This article demonstrates how to use the Python itchat library to extract personal WeChat friend data, then analyzes gender ratios, city distribution, and signature word clouds with R and Python visualisation tools, offering a practical guide for personal social network analytics.
A data‑driven investigation of Paris’s metro and RER systems reveals which lines and stations suffer the most delays, cancellations, and incidents, and uncovers the primary causes behind these chronic problems.
This tutorial walks through using Python and pandas to fetch Yahoo Finance data, visualize stock prices with line and candlestick charts, and apply moving‑average techniques—including 20‑day, 50‑day, and 200‑day averages—to identify trends and build simple trading signals, all while emphasizing that the content is for educational purposes only and not investment advice.
Analyzing over 3.2 million publicly available Zhihu profiles collected via a distributed Python crawler, this report uncovers gender balance near 1:1, top residential cities, dominant occupations, university participation, and the most followed and active contributors, while noting data limitations and temporal relevance.
This article walks through using Python to collect thousands of Weibo comments about Xue Zhi Qian's response, perform text segmentation, remove stop words, and generate word‑cloud visualizations that reveal shifts in public sentiment before and after the reply.
This article explains how operation designers can navigate abundant data like PV, UV, and conversion rates by defining user behavior paths, combining key metric ratios, applying funnel analysis, and distinguishing dimensions from measures to validate and improve product performance.
This article explains why designers need product thinking and breaks down the five key mindsets—user‑centered, logical, data‑driven, marketing, and project thinking—providing practical examples and actionable steps to boost influence, career growth, and design impact.
Using Python, Scrapy, pandas and seaborn, this article crawls public Shanbay user data, cleans and stores it in PostgreSQL, then analyses retention, growth and word‑learning distributions, revealing that the overwhelming majority of learners abandon the app within a few days.
This article explains what statistics entails, outlines its core tools and methods—including data collection, processing, analysis, and visualization—describes essential software and programming skills, and explores career routes for statisticians and data analysts in both technical and business roles.
This guide introduces a Python tool that automates the creation of descriptive and cross‑analysis survey reports in PPT and Excel formats, covering installation, data preparation, quick‑start code snippets, and additional utility functions for comprehensive statistical reporting.
This guide walks beginners through the vast big data landscape, helping them choose the right role, understand essential terminology, plan a learning path, and access curated resources for becoming a data engineer or analyst, all illustrated with clear diagrams.
This tutorial demonstrates how to use the Python itchat library to log into WeChat, analyze friend demographics, generate signature word clouds, and build an automatic reply bot, providing step‑by‑step code, required packages, and visual results.
This article explores how big data analysis can determine the demand, optimal placement, and profitability of beverage vending machines in Beijing subway stations, detailing passenger flow statistics, gender ratios, consumption patterns, and revenue projections to illustrate data‑driven operational strategies.
This article explains the differences between pandas' apply, applymap, and map functions, then demonstrates how to perform principal component analysis on Germany's DAX30 index to identify key stocks, visualize contributions, and achieve effective dimensionality reduction.
This tutorial shows how ordinary users can replace spreadsheet work by using Linux command‑line utilities such as head, tail, wc, grep, tr, sort, sed, cut, uniq and awk to quickly explore, filter, transform and summarize CSV/TSV data sets.
This article explains a six‑layer data analysis framework—from raw data sources and data warehouses through exploration, mining, and visualization—while also reviewing common analysis tools such as R, SAS, SPSS, and describing typical statistical techniques and presentation methods.
This article outlines the key competencies and career roadmap for data professionals, covering big‑data infrastructure, data‑warehouse engineering, visualization, analysis, algorithmic mining, and deep‑learning, while emphasizing the importance of business sense, cloud adoption, and continuous learning.
A diverse group of practitioners share recommendations for books, technologies, real‑world use cases, and practical challenges when learning and applying big‑data processing, covering Hadoop, Spark, data visualization, ETL, and the relationship between data, algorithms, and business value.
The article explains how Ctrip’s flight‑booking team uses AB testing—from definition and experimental design to data collection, traffic allocation, orthogonal experiments, and result analysis—to drive conversion‑rate and revenue improvements across multiple platforms.
This article introduces the AWK scripting language, covering its basic syntax, command‑line options, script components such as BEGIN and END blocks, and demonstrates common text‑filtering, data‑analysis, and formatting tasks with concrete examples.
This article examines the core technologies of big data—including data collection, storage, management, analysis, and mining—highlighting architectural challenges, analysis techniques, storage solutions, ETL processes, and the interplay between big data and cloud computing, while emphasizing practical implementation considerations.
This article reviews a comprehensive big‑data lecture covering traditional databases, Hadoop ecosystems, commercial big‑data platforms, computing models, analysis techniques, visualization, and leading vendors, highlighting how these technologies shape today’s data‑driven enterprises.
This guide walks through essential Linux shell commands—from basic file viewing and simple statistics to powerful exploratory analysis tools—showing how data engineers can efficiently inspect, process, and batch‑handle logs and other data files using the command line.
This article investigates whether the 13‑digit user IDs used in Chinese car‑license lotteries are truly random, revealing how the ID generation, seed‑based selection, and hidden patterns—especially the influential seventh digit—affect outcomes, and demonstrates that simple linear models can achieve an AUC of around 0.8 in predicting winners, while also discussing the system’s opacity across major cities.
