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.
This article describes the design, implementation, and impact of a large‑model‑driven logistics chatbot that unifies knowledge Q&A, data analysis, proactive alerts, and report pushing to streamline operations for functional staff, frontline workers, and managers, dramatically reducing query time and improving decision efficiency.
The document describes the design, implementation, and operational results of Yunli XiaoZhi, an AI‑driven portable knowledge‑base and data‑analysis chatbot that consolidates SOPs, manuals, and real‑time information for logistics staff, using LangChain‑based RAG, vector databases, and large‑model prompting to improve query efficiency, proactive alerts, and reporting across multiple user groups.
SQLite, the lightweight embedded SQL engine created in 2000, is the world’s most deployed database, powering mobile apps, embedded systems, desktop software, data analysis tasks, and even browser‑based acceleration via WebAssembly, thanks to its zero‑configuration design and broad language support.
This guide introduces Pandas, shows how to set up the environment, and provides detailed explanations and runnable code for common DataFrame operations such as reading CSV files, inspecting data, selecting columns, descriptive statistics, filtering, grouping, merging, and handling missing values.
SQLite, a lightweight serverless relational database written in C, powers billions of devices and is used for mobile app storage, embedded systems, offline desktop applications, small‑to‑medium data analysis, and browser‑based acceleration via WebAssembly, supporting languages such as C, C++, Java, Python, and Swift.
This article demonstrates how to use Python to fetch Amap traffic iso‑chrones via API calls, process city and district IDs, and visualize the 30‑minute travel range on an interactive map with Folium, providing complete code snippets for data acquisition, parsing, and map rendering.
This case study details how a recruitment platform identified user‑experience gaps, used journey‑mapping to uncover blue‑collar preferences, implemented design strategies such as local‑job emphasis, anti‑spam features, and address filtering, and achieved measurable improvements in click‑through and conversion rates.
This article demonstrates eleven practical Pandas examples covering grouping aggregation, conditional filtering, rolling windows, multi-indexing, melting, broadcasting, concatenation, merging, time-series creation, missing-value handling, and custom function application, each accompanied by complete Python code and expected output.
This article introduces seven noteworthy open‑source Python repositories—including Pandas, Apache Airflow, G4F, Scrapy, Ultroid, Zulip, and Freqtrade—highlighting their key features, typical use cases, and where to find them, offering developers a curated guide to valuable tools for data analysis, workflow automation, web crawling, chat bots, team collaboration, and crypto trading.
This article explains the fundamentals of metric attribution, outlines a three‑step framework for identifying, analyzing, and solving metric changes, compares deterministic, probabilistic, and speculative methods, and illustrates the approach with two real‑world case studies using decomposition and machine‑learning techniques.
This article explains the concept of quality operations, outlines its main components, lists essential quality‑operation KPIs, describes how to collect and analyze KPI data, and shows how to align KPI metrics with overall business strategy.
This article introduces seven powerful Python libraries—Pandas, Selenium, Flask, Scrapy, Requests, Faker, and Pillow—explaining their core features, typical use cases, and providing ready‑to‑run code snippets to help developers boost productivity and automate routine tasks.
This article explains metric attribution for internet platforms, covering its definition, a three‑step framework, basic deterministic and probabilistic methods—including indicator decomposition, machine‑learning and SHAP techniques—illustrated with two detailed case studies and a brief overview of supporting tools.
The article provides a detailed technical analysis of Microsoft’s preview‑only Python in Excel feature, outlining its current capabilities, major limitations such as lack of VBA replacement, restricted package usage, cloud‑dependency, and workflow friction, while suggesting improvements and alternative approaches for data‑centric users.
This article introduces the open‑source Python package TransBigData, explains how to install it, and demonstrates step‑by‑step methods for reading mobile signaling data, preprocessing, identifying stays and moves, extracting home and work locations, and visualizing individual activity patterns using Jupyter notebooks.
