generative AI for data analysis
generative AI for data analysis on Beyond Market Intelligence: a running collection of 169 stories we have gathered and hand-picked because they are worth your time. Every post here touches on generative ai for 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 generative ai for 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.
60 SQL Interview Questions From Beginner to Advanced (2026)
Preparing for SQL interview questions is a smart move for any aspiring data professional. Given that SQL is required in 90% of data analyst job postings, mastering this skill can significantly enhance your employability. This guide features 60 carefully curated SQL interview questions, ranging from beginner to advanced levels, to help you build confidence and expertise. Whether you’re just starting your journey or looking to refine your knowledge, these questions will equip you with the insights needed to excel in your next interview.
Beyond Code Generation: AI for the Full Data Science Workflow
In "Beyond Code Generation: AI for the Full Data Science Workflow," we explore how integrating Codex and MCP can streamline your data science processes. By connecting Google Drive, GitHub, and BigQuery, this innovative approach transforms traditional workflows into cohesive, efficient systems. Discover how AI can enhance your data management experience, enabling seamless analysis and collaboration across platforms. This post invites you to rethink your data workflow, empowering you to leverage AI's potential for greater productivity and insightful decision-making.
Open-source AI data analyst - tutorial to set one up in ~45 minutes
Unlock the potential of open-source AI data analysts with our hands-on tutorial, designed to get you set up in just 45 minutes. Created by one of the builders behind this initiative, this guide offers a practical approach to harnessing AI for your data needs. You'll run simple terminal commands to import your database schema, generate contextual layers, and connect seamlessly with your coding agent. While it won't transform your workflows overnight, it provides a solid starting point for efficient analysis.
Testing autonomous agents (Or: how I learned to stop worrying and embrace chaos)
In the rapidly evolving landscape of autonomous agents, the stakes have never been higher. After 18 months of building production AI systems, we’ve learned that the challenge isn’t just about creating agents that respond accurately; it’s about ensuring they operate reliably and safely. A misstep—like an agent mistakenly approving a major vendor contract—can have serious consequences. This exploration delves into the complexities of engineering reliable autonomous agents, emphasizing the importance of guardrails, layered reliability, and the balance between innovation and caution in this transformative field.
The three disciplines separating AI agent demos from real-world deployment
Deploying AI agents effectively in real-world scenarios presents significant challenges that go beyond successful demonstrations. Issues such as fragmented data, unclear workflows, and high escalation rates hinder enterprises' progress in adopting these technologies. As Sanchit Vir Gogia highlights, the true test lies in navigating the complexities of organizational environments.
40+ Python Interview Questions and Answers for Data Roles (2026)
Prepare to elevate your data role interviews with our comprehensive guide featuring over 40 Python interview questions and answers tailored specifically for data positions. Each question comes complete with a practical code example, a clear explanation of the underlying concept, and insights into what interviewers are truly assessing. Organized by role, this resource helps you concentrate your study efforts where they will be most impactful. With Python's prominence in data science and analytics, mastering these questions will empower you to showcase your skills confidently.
What is DeerFlow 2.0 and what should enterprises know about this new, powerful local AI agent orchestrator?
DeerFlow 2.0, launched by ByteDance, is a groundbreaking open-source AI agent orchestrator designed to streamline complex, long-duration tasks in enterprise settings. This powerful framework enables the autonomous orchestration of multiple AI sub-agents, facilitating deep research, report generation, and data analysis—all within a secure, isolated environment. Available under the MIT License, it allows for seamless local deployment, ensuring data sovereignty and compliance. As it gains traction in the AI community, enterprises must weigh its technical demands against the potential for transformative productivity enhancements.
15 Power BI Project Ideas to Build Your Portfolio in 2026
Building a robust Power BI portfolio is essential for aspiring business or data analysts in 2026. Employers seek candidates who can transform messy data into clean models and create impactful dashboards that drive informed decision-making. This article presents 15 compelling Power BI project ideas that will not only showcase your technical prowess but also demonstrate your ability to derive actionable insights from data. These projects serve as tangible evidence of your skills, empowering you to stand out in a competitive job market.
