Why Random Forest Needs to Be This Random
Bagging ensembles of decision trees offer improved predictive power, but reach a performance ceiling. The core limitation lies in the correlated errors of individual trees. This post explores why—revealing the equation that quantifies this constraint and presenting an experiment demonstrating its impact. Discover how introducing controlled randomness within the Random Forest algorithm overcomes this barrier, unlocking significantly enhanced accuracy. For a deeper dive into related AI challenges, see our article, "Hallucinations, Watermarks, Removers, and a Squeezed Balloon.”