This tutorial demonstrates how to use Python's Pandas library to read, clean, filter, sort, deduplicate, group, pivot, and export Excel data, providing step‑by‑step code examples that turn large spreadsheets into concise, analyzable summaries.
This article introduces how to automate Excel chart generation using Python libraries like pandas and openpyxl, enabling efficient data visualization and analysis.
When large‑scale A/B testing is impractical for high‑traffic, socially intensive games, developers can rely on methods such as Difference‑in‑Differences, hypothesis proportion analysis, and differential‑ratio comparison to infer the causal impact of content updates on key performance metrics.
This article demonstrates how to use Python to scrape Weibo comments about the TV series 'The Legend of the Year', generate word clouds, compute character frequencies and co‑occurrences, and visualize the resulting character relationship network with Gephi.
This article presents a collection of Python scripts that automate key HR tasks—including resume parsing, interview invitation emailing, pay‑slip creation, hiring‑channel analysis, turnover calculation, satisfaction survey reporting, salary competitiveness comparison, employee data updates, Word document generation, and leave‑request handling—complete with ready‑to‑run code examples.
Data analyst Wu Weiwei from eBay China will present how uplift modeling—a causal inference technique—can be applied to e‑commerce coupon distribution, demonstrating methods to identify marketing‑sensitive users, optimize subsidy strategies, and improve business efficiency through data‑driven decision making.
In the data‑driven era, KNIME offers a free, visual, and highly scalable platform that streamlines massive data ingestion, preprocessing, analysis, automation, and visualization, enabling researchers to handle millions of records efficiently without extensive coding or costly software.
This article examines how large language models such as GPT and Transformer‑based architectures are reshaping the automotive sector by enhancing in‑vehicle intelligence, streamlining product development, improving customer service, and redefining data analyst roles, while also presenting practical experiments, deployment challenges, and future directions.
This article explains why and how to conduct A/B testing for UI design, outlines experiment setup, variable creation, and data analysis, and presents detailed case studies of JD Express mini‑program pop‑up and order‑completion page experiments that demonstrate measurable improvements in click‑through and conversion rates.
This guide introduces AWK’s powerful text‑processing capabilities on Unix/Linux, covering analysis, formatting, pattern matching, calculations, custom functions, flow control, and a practical IP‑counting example, all illustrated with clear command‑line snippets.
This article demonstrates how to download the Google 1‑gram dataset, load its 1.4 billion rows using NumPy and the PyTubes library, compute yearly word‑frequency percentages for terms like Python, and visualize the results while discussing performance challenges and future improvements.
The DIN‑SQL approach enhances Text‑to‑SQL performance by using large language models in a decomposed in‑context learning framework with schema linking, query classification, SQL generation, and self‑correction modules, achieving state‑of‑the‑art 85.3% execution accuracy on the Spider benchmark by breaking complex queries into manageable sub‑tasks.
This guide recommends a four‑book sequence—SQL Data Analysis, Python Data Analysis, Python Report Automation, and Python Statistical Analysis—to help aspiring data analysts build essential SQL skills, master Python data manipulation, automate reporting, and grasp the theoretical foundations of statistics.
This article demonstrates how testers can ensure large‑scale data accuracy by leveraging Python’s Pandas library to compare and match data across files and databases, presenting a reusable class, field‑mapping techniques, code examples, and a comparison of Pandas with other data‑handling libraries.
This article explains the concept of time series, describes its main patterns such as long‑term trend, seasonal, cyclical and random variations, and demonstrates a practical Python analysis of a three‑year sales dataset from a Taobao shop using pandas and matplotlib.
This article explains the concept and calculation of odds ratios, interprets their values, and illustrates their use with examples from epidemiology and marketing, while also discussing their limitations and proper interpretation.
This article walks through a real‑world Pandas solution that calculates each merchant's total orders and first‑order counts to reveal which merchants are most popular among new customers, using AI‑assisted code generation and step‑by‑step explanation.
