This article walks through creating a Python‑based Zhihu crawler, visualizing the harvested user data with the ELK stack, and offers practical improvements such as threading, Redis URL storage, and MongoDB persistence for more efficient and scalable web scraping.
This article explains the fundamentals of Geographic Information Science (GIS), recounts John Snow's historic cholera map as an early GIS example, and details how GIS technology can be leveraged to build a road‑logistics platform that enables real‑time monitoring, dispatch, path planning, and data‑driven decision making for transportation companies.
This tutorial guides programmers through installing required Python libraries, extracting keywords from One Piece song lyrics using jieba, and generating a Chopper‑shaped word cloud with the wordcloud package in a Jupyter notebook, complete with code examples and visual results.
The 2018 roundup showcases ten standout data‑visualization projects—from a Spotify‑driven map of Bruce Springsteen’s songs and a real‑time solar‑lunar app, to Game of Thrones dialogue analytics, interactive world‑book maps, Apollo mission prints, child‑friendly Earth bios, mascara‑choice charts, Marvel cinematic networks, ISS construction timelines, and a poster‑ready Jupiter moons diagram.
This article provides a comprehensive guide to Lasso regression, covering its theoretical background, scikit-learn API parameters, step‑by‑step Python implementation, cross‑validation for hyper‑parameter tuning, visualization of predictions, and a discussion of its advantages over ridge regression.
This article introduces the open‑source BI platform Redash, outlines its wide‑range data source support and visualization capabilities, and provides step‑by‑step Docker‑Compose installation instructions along with practical usage tips for building dashboards.
On September 15, Youzan’s big-data team and NetEase YouShu hosted a technical sharing titled “The Road to Data-Driven Operations,” where speakers explored the evolution of Youzan’s data warehouse metadata system, the architecture of its big-data development platform, and the application of functional programming in visual data analysis, highlighting current trends and future directions.
The article outlines a comprehensive data‑analyst competency framework, covering data collection, storage, extraction, mining, analysis, visualization, and practical application, and provides concrete questions, techniques, and tool recommendations to help analysts turn raw data into actionable business insights.
This article explores the concept of large‑screen displays as a powerful medium for sharing information, outlines three main types of big‑screen designs, compares data screens with traditional reports, and provides a step‑by‑step guide—including information extraction, chart selection, visual refinement, and emphasis techniques—to create clear, focused, and effective data visualizations.
This article explains why BI is essential for big data platforms, outlines the value hierarchy of data, details the Hadoop‑based analysis workflow, and provides step‑by‑step guidance for constructing both pure Hadoop and hybrid Hadoop‑Spark analytics architectures.
This article explains why monitoring page performance is crucial for user experience and SEO, outlines the design of a three‑part monitoring system—including front‑end data reporting via Navigation Timing, server‑side log collection with Nginx, data aggregation, sampling, storage, and visual dashboards—providing a complete end‑to‑end solution.
The article shares Wang Baoping’s (Yu Bo) reflections on the essence of frontend technology, the rise of BFF architecture, the convergence with mobile, IoT and experience tech, and outlines future directions such as TWA, UI intelligence, data visualization, and natural‑virtual interaction.
This beginner-friendly tutorial walks you through setting up Matplotlib, installing the library, and creating a variety of basic visualizations—including line plots, scatter plots, pie charts, bar charts, and histograms—while also covering multiple figures, subplots, and customization options for each chart type.
Meituan’s in‑store dining team demonstrates how R’s open‑source packages, powerful data manipulation, rich visualization libraries, and reproducible reporting can be engineered into scalable, parallelized workflows that turn secondary data processing into fast, interactive dashboards and analytics, proving R’s enterprise‑grade performance and adoption.
The G2 3.2 release from AntV introduces a richer visual cognition system with tail‑legend support, region‑filter color guides, new data marker guides, more flexible component configurations, and chart‑level theme customization, while providing installation options via npm or CDN for developers to try the latest features.
