This tutorial walks through a complete Python workflow for analyzing a month of Taobao mobile app data, covering data import, cleaning, feature engineering, traffic and behavior metrics, funnel conversion, RFM segmentation, and visualizations to reveal user patterns and business performance.
This article explains what an IoT platform is, how it fits into a complete IoT system, the functions it provides such as connectivity, protocol handling, security, data collection and analysis, and offers guidance on when businesses should adopt one despite cost trade‑offs.
Designers often overlook data analysis, yet mastering it helps identify problems, set measurable goals, generate hypotheses, collect and interpret metrics, and draw actionable conclusions, ultimately guiding product decisions and improving conversion rates across various scenarios such as page performance, financial calculations, and insurance plan selection.
This article explains why many static data reports are ineffective, identifies the lack of judgment standards as the core issue, and offers three practical ways—problem‑based, goal‑based, and business‑based—to establish meaningful standards that make data analysis reports valuable.
This article uses a vivid kite‑selling story to illustrate how user profiling, data tagging, and recommendation tactics can be combined to increase transaction volume, improve average order value, and avoid common pitfalls such as unclear goals, poor data quality, and unvalidated tags.
This article explains how the Nanomirror data analysis platform introduces intelligent slice analysis—both activity‑metric and AB‑bucket slice analyses—to uncover the most impactful user segments, guide targeted interventions, and improve operational efficiency for Xianyu’s online campaigns.
This article describes how to collect, process, and visualize Git commit data—including hashes, authors, dates, messages, and line changes—by using native git commands, the libgit2sharp .NET library, a custom cross‑platform CLI tool, and PowerBI dashboards for comprehensive reporting.
This article explains how to replace cumbersome Python‑Plotly visualizations of performance test logs with a lightweight Groovy solution that generates plain‑text bar charts directly in the shell or email, using Unicode block characters and a bucket‑based median algorithm.
This guide outlines the essential Python knowledge and libraries needed to automate office tasks such as Excel, PowerPoint, Word, email, batch file handling, data analysis, and web scraping, providing practical resources and examples for non‑IT professionals.
The February 12, 2020 COVID‑19 daily report details a sharp rise in national confirmed cases to 59,804, explains the inclusion of clinically diagnosed cases in Hubei, presents provincial death and cure rates, and offers extensive data‑driven analyses of trends, infection coefficients, and regional transmission dynamics.
This guide introduces pandas, the essential Python library for data science, covering installation, data import/export, basic DataFrame operations, logical filtering, visualization with matplotlib, performance tips using tqdm, and advanced techniques like merging, grouping, and iterating, helping beginners become efficient data analysts.
This article investigates whether a short‑term time‑series algorithm, specifically an LSTM model trained on limited SARS data, can predict and assess COVID‑19 case numbers, describing data collection, model training, experimental validation, error analysis, and practical implications of the findings.
This tutorial walks data analysts through a side‑by‑side comparison of common data‑manipulation tasks using pandas in Python and SQL, covering everything from basic selects and filters to joins, aggregations, unions, ordering, case expressions, and data updates with clear code examples.
The article argues that programmers who only code are becoming replaceable, emphasizing that mastering real-world business processes, data-driven decision making, and continuous curiosity is essential for engineers to remain valuable and drive meaningful product outcomes.
This article explains why measurement is essential for management, outlines a step‑by‑step process for collecting and analyzing efficiency metrics, and shows how to turn data‑driven insights into concrete conclusions and actionable improvement plans for software projects.
A comprehensive collection of 70 NumPy exercises with detailed problem statements, code snippets, and expected outputs that cover array creation, manipulation, statistical analysis, handling missing data, encoding, sorting, and advanced mathematical operations for Python developers.
This article walks through downloading World Bank GDP and growth‑rate datasets, explores income‑group classifications, visualizes top‑ranking economies, maps worldwide GDP distribution, and compares growth trends across countries using Python and data‑visualisation techniques.
This tutorial walks you through using Python to crawl Tmall lingerie product and comment data, store it in MySQL, clean and analyze cup‑size distribution and popular colors, and finally visualize the results with pyecharts.
Meituan Homestay created a platform‑wide Jupyter service built on JupyterHub and Kubernetes that integrates Spark, scheduling, documentation and storage, providing seamless, reproducible notebooks with custom extensions, magics and container isolation to unify data analysis, model training and production workflows.
This article explains how to scrape Chinese Basketball Association (CBA) match data from a portal website, analyzes the page structure, extracts table rows using regular expressions, converts them to CSV format, and provides a complete Java/Groovy code example for automated data collection.
This tutorial walks you through using Python to collect tourism data from Qunar, extract key fields such as name, price, and rating, store the results in Excel with pandas, and visualize sales and popularity trends using pyecharts, including a simple recommendation algorithm.
This article provides an extensive, English‑language overview of statistical software—including open‑source, public‑domain, freeware, and proprietary tools—detailing their primary functions, programming languages, and typical application areas for statistical and econometric analysis.
This guide walks you through installing Pandas, understanding its core data structures like Series, DataFrame, and Panel, performing data access, adding or removing rows and columns, filtering datasets, and applying these techniques to a real‑world stock selection workflow using TuShare and TALib.
The article introduces Snowball's AIBO data middle platform, detailing its storage‑compute separation architecture, core capabilities such as data integration, catalog, tagging, analysis tools, micro‑service data APIs, and outlines future enhancements for security, lineage, and continuous business‑driven iteration.