Happy numbers are positive integers that eventually reach 1 when repeatedly replaced by the sum of the squares of their digits; this article defines them, demonstrates Python implementations using recursion and sets, and explores practical uses in cryptography, game development, and data analysis.
This article walks through a practical Pandas solution for merging multiple text files, showing how to read, concatenate, clean, and export data with concise Python code, while also offering additional resources and community support for Python learners.
This article explains why Pandas' dropna may fail on empty strings, shows the correct way to filter them out using a concise inequality condition, and provides clear code examples for cleaning data in Python.
This article walks through a Python community question on sorting a Pandas pivot table by its values and extending the result with a summary row that totals each column, providing clear code examples and explanations for data analysis tasks.
This article reviews the evolution of data analysis and business intelligence, highlights current trends such as precision, agility, and real‑time needs, discusses common challenges, and presents the design and implementation of a unified semantic layer and indicator platform to enable agile, accurate, and real‑time analytics.
This article presents a collection of Python code examples demonstrating how to scrape, process, visualize, and analyze data from news sites, social media, stock markets, e‑commerce, web traffic, text, images, and more, covering tasks such as clustering, time‑series forecasting, and sentiment analysis.
This article walks through a Pandas data‑manipulation problem where a user needs to generate sequential dates, convert column types, reorder columns, and produce a FIFO‑style dataset, providing the original code, a step‑by‑step solution with code snippets and illustrative images.
Empirical research, a systematic method that relies on real data and observation rather than theory alone, follows a clear process—from defining questions and hypotheses, collecting and cleaning data, selecting analysis techniques, to interpreting results and reporting findings—illustrated by the classic Pygmalion Effect study.
The paper defines AB increment, shows how to calculate DAU gains from per‑user visit rates, explains how social viral experiments introduce unidirectional or bidirectional contamination that biases increment estimates, and proposes four probability‑estimation schemes—exponential smoothing, expansion coefficients, and homogeneous‑group sampling—to correct the bias based on experiment design and business context.
This article explains Douyin’s growth‑analysis methodology, how to construct a comprehensive growth‑metric system with North‑Star indicators and hierarchical metric layers, the end‑to‑end analysis loop, new scenario‑driven metric applications, and a detailed case study on improving video‑submission rates.
This comprehensive guide shares a seasoned data professional’s methodology, classifications, goal‑setting techniques, team‑building strategies, analyst competencies, report standards, and the three‑pillar "trend‑method‑technique" framework to help newcomers and veterans alike extract business value from data.
Simpson’s Paradox shows how aggregated data can suggest one trend while each subgroup reveals the opposite, illustrated with hospital survival rates where overall A appears better than B, yet detailed analysis by severity flips the conclusion, highlighting the need to consider background variables in statistical interpretation.
This article analyses how fixed‑order and random‑order time‑slice carousel designs affect experiment interference, recovery cycles, and data homogeneity through theoretical discussion and extensive simulations, revealing that mixed scheduling rarely shortens cycles and may worsen homogeneity compared to pure fixed‑order approaches.
This article explains why metrics are essential, defines what a metric is, describes the four essential elements of metric design, outlines the three‑step design process, discusses measurement scales and time characteristics, and provides criteria for evaluating good metrics.
This article explains how to implement online traffic replay for Python automated testing by parsing logs, storing request data in a local MySQL database, analyzing user information with data‑analysis libraries, and locally persisting URL, method, headers, and body details using JSON files, complete with sample code.
Using Tencent's Hunyuan model, the tutorial walks through a Python workflow that scrapes a student‑score table from a web page, saves it as CSV and Excel, cleans missing values, computes total and average scores, and visualizes their distributions with matplotlib, illustrating how LLMs can accelerate data‑analysis coding while still needing human verification.
This guide presents a comprehensive set of SQL statements for the idc_evaluating database, illustrating how to retrieve rows by province or IP, count and delete active IPs, describe table structures, perform conditional aggregations, and execute inner‑join queries with calculated fields.
