The author walks through a hands‑on test of the Skywork cloud AI Agent, showing how it can ingest exported Excel data, generate a data‑analysis report, automatically create a PPT, produce narrated video and images, all via a single input without any local deployment.
The review dissects Andrew Ng’s 2026 “AI Prompting for Everyone” course, outlining its three modules—information retrieval tiers, AI‑assisted writing with progressive outlining, and building apps or analyzing data via code execution—while highlighting the Deep Research agentic loop, AI‑slop detection, and a decision‑tree for choosing the right AI tool.
The article analyzes how AskTable uses AI agents to replace static BI dashboards with an immersive, real‑time data‑analysis canvas, enabling business users to query multiple data sources in seconds, while addressing accuracy, table‑finding, and fine‑grained permission challenges.
The article outlines fifteen mature AI Agent scenarios across office productivity, customer service, software development, and data analysis, explains how each works, ranks their readiness, and discusses why some applications are ready for large‑scale use while others remain experimental.
OpenAI’s official ChatGPT for Excel plugin embeds AI directly into Excel, offering natural‑language commands and a suite of AI‑prefixed functions that can generate tables, clean data, extract information, auto‑fill, translate, and even pull financial data, dramatically reducing manual spreadsheet work.
In 2026, a leading e‑commerce platform cut A/B test preparation from 3.2 days to 47 minutes, boosted experiment throughput 4.3× and GMV 2.8% by applying a three‑step automation workflow, an intelligent analysis engine with three‑level reasoning, and a three‑tier human‑machine decision guard.
Analyzing five years of ICLR submission metadata reveals that titles containing a question mark boost acceptance to 45.5% in 2022, while emerging keywords such as diffusion, sparse, and planning dominate high‑acceptance lists, and older topics like federated learning, adversarial attacks, and security suffer low acceptance and high withdrawal rates.
The article dissects the Silly Big Personality Test (SBTI), explaining its 15‑dimensional Euclidean space, threshold‑based hierarchical clustering, statistical tricks like the Barnum effect, and its viral spread as a social meme, while highlighting the scientific limits of such entertainment‑focused assessments.
This guide outlines how generative AI is reshaping the software testing lifecycle, highlights AI's strengths and limits, proposes three evolutionary roles for testers, details four essential capabilities, and provides a practical roadmap for adopting AI‑driven testing.
The article outlines how AI‑native coding in 2026—driven by models like Claude Optus 4.6, GPT Codex 5.2 and Gemini 3.1—relies on refined task prompting, rigorous testing, multi‑model perspectives, and specialized coding‑agent skills such as code review, debugging, and data analysis.
This guide translates and refines the Claude Code founder’s ten practical usage tips, covering parallel worktree sessions, plan‑mode workflows, CLAUDE.md maintenance, custom skills, automated bug fixing, prompt strategies, terminal setup, sub‑agents, data analysis, and learning techniques to help developers get the most out of the AI coding assistant.
The article evaluates GLM‑5, the claimed strongest open‑source large language model, comparing its benchmark scores to Claude Opus, Gemini and GPT, detailing its DeepSeek‑inspired architecture, quantized FP8 deployment requirements, and step‑by‑step usage of Ollama’s free cloud model with Agent, data‑analysis and document‑generation features.
This article details how a production‑grade prompt system powered by large language models was designed to replace manual A/B experiment inspection, introducing a six‑level priority decision tree, robust data preprocessing, and systematic bad‑case analysis that lifted automation accuracy from 68% to over 80% while providing clear, explainable recommendations.
This article walks through a complete data‑science pipeline for autonomous driving, covering CSV/JSON preprocessing with pandas, rule‑based scene classification, a weighted complexity‑risk model, and visual analytics using Matplotlib and Seaborn to identify high‑risk driving scenarios.
This article presents ten powerful SQL techniques—including CTEs, recursive CTEs, temporary functions, CASE‑WHEN pivots, EXCEPT vs NOT IN, self‑joins, ranking functions, delta calculations, cumulative sums, and datetime manipulation—complete with explanations and runnable code examples for data analysts and engineers.