Need Excel Help – Investor Distribution Comparison (High Visibility Project)
Hi everyone, I'm an accountant in the real estate sector, tasked with a high-visibility project to validate our new automated investor distribution portal. After two years of manually calculating returns, I’m eager to elevate my Excel comparison of historical distributions against the portal's output. I seek to create a robust, presentation-ready spreadsheet that not only highlights variances but also provides meaningful insights for management. Any suggestions on formulas, layout, or features to enhance clarity and professionalism would be greatly appreciated. Thank you for your support!
30 Data Science Projects for Beginners to Advanced (with Source Code)
Building a strong data science portfolio is essential for showcasing your skills and standing out to potential employers. "30 Data Science Projects for Beginners to Advanced" provides an invaluable resource, offering a curated list of projects that span various skill levels. Each project includes source code, real datasets, and step-by-step instructions to guide you through completion.
Moving From Moment.js To The JS Temporal API
As JavaScript continues to evolve, so does its approach to handling time. The introduction of the Temporal API marks a significant advancement over the built-in `Date` API and Moment.js, addressing longstanding limitations while providing a more robust framework for date and time manipulation. In this guide, Joe Attardi offers practical recipes for seamlessly migrating your Moment.js code to the Temporal API. Discover how this innovative standard can enhance your time management capabilities and empower your JavaScript applications with greater accuracy and simplicity.
Extracting reports from a website based medical records system, refining the data, and consolidating it in a sheet
Navigating a web-based medical records system to extract and consolidate reports can seem daunting, especially for those new to Excel. However, with your experience in R and Python, you're well-equipped to tackle this challenge. Start by learning how to automate the login process using Selenium to streamline report generation. Once you've downloaded "Report A1," "Report B1," and "Report C2," focus on transforming and analyzing the data within those files.
14 Machine Learning Projects for Beginners to Advanced (2026)
Embarking on a journey into machine learning can be both exciting and overwhelming. This article presents 14 carefully curated projects, organized by difficulty, that cater to everyone from beginners to advanced practitioners. Each project includes free datasets, starter code, time estimates, and valuable insights on how to enhance your work into a standout portfolio piece. By exploring these projects, you'll not only develop transferable skills but also create tangible results to impress hiring managers, empowering your career in this dynamic field.
Persuasive Design: Ten Years Later
In "Persuasive Design: Ten Years Later," Anders Toxboe revisits the principles of persuasive design, highlighting its relevance in today's fast-evolving digital landscape. Despite ongoing reliance on usability tweaks and minor behavioral adjustments, many product teams face stagnating results in activation, retention, and user engagement. Toxboe clarifies which core elements of persuasive design have endured over the last decade, offering insights that challenge shallow gamification approaches. This update invites product teams to explore deeper, more impactful strategies for fostering genuine user connections and enhancing overall experience.
Any faster way to merge large Excel reports automatically?
Are you struggling to merge large Excel reports automatically? If you're dealing with financial and operational data generated daily, the task of manually combining multiple reports can be daunting and time-consuming. Even tools like Power Query may fall short when handling thousands of rows across numerous files. Fortunately, there are more efficient approaches to streamline this process. By exploring automation tools designed to effortlessly pull and merge your reports into a structured dataset, you can reclaim valuable time and enhance your data analysis capabilities.
Project Tutorial: Exploring Financial Data Using the Nasdaq Data Link API
In the world of financial analysis, accessing data has often required cumbersome downloads or costly subscriptions. However, the advent of APIs has transformed this landscape. With just a few lines of Python code, you can effortlessly pull structured financial datasets from providers like Nasdaq, enabling you to begin your analysis in minutes. In this tutorial, we will guide you through the process of connecting to the Nasdaq Data Link API, empowering you to explore and leverage rich financial data with ease and efficiency.