SQL database queries are essential skills for data analysts, enabling them to extract, transform, and analyze data from databases through various operations like filtering, sorting, aggregation, and joining.
This study analyzes JD e‑commerce operational data from February to April 2018, employing Python and SQL to compute key metrics such as PV, UV, conversion rates, attrition, purchase frequency, time‑based behavior, funnel analysis, retention, product sales, and RFM segmentation, and provides actionable recommendations for improving user engagement and sales performance.
This article walks through a Pandas data‑analysis issue where a user needs to filter stock codes between 10 and 12, explains the type‑comparison error, and provides a corrected solution with code snippets and best‑practice tips for asking Python questions.
This article presents 36 concise data analysis models spanning six key business dimensions—Internet operations, strategy and organization, quality and production, marketing services, financial management, and human resources—to help analysts choose the right method for structured, logical, and effective insights.
This guide answers the most frequent questions about math modeling contests, covering required knowledge, practical skill development, competition preparation tactics, Python and data‑analysis techniques, software choices, and recommended self‑learning resources, while also introducing a dedicated online course.
This article explains the role and mindset of a strategy product manager, outlines the decision‑making framework for content recommendation platforms, compares it with related positions, and answers practical questions about value, AI impact, commercial‑consumer trade‑offs, and content creation versus consumption.
This article explains ten data‑analysis techniques for enhancing user experience—such as behavior tracking, A/B testing, sentiment analysis, and personalization—and provides concrete SQL code snippets that illustrate how to import, query, filter, sort, aggregate, join, update, delete, and back up data in relational databases.
This guide explains how to build a comprehensive operational data analysis framework by adopting macro, business, thinking, and personal perspectives, defining dimensions and metrics, applying structured workflows, and avoiding common pitfalls to develop data sensitivity and drive effective decision‑making.
This guide explains why many enterprises struggle with incomplete metric systems, outlines universal principles, methods, and step‑by‑step procedures—including defining a North Star metric, creating a metric dictionary, and systematic integration—to design effective, dynamic data indicator frameworks that drive informed decision‑making.
This article explains the differences between diagnostic, descriptive, predictive, and prescriptive analytics, shows when each method is useful, and offers guidance on selecting the right analytics solution for modern enterprises dealing with massive data volumes.
This article presents a comprehensive AB testing framework tailored for new‑user growth scenarios, detailing the challenges of early traffic splitting, the design of a scientifically validated experiment system, ID selection criteria, and real‑world case studies that demonstrate improved retention and device activation.
This article introduces five widely used Python libraries—Pandas, NumPy, NLTK, Scikit-learn, and Matplotlib—explaining their core functionalities for data cleaning, statistical analysis, natural language processing, machine‑learning modeling, and visualization, and provides practical code snippets for each.
The article details Taobao’s data‑driven study linking user questionnaire satisfaction to cold‑start times, revealing that startup performance—especially on low‑end Android devices—dominates overall satisfaction, and proposes tier‑based cold‑start targets, a subjective‑objective model, and a monitoring pipeline to achieve a 20% satisfaction boost.
The article explains the importance of data analysis for business insight, problem detection, decision support, operational optimization, forecasting, and competitiveness, and then presents ten practical Python code scenarios covering data loading, cleaning, filtering, aggregation, visualization, statistics, transformation, time‑series analysis, export, and machine‑learning applications.
This article presents a data‑driven approach to predict weekly vegetable sales, determine optimal restocking quantities, and set cost‑plus pricing strategies using time‑series analysis, correlation mining, and profit maximization models for supermarkets.
This article explains how to use PHP to collect, process, and visualize data from sources such as CSV files, then design report templates and export the results as PDFs using libraries like Chart.js, PHPlot, TCPDF, and PHPExcel.