The article shows how product managers can avoid the disappointment of a feature that looks perfect but gets no users by building a complete data‑driven loop that combines user‑behavior and business metrics, walks through a real e‑commerce recommendation case, outlines data‑collection pitfalls, metric‑design methods, hypothesis‑driven analysis, testing procedures and concrete steps to turn insights into iterative product improvements.
The article presents ten powerful Pandas query methods—such as .query(), .isin(), .between(), .str.contains(), .loc, .iloc, .nlargest/.nsmallest, .where/.mask, and .eval()—showing how each can replace verbose code, improve readability, and dramatically speed up data‑analysis pipelines.
ARAG introduces a novel Retrieval‑Augmented Generation framework that tightly integrates LLM‑driven structured data analysis with unstructured information retrieval, addressing the “structured + unstructured” reasoning gap in socio‑economic queries, and demonstrates superior accuracy, robustness, and hallucination resistance through extensive evaluations.
This article presents seven practical DuckDB SQL patterns—querying files directly, treating partition folders as tables, deduplicating with QUALIFY, computing rolling metrics with window functions, pivot/unpivot, handling JSON arrays, and exporting results to Parquet—plus tips and a mini case study that show how to turn a notebook into a lightweight OLAP engine without leaving the Python environment.
This article walks through ten hidden Jupyter Notebook features—including magic commands, terminal integration, shortcut keys, markdown styling, collapsible headings, nbconvert exporting, and tab completion—showing how each can streamline debugging, documentation, and reporting to save roughly an hour of work per day.
This comprehensive guide explains what data visualization is, why it matters, how to choose the right chart type, preprocess data, design effective visuals, select appropriate Python tools, and provides numerous code examples for pie, bar, histogram, box, scatter, bubble, and deviation charts, concluding with best‑practice insights.
This article presents eight essential probability distributions for everyday data‑science tasks, explains when to use each, provides concise Python code for fitting and sampling, and shares practical tips and a real‑world case study to help you choose the right model quickly.
Aloudata Agent introduces a novel NL→MQL→SQL framework that combines large language models with a custom metric query language, enabling business users to perform end‑to‑end intelligent data analysis, attribution, and reporting without technical expertise, while balancing accuracy, cost, and performance.
In this talk, Volcano Engine’s technical expert Chen Shuo explains the evolution of the Data Agent platform, the four‑quadrant framework for AI‑driven analytics, real‑world deployment challenges, architectural upgrades from pipeline to intelligent scheduling, and key lessons for building reliable, enterprise‑grade AI agents.
The 2022 JetBrains and Python Software Foundation survey of over 23,000 developers reveals that 93% now use Python 3, Flask, Django and FastAPI dominate web frameworks, PyCharm and VS Code lead IDEs, and Python remains the primary language for most respondents, especially in web and data work.
This tutorial walks you through building a LangGraph-powered data analysis assistant that converts natural language into SQL, executes queries via NL2SQL and NL2Python tools, visualizes results with a Python interpreter, and deploys the agent using LangGraph CLI and Agent Chat UI for end‑to‑end interaction.
This article analyzes a JD e‑commerce dataset using Python and MySQL to calculate key metrics such as PV, UV, conversion rates, attrition, daily activity, hourly trends, user‑behavior funnels, purchase intervals, retention rates, product sales, and RFM segmentation, and then offers data‑driven recommendations to improve traffic, conversion, and user loyalty.
A recent Stack Overflow survey shows PostgreSQL surpassing MySQL in popularity, and the article explains the technical advantages, real‑world performance tests, and evolving use cases that make PostgreSQL the preferred choice for many developers while still acknowledging MySQL’s enduring role for beginners.
The author evaluates the free open‑source AiPy tool through three real‑world cases—U.S. GDP trend analysis, Cambricon stock assessment, and short‑video production—showing its local, code‑free workflow for data processing, report generation, and multimedia creation while noting minor visual glitches and their fixes.
This guide demonstrates using the Tongyi Lingma AI agent to automatically design an SQLite database schema, generate SQL, insert sample data, and conduct data analysis with visual charts, covering all required installations, configurations, prompts, and code snippets.
This article explores Tomoro’s Lumos data‑intelligent agent, detailing its multi‑agent architecture, technical design, practical implementations, and performance optimizations that enable seamless, low‑threshold big‑data self‑service analysis powered by AI.