Getting unexpected results when generating descriptive statistics for a column of Z scores
If you’re encountering unexpected results when generating descriptive statistics for your Z scores, you’re not alone. This common challenge often arises from formula applications or data handling nuances. The formula you're using, =STANDARDIZE(x, mean, stdev), is foundational, but ensuring the accuracy of your mean and standard deviation inputs is crucial. Your goal is to achieve reliable averages and standard deviations, ideally around 2. Let’s explore potential pitfalls and solutions that can enhance your understanding and streamline your data analysis in Excel.
How to Become a Data Analyst (Step-By-Step Guide for 2026)
Navigating the path to becoming a data analyst can feel overwhelming, especially with advice ranging from mastering Excel to pursuing a master’s degree. The reality is more nuanced. Data analysts are crucial in transforming raw data into actionable insights, answering pivotal questions like why sales fluctuate or how customer behavior changes. This step-by-step guide for 2026 will clarify the essential skills, educational requirements, and practical experiences you need to thrive in this dynamic field.
Project Tutorial: Predicting Employee Productivity with Decision Trees and Random Forests
Are you grappling with the challenge of predicting employee productivity in your factory? In this project tutorial, we’ll guide you through using decision trees and random forests to create a machine learning model that forecasts whether a workday will be productive. By analyzing team-level productivity data, you’ll uncover the key conditions that drive performance on the factory floor. Join us as we transform complex data into actionable insights that empower you to optimize productivity and enhance team outcomes. Let’s dive in!
10 Data Analysis Tools For Entry-Level Analysts
Navigating the world of data analysis tools can feel overwhelming, especially with countless lists suggesting 15, 20, or even more options. For entry-level analysts, this plethora of choices often complicates rather than clarifies where to begin. In this guide, we’ll distill the essentials, highlighting the top 10 data analysis tools that empower you to tackle real-world challenges effectively. Discover how these tools can streamline your workflow and enhance your insights, setting you on a path to success in your data analysis career.
I built a free Excel add-in that adds 12 dashboard visuals and tools, including a vertical waterfall chart
Are you frustrated with the limitations of traditional Excel dashboards? Introducing Pine BI Lite, a free Excel add-in that enhances your reporting capabilities with 12 powerful visualizations, including a dynamic vertical waterfall chart. This intuitive tool simplifies Actual vs Target reporting and variance analysis, allowing you to create impactful dashboards directly from the ribbon. With built-in templates and easy customization, Pine BI Lite transforms your data into actionable insights. Dive in to discover how this innovation can elevate your Excel experience!
Best Machine Learning Certifications in 2026
In 2026, the landscape of machine learning certifications is more complex than ever, with countless options vying for your attention. Whether you’re transitioning into tech, expanding your developer toolkit, or seeking to elevate your existing data skills, choosing the right certification is crucial for proving your expertise. This guide will navigate through the best machine learning certifications available, highlighting what sets them apart and how they align with industry demands.
Built an open-source Excel structural risk analyzer - looking for feedback from model builders
Are hidden structural issues in your Excel models causing silent failures? Meet XLChek, an open-source structural risk analyzer that scans .xlsx files offline, pinpointing risks like circular references and orphan calculations. Designed for advanced Excel users and model builders, this read-only tool generates clear HTML reports, ensuring your spreadsheets maintain structural integrity without the need for data uploads. I invite your critical feedback on its utility and potential risks it may overlook. Join me in refining this tool for better financial modeling and operational accuracy.
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40+ Data Analyst Interview Questions and Answers for 2026 [Entry-Level Guide With Code]
Preparing for a Data Analyst interview can be daunting, especially when self-doubt creeps in: "Am I ready? What if I freeze?" This guide alleviates those concerns by providing over 40 essential interview questions and answers tailored for entry-level candidates. With a strong foundation in SQL, Python, and statistics, you can confidently navigate the interview landscape. Dive in to discover the insights and strategies that will empower you to showcase your skills and secure that coveted position in 2026. Let’s transform your anxiety into assurance.