This article explains how comprehensive user profiling—combining static demographics and dynamic behavior logs—feeds recommendation engines, detailing data sources, feature extraction, ranking formulas, and the long‑term goals of delivering personalized, high‑quality content to users.
This article explains how to build a comprehensive mobile app quality monitoring framework by defining quality standards, setting capability indicators, and leveraging data acquisition, analysis, and visualization to continuously improve product reliability and user experience across different development stages.
The article explains how internet products use data analysis—covering industry traits, retention curves, magic moments, and extensive A/B testing—to drive user growth, evaluate experiments, and align product improvements with measurable metrics.
This article introduces dataplay2, an open‑source self‑service BI platform built with Flask, pandas, scikit‑learn on the backend and React, ECharts, D3, and other JavaScript libraries on the frontend, detailing its architecture, installation steps, core features such as data upload, visualization, classification, clustering, and future improvement ideas.
This article details Baidu Doctor’s comprehensive testing and monitoring strategies, covering user experience data analysis, source data trust, online monitoring systems, log‑based automated checks, retrieval backend testing, evaluation metrics, Badcase mining, and user search habit analysis to ensure high‑quality medical O2O services.
This article introduces Splunk as a specialized IT search engine for big data, detailing its data collection, indexing, analysis, reporting, security integration, scalability, and practical use cases such as operational troubleshooting, compliance auditing, and application performance monitoring.
The article examines what big data really means, its core 4 V characteristics, current limitations in China, the overhyped value of data, the importance of business‑driven applications, and why starting from small, relevant data is essential for true predictive power.
The article explains why operators should start from clear goals rather than events, defines meaningful metrics such as user retention and API call volume, shows how to break down and evaluate these metrics, and offers practical advice on data collection, benchmarking, and continuous improvement.
The article examines the shortcomings of visual programming tools for data scientists, highlighting issues such as poor version control, limited IDE flexibility, lack of open‑source integration, and reduced modularity, and argues that code‑first approaches remain more effective in enterprise environments.
This article introduces the GWPY Python package for analyzing LIGO gravitational‑wave data, explains the physics of gravitational waves and LIGO, shows how to install and use the library with object‑oriented examples, and demonstrates data visualization through complete code snippets.
This article explains what product evaluation is, why it matters, and outlines a step‑by‑step process—including defining goals, setting standards, selecting methods, executing tests, analyzing results, and presenting findings—to ensure both functional and experiential quality of products.
The article reveals the inner workings of Facebook’s Growth Team, detailing its data‑driven culture, organizational structure, key leaders, and engineering‑focused strategies that have propelled the platform to billions of active users, offering a rare behind‑the‑scenes look.
The article outlines data science as an interdisciplinary field centered on three key steps—pre‑processing, interpretation, and modeling—while providing concise recommendations of foundational books for R, Python, exploratory analysis, machine learning, and essential tools to guide practitioners.
Aspiring data specialists should cultivate curiosity and hands‑on experience with production‑grade tools, and this guide highlights seven indispensable Python libraries—IPython, GraphLab Create, pandas, PuLP, matplotlib, scikit‑learn, and Spark—each explained with key features to boost your data‑science career.
This article presents a PHP‑based web‑scraping experiment that collects and visualizes several months of data from the “Da Gai Er” board of the Caoliu forum, revealing user activity patterns, image hosting distribution, registration trends, and overall forum health through charts and statistical summaries.
This article recounts a recent cross‑border shopping user research project, detailing preparation, requirement gathering, timeline planning, execution, data analysis, report writing, and final sharing, while highlighting practical insights and best practices for effective research and project management.
This article demystifies big data by exposing common misconceptions about its sheer volume, critiquing industry definitions, and highlighting how value emerges from thoughtful analysis, dynamic correlations, and user‑centric perspectives rather than raw data size alone.
The October 2014 100offer programmer auction data reveals which developer skills, locations, and experience levels attract the most interview invitations and highest salary offers, highlighting trends such as higher demand for mobile and web front‑end engineers, the impact of salary increase ranges, and common reasons candidates reject offers.
This guide explains how to conduct comprehensive product data analysis for games, covering methods like 5W2H, multi‑dimensional analysis, user modeling, and the AARRR framework, while detailing key metrics such as DAU, ARPU, LTV, CAC and ROI to drive informed decisions.
At Baidu’s Technology Salon #53 on August 16, a near‑full audience heard data scientist Shen Zhiyong explain the origins of Baidu’s tourist‑flow prediction tool and Liu Ying discuss the evolution of social‑economic forecasting from classic “beer‑and‑diapers” models to modern big‑data analytics, followed by an interactive Q&A and prize‑giving session.
The article argues that predictability—evident in Baidu’s Gaokao essay forecasts and formulaic Hollywood blockbusters—arises when markets favor safe, widely appealing content, making simple systems like baseball easy to predict while complex systems with feedback loops resist accurate forecasting despite apparent creativity.
This guide explains how interaction designers can develop data‑analysis skills—from collecting and understanding data sources to applying analytical methods and communicating results—to make evidence‑based design decisions and improve product outcomes.
This guide explains how interaction designers can systematically collect, analyze, and apply data—clarifying purpose, understanding sources, using core analytical methods, communicating results, and avoiding common pitfalls—to make evidence‑driven design decisions.