This article provides a comprehensive overview of AI Agents, covering their fundamental concepts, core features, technical evolution, work cycle, architectural modules, key technologies such as prompt engineering and RAG, practical development steps, a data‑analysis agent case study, and typical industry applications.
This article explains how large‑language‑model tools like Metaso AI’s “Deep Research” can dramatically speed up reliable data collection for mathematical modeling by providing systematic retrieval workflows, visual summaries, and interactive reports within minutes.
This article walks through a real‑world Pandas problem where missing values in one column are filled based on another column, presenting both a simple one‑liner solution and a more robust function‑based approach with full code examples.
This article explains the 400 °C and 600 °C rules for forecasting cherry blossom opening using effective accumulated temperature, presents the underlying mathematical model, validates it with a 1997‑2022 Japanese dataset, and discusses how adjusting the critical temperature improves prediction accuracy.
This article explains why data cleaning and exploratory analysis consume most of a data scientist's time, introduces the pandas‑profiling library as a richer alternative to pandas.describe(), shows how to install and generate reports, customize them via code or YAML, and discusses performance considerations for larger datasets.
This article walks through practical Python data‑analysis steps for vehicle telemetry, covering data import, time‑based calculations, SOC efficiency metrics, speed binning, group aggregation, common coding pitfalls, and tips for clean, reproducible analysis using pandas.
Inventory turnover rate, not delivery time or fulfillment rate, is the key metric for evaluating supply chain efficiency; this article explains its definitions, business-specific calculations, common pitfalls, data requirements, and practical steps to set, monitor, and leverage the metric for better operational performance.
Python’s open‑source ecosystem, extensive libraries, and low learning curve are rapidly displacing MATLAB in academic research, industry, and AI development, as universities adopt Python curricula, companies integrate it for data analysis and modeling, while MATLAB retains niche strengths in Simulink and specialized engineering toolboxes.
This article outlines the design of a scientific, quantifiable, multi‑dimensional evaluation system for the DataV‑Note intelligent analysis platform, addressing the lack of unified standards and accuracy challenges in AI‑driven data reporting, and proposes concrete metrics, model architecture, and future automation plans.
Enterprises face a sharp paradox: despite exploding data volumes, only about 20% of structured data is used while the remaining 80% of unstructured data stays frozen, and this talk explores how Data Agent‑powered imperfect AI can awaken that hidden value.
This article explores how mathematical and statistical thinking differ in their starting points, research objects, problem formulation, reasoning methods, and attitudes toward uncertainty, highlighting that mathematics emphasizes formal deduction while statistics focuses on probabilistic inference and decision‑making under uncertainty.
This guide introduces the most popular Python libraries across web development, GUI creation, data analysis, machine learning, and automation, explaining their purposes, advantages, selection criteria, and providing concise code examples to help developers quickly adopt the right tools for their projects.
The article presents an in‑depth overview of DeepInsight Copilot, an AI‑driven business intelligence product that streamlines data, information, insight, and decision‑recommendation stages, detailing its functional modules, intelligent agents, multi‑generation technical evolution, architecture, model fine‑tuning, and future challenges and solutions in data analysis.
This guide explains how to design, track, collect, manage, analyze, and monitor product metrics to create a closed‑loop growth system, and demonstrates the process with a real‑world video‑app submission‑rate optimization case study.
In 2025, Python remains the leading language, and this article presents twelve indispensable libraries—ranging from data manipulation with Pandas to deep learning with TensorFlow and PyTorch—detailing their key features and providing code examples to help developers master essential tools across various domains.
A comprehensive glossary for e‑commerce product managers that defines key metrics such as GMV, SKU, UV, CVR, AOV, CPC, and models like RFM and AARRR, while highlighting common pitfalls and practical strategies for data‑driven decision making.
This article provides a comprehensive overview of NumPy, covering its high‑performance ndarray object, core functionalities such as broadcasting and vectorized operations, array creation and manipulation methods, mathematical and statistical functions, linear‑algebra utilities, random number generation, and input/output capabilities with practical code examples.