Microsoft has integrated Python directly into Excel, enabling data analysts to run Python code, create visualizations, and apply machine‑learning models within spreadsheets, all powered by Azure and Anaconda, marking a major shift for enterprise data workflows.
This article walks through discovering Bilibili's comment API, programmatically fetching paginated JSON data, converting it into Java POJOs, storing and sorting the comments, and finally feeding the top entries to ChatGPT for automated sentiment and content analysis.
This article explains the fundamentals, principles, methodologies, and practical applications of user profiling, covering core concepts such as user characteristics, behavior, preferences, needs, and value, the data collection-to-model pipeline, common models like RFM, clustering, association rules, text mining, and how these insights enable personalized recommendation, precise marketing, brand management, service optimization, CRM, market research, and product innovation.
This article examines how ChatGPT-4, as an advanced natural‑language‑processing model, can streamline data analysis tasks—from generating Hive table definitions and sample data to crafting complex HiveSQL queries, visualizing results, and implementing ClickHouse and Flink solutions—thereby improving efficiency, insight, and problem‑solving in big‑data environments.
This article outlines a clear framework for writing data analysis reports—including hierarchical structure, concise conclusions, business‑oriented recommendations, reliable data sourcing, and best‑practice chart design—while highlighting common statistical pitfalls and tips for improving readability and impact.
This article uses a Kaggle Pokémon dataset of 802 creatures to explore statistical correlations, build a random‑forest classifier for legendary status, assess type strengths, and apply optimization techniques—including integer linear programming, greedy selection, and simulated annealing—to propose an optimal six‑Pokémon dream team.
This article reviews the evolution and current challenges of modern data analysis, explains why metric‑driven approaches are needed, and details the design, core capabilities, and technical practices of a unified metric platform that enables agile, real‑time insights across diverse data sources.
The article details how iQIYI's Magic Mirror platform evolved from a simple single‑table reporting tool to a multi‑engine, self‑service big data analysis system that improves data access speed, reduces operational costs, and supports comprehensive business analytics across the company.
This guide walks developers through the SQL Bond competition timeline, prize details, registration steps, Hologres database creation, foreign table setup, sample analysis queries, and submission process, providing all essential instructions and code snippets for successful participation.
This article describes Anjuke's R&D efficiency measurement framework, detailing its quality and efficiency metrics across project phases, the data collection and processing architecture, visualization dashboards, and analysis methods used to monitor and improve development productivity, reliability, and continuous delivery.
This article shares practical experience building a payment data analysis system from scratch, explaining why data analysis matters, outlining a five‑stage framework, detailing metric design, and presenting common analytical methods such as funnel, multi‑dimensional, trend, comparison, Pareto, and cross analysis to drive product decisions.
This article explores the practical deployment of ChatGPT‑based products, detailing the model fundamentals, technical architecture, engineering‑focused prompt design, real‑world application scenarios, and the challenges of model generalization, resource consumption, data privacy, interpretability, and ethical considerations.
The article details Taobao’s data‑driven approach to redesigning logistics information display and self‑service tickets—defining problems, preparing subjective and objective data, creating metrics, analyzing pain points, implementing timed soothing messages and proactive tickets, and showing through A/B tests reduced help volume and improved user satisfaction.
OpenAI’s newly released Code Interpreter plugin lets ChatGPT write, run, and test code, offering file handling, a built‑in Python environment with popular libraries, and a secure sandbox, while showcasing ten impressive tricks—from converting GIFs to MP4s to turning data into interactive webpages.
ChatGPT’s new Code Interpreter lets GPT‑4 Plus users upload files, have the model write, run, and debug Python code in a private sandbox, enabling non‑programmers to automate complex tasks such as data analysis, image‑to‑video conversion, and PDF generation through natural language prompts.
This article explains what an indicator system is, how to design and analyze metrics, methods for managing them, and the right mindset for using indicator systems to improve business decision‑making and operational efficiency.