This tutorial introduces Matplotlib, a powerful Python 2D plotting library, covering installation, basic usage, multiple figure handling, subplots, and common chart types such as line, scatter, pie, bar, and histogram with clear code examples and visual illustrations.
SpriteJS is an open‑source, cross‑platform JavaScript library that simplifies canvas drawing by providing a DOM‑like API for creating sprites, paths, groups, and animations, integrating seamlessly with tools such as D3, Matter‑JS, Proton, and CurveJS for rich interactive visualizations.
SpriteJS is an open‑source, cross‑terminal canvas library from 360 Qiyou that provides a DOM‑like API for drawing shapes, creating sprites, animating with transitions or the Web Animation API, handling textures, vectors, groups, events, and integrating with D3, physics engines and particle systems.
This tutorial shows how to use Python's itchat library to extract your WeChat contacts' province and city information, analyze the distribution, and create visual maps, providing step‑by‑step guidance and sample screenshots for each stage.
This tutorial walks you through extracting WeChat Moments with a Python web scraper, processing the Chinese text using jieba, and visualizing the most frequent words as beautiful word clouds with customizable shapes and fonts.
The Suning Front‑End Technology Forum in May 2018 gathered over 500 engineers to share insights on Node.js performance tuning, React 16's new features, WebRTC for online education, WebAssembly‑based optimization, and dashboard data‑visualization techniques, offering a comprehensive snapshot of modern front‑end advancements.
This tutorial demonstrates how to collect Python job postings from Lagou using Python's requests library, process the JSON response with pandas, and create insightful visualizations—including bar charts, word clouds, and geographic heatmaps—while handling anti‑scraping measures and data cleaning steps.
Using a self‑built C# scraper, ETL tools, and Python's pandas, matplotlib, and seaborn, this analysis visualizes Beijing’s residential real‑estate data from 1992 to 2015, highlights price spikes, maps hot‑spot districts with Baidu maps, and quantifies the 5 % average rise between 2014 and 2015.
This article compares Grafana and Kibana/Timelion, explaining their data source support, query syntax, visualization capabilities, and user interaction features to help developers choose the most suitable tool for real‑time time‑series monitoring and analysis.
Using Python web scraping, jieba segmentation, and word‑cloud visualization, this article analyzes Douban comments for the three iconic anime series One Piece, Naruto, and Bleach, highlighting dominant themes, character popularity, and audience sentiment through keyword frequency maps.
This article explains why web interfaces are optimal for displaying large‑scale B2B statistical data, outlines methods to organize information hierarchically, choose appropriate visualizations, and implement flexible table controls such as modular tabs, layering, filtering, sorting, and pagination to improve usability.
This guide walks you through importing stock data, cleaning column names, visualizing price and volume trends, creating candlestick (K‑line) charts, analyzing relative changes, exploring correlations, and implementing a simple moving‑average trading strategy using pandas, matplotlib, and numpy.
In just over a month, 37 Interactive Entertainment transformed its fragmented monitoring wall into a unified, twelve‑screen dashboard by consolidating game and service data into Elasticsearch, creating a single API, and employing modular JavaScript, custom ECharts visualizations and a 3D map, delivering real‑time insights with a cohesive sci‑fi inspired UI.
The article explains that industrial AI needs specialists who can apply machine‑learning models to specific domains, outlines essential fundamentals such as regression, classification, neural networks, data visualization, and unsupervised learning, and offers practical career advice for students and early‑career professionals seeking to transition into machine‑learning roles.
This tutorial explains how to create rational, layered choropleth maps using Python libraries like Pandas together with Vega and GeoJSON, covering data preparation, FIPS mapping, and generating visualizations for population density, income, and unemployment rates.
This tutorial demonstrates how to set up a Flask web application that uploads CSV files and visualizes machine‑learning datasets using Google Facets Overview and Dive components, providing step‑by‑step code, configuration, and examples of data insights and potential use cases.