Recent data confirms that large language models have dramatically reduced Stack Overflow’s monthly question volume, dropping to levels seen in 2009, with key milestones from 2014 to 2025 illustrating how policy changes, the pandemic surge, and the rise of ChatGPT accelerated the platform’s decline.
This article explains why systematic procurement analysis is essential, outlines its eight core modules—from demand and category analysis to supplier management and price control—and provides practical methods such as the upgraded Kraljic matrix, dual‑dimensional supplier scoring, and regular review cycles to boost cost efficiency, operational speed, and risk mitigation.
This article explains why Python has become the leading language for data analysis and machine learning, outlines the essential libraries and frameworks, provides practical code examples, describes typical application scenarios, suggests a staged learning roadmap, and forecasts future trends such as AutoML and federated learning.
This guide walks you through creating a ChatBI intelligent agent by integrating Alibaba's Qwen3 large language model with PAI‑LangStudio, configuring the Model Context Protocol (MCP) server, and connecting to Hologres real‑time data warehouse, covering setup, deployment, and verification steps for enterprise data analysis.
This article demonstrates how large language models can augment business development engineers by providing data insight, automating algorithm training, and enabling low‑cost rapid product prototyping, thereby transforming traditional backend‑focused roles into full‑stack, AI‑enhanced innovators.
This article demonstrates how to calculate the average of list‑type marks for each student in a Pandas DataFrame using two straightforward approaches, complete with code snippets and visual results to help Python learners solve similar data‑processing tasks.
This article explains how large language models combined with Python's data‑science ecosystem can automate metadata extraction, data cleaning, and analysis tasks—illustrated with a step‑by‑step Titanic passenger dataset case study, complete prompts, code snippets, and best‑practice recommendations.
This article provides a thorough overview of NumPy, covering its core ndarray structure, various array creation methods, indexing and slicing techniques, vectorized operations with broadcasting, statistical and linear‑algebra functions, file input/output, and a practical data‑analysis example, all illustrated with executable Python code.
The article explains how AI tools like DeepSeek lower data‑analysis barriers, outlines the new CDA curriculum with three skill levels, describes essential business‑analysis abilities—from status visualization to attribution and quantitative strategy—and highlights the updated textbooks designed to boost professionals' competitiveness in the AI era.
This article introduces the concept of AI-powered Data Agents, outlines their key features and use cases across enterprise analytics, data governance, and intelligent customer service, and reviews recent implementations from Google, Alibaba Cloud, and ByteDance, highlighting their impact on modern data-driven workflows.
This comprehensive guide explains the fundamentals of statistics—including data types, collection, descriptive analysis, visualization tools, measures of central tendency and dispersion, correlation techniques, and regression modeling—providing practical insights for data scientists and engineers seeking to extract meaningful insights from data.
Python in Excel lets Microsoft 365 users write and run Python code directly within spreadsheets, offering built-in libraries for data analysis, visualization, and machine learning, with cloud execution, seamless cell integration, step‑by‑step activation, sample code, library support, data import, troubleshooting, and practical tips.
This article explains the challenges LLMs face in data analysis, introduces the Model Context Protocol (MCP) as a standard bridge, and provides a step‑by‑step guide to integrate Hologres, MCP, and large language models—using Claude Desktop as an example—to create a fast, multi‑source data‑analysis agent.
This guide explains how to create a scheduled financial data analysis system by integrating the XXL‑JOB distributed task scheduler with the Deepseek large‑model AI, covering model selection, local and cloud deployment options, job configuration, and a complete code example for automated news processing.
This article outlines Python's origin in 1989, its evolution through major versions, highlights its simple syntax, powerful libraries, cross‑platform nature, and showcases its widespread use in web development, data analysis, AI, automation, web crawling, and game development.
This article provides a detailed, step‑by‑step guide to installing, configuring, and using DeepSeek—a powerful command‑line data processing tool—covering basic operations, advanced features, scripting tips, and troubleshooting to help users efficiently import, clean, analyze, and visualize data.
This article demonstrates how to combine Playwright web‑scraping of multiple Chinese movie platforms with the DeepSeek AI model to automatically collect data and generate a scientific prediction of the box‑office revenue for the film "Ne Zha 2".