This article reviews a product‑manager’s end‑to‑end workflow for forecasting demand value and validating hypotheses, illustrating how Wallace’s scientific loop translates to business, and detailing causal‑inference techniques such as matching, DID, regression discontinuity, and instrumental variables with a real‑world case study.
This article examines how data can be misused or misinterpreted through real‑world case studies—ranging from breakfast myths and toothpaste advertising to contraceptive risks, crime statistics, judicial decisions, questionnaire bias, airline efficiency, and correlation‑causation confusion—offering practical guidelines to recognize and prevent invalid data analysis in the big‑data era.
Effective R&D efficiency improvement requires moving from manual, scattered, and unstandardized data collection to mature, integrated systems, defining metrics aligned with business goals, and applying a three‑layer framework—quantifiable, explainable, and intervenable—through cleaning, baseline setting, statistical analysis, root‑cause identification, and ROI‑focused action planning to turn numbers into actionable insights.
This article explores whether AI agents such as ChatGPT can help business owners quickly perform data analysis by linking large language models with local MySQL databases via LangChain, presenting the experimental setup, schema creation, sample queries, results, and practical insights.
The paper presents the ‘Post‑Darwin’ evaluation framework, which uses stratified sampling to compare participants and non‑participants across uniform payment layers, overcoming uneven user distributions and the lack of viable A/B tests in game‑business effect analysis, and demonstrates its effectiveness through real‑world promotional and reservation case studies.
This article provides a thorough walkthrough of advanced Pandas techniques—including complex logical queries, data‑type conversion, sorting, adding or modifying rows and columns, sophisticated filtering, iteration methods, and various function‑application utilities—complete with practical code examples for each operation.
This article introduces ten essential Python libraries and frameworks, providing practical examples and code implementations for each to help readers understand and master their applications in various domains.
This tutorial demonstrates how to perform A/B testing on two marketing campaigns using Python, covering data loading, cleaning, merging, and visual analysis of key metrics such as impressions, spend, clicks, searches, content views, add‑to‑cart actions, and purchases to identify the most effective strategy.
The article presents a practical toolbox of statistical and machine‑learning metrics—including short‑term growth rates, CAGR, Excel forecasting functions, Wilson score adjustment, sigmoid decay weighting, correlation coefficients, KL divergence, elbow detection with KneeLocator, entropy‑based weighting, PCA, and TF‑IDF—offering concise formulas and code snippets for data analysis without deep theory.
This article reflects on how the timeless principles of Lean Data Analysis remain vital in the AI era, arguing that the true first metric for startups and society is the value of time spent, and illustrating metric hierarchies that guide data‑driven decision making.
This step‑by‑step tutorial shows how to set up Alibaba Cloud DataWorks and MaxCompute, bind them together, and use free trial resources to explore public big‑data datasets such as Alibaba e‑commerce, Github events, and custom data with SQL queries and visualizations.
In this article, the author shares a Pandas column‑checking solution, presents a concise code snippet that determines whether specific columns exist in each row and assigns scores accordingly, discusses encountered issues with the implementation, and offers practical tips for asking data‑analysis questions in Python communities.
This article explains the concepts of good data metrics, how to identify and select appropriate indicators, and provides a step‑by‑step methodology—including the OSM model and a practical e‑commerce case study—for building a comprehensive data metric system that drives business growth.
This article explains how SQL, built on relational algebra, became the standard analysis language for OLAP engines, covering its history, data models, syntax, functions, aggregation techniques, window functions, subqueries, and practical optimization considerations for modern data warehouses.
This article shows how to use ChatGPT to conduct statistical significance testing for A/B experiments, explains the underlying concepts of Type I and Type II errors, demonstrates a practical “spell” for conversion data, and provides a reliable online calculator for quick results.
This article provides a comprehensive introduction to user profiling, covering its definition, key elements, classification types, common dimensions, practical application scenarios, lifecycle considerations, development workflow, and validation methods for building effective data‑driven user models.