This article walks through essential Matplotlib techniques—including creating scatter plots, applying polynomial fitting, generating multiple subplots with the Iris dataset, drawing reference lines, and enabling interactive updates—providing concise code snippets and visual examples for each step.
An overview of popular open‑source Linux data‑visualization applications—Gnuplot, GNU Octave, Scilab, MayaVi, and Maxima—examining each tool’s licensing, capabilities, scripting interfaces, 2‑D/3‑D plotting features, and typical use‑cases to help readers choose the most suitable solution for their computational tasks.
This tutorial guides readers with basic programming experience through using Python 3 in Jupyter to extract keywords from One Piece lyrics with jieba, and then generate a Chopper‑shaped word cloud using matplotlib and the wordcloud library, covering required dependencies and step‑by‑step code.
This guide shows how to set up a Windows 10 Python 3.6 environment, generate random numbers using NumPy’s various methods, and visualize the data with Matplotlib through eight chart types—including scatter, line, bar, histogram, pie, and box plots—complete with code snippets and images.
This article provides a comprehensive overview of 56 data visualization tools ranging from simple spreadsheet options to advanced JavaScript libraries, mapping frameworks, and statistical packages, helping developers choose the right solution for creating interactive and insightful visual representations of their data.
This tutorial demonstrates how to capture hot comments from NetEase Cloud Music using web‑scraping techniques, handle the platform's encrypted API, and generate a Chinese word cloud with Python's WordCloud library for visual insight.
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.
The article outlines the key competencies, responsibilities, and career development advice for data professionals across the entire data stack—from building big‑data platforms and data warehouses to visualization, analysis, algorithm engineering, and deep‑learning applications—emphasizing the importance of creating business value with data.
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.
This tutorial walks through setting up a Python 2.7 machine learning environment with scikit-learn, installing required libraries, loading web traffic data, cleaning NaN entries, visualizing the data, performing a linear regression using SciPy's polyfit, and evaluating the model's fit.
This article introduces Baidu's open‑source ECharts library, walks through installing the JavaScript package, embedding it in a web page, and demonstrates using the Python ECharts API to create interactive charts, while highlighting key setup notes and resources.
The article critiques popular but unnecessary log‑analysis features—such as sub‑second alerts, endless pagination, flashy maps, full SQL support, bulk downloads, and live tail—arguing that focusing on practical alert content, efficient querying, and proper architecture yields far more value for IT operations.
This guide explains how operations engineers can systematically map business architecture, set up comprehensive multi‑dimensional monitoring, and consolidate monitoring data into dashboards to gain full visibility and quickly resolve issues in large‑scale online services.
This article introduces G2, an open‑source JavaScript visualization library from Ant Financial, explains its semantic charting capabilities and flexible graphic grammar, and provides a complete HTML example that creates a bar chart from sample data, guiding developers to quickly build custom visualizations.
This article explains how Uber built a full‑stack data‑visualization team that uses open‑source React and WebGL libraries to turn massive GPS streams into real‑time map analytics, public data stories, and reusable visual components for internal and external users.
This article explains how to transform massive security‑related big data into clear visual insights, covering storytelling, data processing, visual encoding, design workflow, and two real‑world case studies that illustrate vulnerability mapping and internal traffic analysis for improved threat awareness.
This article analyzes how finance, health, and business mobile apps adapt classic chart types for limited screen space, highlighting design choices such as color usage, compact layouts, and interactive filters that improve readability and user decision‑making.
This article explores how Baidu Map’s open platform leverages web‑based visualization libraries such as ECharts, MapV, Canvas, SVG and WebGL to render point, line and polygon geospatial data—including heatmaps, clustering, and 3D charts—while addressing performance challenges of large‑scale datasets and outlining the front‑end technologies used.
This guide outlines a comprehensive, step‑by‑step Python learning path for aspiring data scientists, covering environment setup, core language fundamentals, regular expressions, scientific libraries such as NumPy, SciPy, Matplotlib, Pandas, data visualization, machine‑learning with scikit‑learn, and an introduction to deep learning, with curated resources and practice projects.