This article explains the concept and syntax of SQL window functions, demonstrates basic and advanced use cases with step‑by‑step data preparation, queries using row_number() and lag, and provides complete scripts to solve common interview‑style ranking problems.
This comprehensive tutorial walks users through preparing their system, downloading the appropriate DeepSeek installer, and performing detailed installation steps for Windows, macOS, and Linux, followed by initial launch, configuration, and troubleshooting tips to ensure a successful setup of the data analysis tool.
This guide walks you through creating a sample e‑commerce sales Excel file with Pandas and then demonstrates twenty practical data‑cleaning and transformation scripts—including handling missing values, renaming columns, filtering, grouping, and exporting—so you can efficiently prepare sales data for analysis.
This article provides a detailed, step‑by‑step tutorial on DeepSeek—a command‑line data processing tool—including its overview, installation on Windows/macOS/Linux, configuration, basic commands for importing, querying, and visualizing data, advanced cleaning and analysis features, practical tips, and a FAQ section.
This comprehensive guide walks you through DeepSeek's features, installation on Windows, macOS, and Linux, configuration details, basic commands for data import, querying, and visualization, as well as advanced cleaning, analysis, plugin extensions, troubleshooting tips, and a handy command cheat sheet.
This article provides a comprehensive, step‑by‑step tutorial on DeepSeek—a versatile command‑line data processing and analysis tool—covering its core features, installation on Windows/macOS/Linux, configuration options, basic commands, advanced functions, practical tips, and troubleshooting advice.
This article presents a comprehensive collection of 25 practical pandas techniques, covering version inspection, DataFrame creation, column renaming, prefix/suffix addition, row and column reversal, dtype selection, type conversion, memory optimization, and efficient construction of DataFrames from multiple CSV files.
This article examines the challenges of outliers in large‑scale internet A/B testing, explains their statistical definition, outlines common causes, evaluates the benefits and limits of removal, and compares traditional trim and winsorize techniques along with practical detection and risk‑control strategies.
This tutorial introduces pandas' two core data objects—Series and DataFrame—demonstrates how to create, index, query, modify, add, delete, sort, merge, and copy them, and shows common parameters, functions, I/O operations, plotting, and a practical log‑analysis example using Python.
This guide walks you through setting up Alibaba Cloud DataWorks, creating a workspace and personal development environment, and importing a Kaggle house‑price prediction notebook to perform data loading, cleaning, feature engineering, model training, and evaluation—all without writing code from scratch.
This article examines the importance of R&D efficiency measurement in the digital era, outlines current challenges, proposes principled metric construction, details data collection, cleaning, and analysis methods, and discusses visualization techniques and practical experience for continuous improvement.
This article presents practical examples and code snippets for using Python libraries such as NumPy, Pandas, SciPy, Statsmodels, Dask, Vaex, Modin, CuPy, Scikit‑learn, TensorFlow, PyTorch, XGBoost, LightGBM, and various visualization tools to perform efficient numerical computation, data processing, machine‑learning modeling, and interactive visual analytics.
This article presents the Tencent PCG OlaChat team's research and practice in intelligent data analysis, covering the DIKW model, evolution of BI platforms, the impact of large language models, challenges of third‑generation data products, detailed product features, agent architecture, system design, and related academic publications.
This article examines the current challenges of enterprise data analysis platforms, outlines three traditional analysis modes, and presents a next‑generation solution that combines Headless BI’s semantic modeling with Chat BI’s large‑language‑model interaction to deliver a more efficient, secure, and user‑friendly analytics experience.
This article explains how large‑model capabilities combined with metric and tag platforms can be used to build intelligent data‑analysis products for enterprises, covering challenges, solution routes such as NLP2SQL, NLP2API, NLP2Python, agent design, planning, and future outlooks.
This article introduces pandas‑profiling as a powerful Python library for automating exploratory data analysis, compares it with R's skimr and pandas.describe(), shows quick installation and usage examples, and explains how to customize reports via code or YAML configuration for small to medium datasets.
This guide explains how to use Pandas to import data from various file formats such as CSV, Excel, JSON, SQL, HTML, HDF5, Pickle, TSV, fixed‑width files and the clipboard, and also demonstrates how to export DataFrames to formats like CSV, Excel, JSON, SQL, HTML, HDF5 and Pickle, providing clear code examples for each operation.
This article presents a complete data‑analysis workflow that scrapes complaint records from a Chinese platform, cleans and merges the data, performs time‑series analysis, generates visualizations and word‑clouds, and interprets the findings to reveal complaint trends and user demands.
This article details Tencent's shift from traditional BI to an AI‑driven intelligent BI platform, describing the challenges of architecture, large‑language‑model integration, and data integration, and presenting the OlaChat framework, unified orchestration, atomic agents, DSL conversion, monitoring, and future roadmap.
This guide walks you through 35 core Pandas commands—from creating DataFrames and inspecting data to filtering, grouping, merging, pivoting, and iterating—providing concise examples that enable quick, effective data manipulation and analysis in Python.
This article teaches how to use Pandas for data reshaping, covering the melt method for converting wide-format data to long-format and the pivot method for the reverse transformation, with practical code examples.
This article explores the challenges of data analysis and governance, introduces the concept of data weaving and its application in an indicator platform, details key technologies such as data virtualization, intelligent acceleration, and active metadata, and outlines current progress and future plans.
This tutorial demonstrates how to use Pandas' advanced features, including creating and manipulating multi‑level indexes, selecting data with hierarchical keys, resetting and sorting indexes, and applying custom functions to columns and rows using the apply method, with comprehensive code examples.
This tutorial demonstrates how to use Pandas' concat and merge functions to combine multiple DataFrames by rows, columns, custom indexes, and multiple keys, providing clear code examples for inner, left, right, and outer joins.
This tutorial teaches how to use the Pandas library's groupby method to group data by one or multiple columns and apply aggregation functions such as sum, mean, max, and min, with clear code examples and a practical exercise.
This article details the end‑to‑end workflow of converting 3D design models into 2D construction drawings, explains the platform’s architecture, testing baseline management, automated data collection, regression testing, and visual diff results, and highlights the massive efficiency gains and future automation roadmap.
AI-driven medical imaging leverages deep learning and massive datasets to detect lesions—often earlier than human eyes— with high accuracy and speed, reducing fatigue‑related errors and workload, while challenges such as data quality, interpretability, privacy, and regulatory compliance must be addressed for widespread clinical adoption.
This article provides a comprehensive overview of 20 essential Pandas data processing methods with detailed code examples covering statistics, data cleaning, transformation, filtering, merging, grouping, sorting, reshaping, aggregation, window functions, time series analysis, conditional selection, indexing, slicing, visualization, type conversion, data filling, filtering, renaming, and import/export operations.
This article presents thirty essential Python techniques for rapid statistical analysis, covering calculations such as mean, median, mode, variance, moving average, correlation, and more, each accompanied by complete code snippets and sample outputs to help readers apply these methods directly.
The article details the development of Yunli XiaoZhi, an AI‑driven logistics chatbot that combines knowledge‑base Q&A, data‑analysis, proactive alerts and report‑pushing to streamline SOP access, reduce manual query effort, and improve operational efficiency for operators, carriers and drivers.
This tutorial introduces Pandas, covering installation, core data structures like Series and DataFrame, data input/output, viewing, selection, filtering, sorting, grouping, aggregation, handling missing values, merging, advanced features such as time series and multi‑index, performance tips, and basic visualization techniques.
This article explains the concept of data thinking, its core components of data sensitivity and methodological experience, outlines a step‑by‑step data analysis process, and shows why cultivating this mindset improves decision‑making, communication efficiency, and business opportunity discovery across various domains.
This guide explains why data analysis is essential, breaks it into three core stages—data collection, data mining, and data visualization—offers practical tool recommendations, and presents principles for efficient learning and skill development.
This guide compares the strengths and weaknesses of common data analysis tools—from basic spreadsheet software and multi‑dimensional tables to professional statistical packages, BI platforms, and programming languages like R and Python—helping readers select the most suitable solution for their needs.
Discover seven essential open‑source Python repositories—from data analysis with Pandas and workflow automation with Apache Airflow to AI‑powered G4F, web crawling with Scrapy, Telegram bots via Ultroid, team chat with Zulip, and crypto trading with Freqtrade—each offering powerful capabilities and community support.