This tutorial walks you through using Python and Pandas to import Excel files, explore DataFrames, apply filters, perform statistical calculations, create pivot tables, and emulate Excel functions like VLOOKUP, providing code examples and visual guides for each step.
This article explains how big data enhances machine learning by enabling finer-grained data characterization, improving confidence in statistical conclusions, and supporting smarter learning through multiple stages of model development, illustrated with concrete examples and a discussion of sample size dilemmas.
Learn how to install Jupyter via Anaconda or pip, create and manage notebooks, understand cells and kernels, write and run Python code, explore a Fortune 500 dataset with pandas, clean missing values, and visualize profit and revenue trends using matplotlib and seaborn—all illustrated with screenshots and code snippets.
This guide explores advanced Jupyter Notebook techniques, including using shell commands, line and cell magic commands, autosave configuration, timing execution, logging customization, running external scripts, integrating Seaborn for enhanced visualizations, managing databases with ipython-sql, and extending functionality with plugins.
This tutorial walks you through discovering a real‑time earthquake data source, analyzing its pagination API, building a Python scraper with requests, parsing the JSON responses, storing the results in CSV, and performing visual analyses such as top‑magnitude locations, monthly frequency, repeat‑site counts, magnitude distribution, and word‑cloud generation.
This article demonstrates how to collect, process, and visualize thousands of user reviews for the Chinese animated film "Nezha" from Maoyan, covering dynamic data extraction, JSON parsing, and insightful visual analysis with Python libraries.
This article provides an overview of Python, covering its origin, historical releases, key features, major application domains such as web development, AI and data analysis, various interpreters, and development guidelines like PEP 8.
After quitting my job in Shanghai, I went through eleven technical interviews ranging from Python full‑stack to data‑analysis roles, sharing detailed experiences, resume strategies, scheduling tactics, interview outcomes, key takeaways, and a curated list of frequently asked interview questions.
The article details a contestant's end‑to‑end approach for an advertising competition, covering data analysis, rule‑based preprocessing, a three‑layer neural network architecture, model‑rule ensemble weighting, self‑correction strategies for the B phase, and final model‑only solutions that achieved top scores.
This article explains the fundamentals of AB testing, outlines the iterative workflow, shares best‑practice guidelines, compares single‑layer and multi‑layer experiment frameworks, and details the technical implementation—including SDK design, hashing algorithms, data denoising, and statistical evaluation methods.
This article explains fundamental data‑analysis methods, introduces the AARRR user‑growth model with key metrics for each stage, and presents practical tools such as user path analysis, funnel conversion, heatmaps, and A/B testing to help product teams make data‑driven decisions and continuously improve user experience.
This article demonstrates how to download the Google 1‑gram dataset, load and filter billions of rows with the PyTubes library, compute yearly word frequencies using NumPy, and reproduce the classic Python usage trend chart while discussing performance considerations and future improvements.
In a playful data‑driven experiment, the author scraped 27,664 Zhihu answers to “What are your dating criteria?”, filtered out short, outdated, high‑profile or already‑matched posts, applied follower‑and engagement‑thresholds to narrow the pool to 480 candidates, then ranked the top 30 by a like‑to‑comment ratio, sharing the code and dataset for reproducibility.
This article walks through a complete data‑analysis workflow—scraping Zhihu dating‑preference answers, cleaning and filtering the data, deriving gender and activity metrics, designing a four‑step screening process, and finally ranking candidates with a custom like‑to‑comment index—to help a single programmer create a concise, high‑quality list of potential partners.
This article dissects the 2019 Spring Festival (春运) campaign by 58.com, revealing how a win‑win design mindset, data‑driven insights, and integrated business collaboration transformed user experience, increased traffic, and delivered measurable results across multiple channels and game‑based interactions.
This guide provides a concise review of fundamental database concepts—including definitions of databases, RDBMS, SQL, and tables—and walks through essential SQL query techniques, data manipulation commands, schema definition statements, and control language basics, offering practical examples for data analysts.
An in‑depth data analysis of the 996.ICU GitHub repository shows where the protesting programmers work, which cities they live in, their typical GitHub activity, the most discussed issues, and the common keywords in their bios, highlighting the scale of the anti‑996 movement.
This tutorial shows how to collect book metadata such as publisher, publication date, ISBN, price, rating and review count from Douban for a list of titles stored in Excel, using Python requests, lxml XPath parsing, pandas for merging and analysis, and visualizing the results with matplotlib.
This article introduces Python’s core data‑analysis stack—NumPy for fast multidimensional arrays, pandas for labeled DataFrames, and Matplotlib for interactive plotting—while showing how to set up a Jupyter/VS Code environment, perform basic indexing, slicing, and visualisation, and clean log files with pandas.
This article presents a detailed infographic comparison of R and Python from a data‑science perspective, outlining their histories, ecosystems, usability, community support, and advantages in data analysis to help readers decide which language better fits their projects.
Using Python’s Scrapy, pandas, and seaborn, the author scraped and cleaned public Shanbay user data, stored it in PostgreSQL, and analyzed registration and study habits to reveal that over 68% of users abandon word‑learning on day one, with only a tiny fraction persisting beyond 100 days.
The podcast examines AI’s rapid integration into entertainment, security and personalization, highlighting its use in automated video editing, facial-recognition tagging of celebrities and non-celebrities, while debating ethical concerns such as echo-chambers, emotional nuance, and the technology’s transformative yet limited role across industries.
AWK is a versatile text-processing language that reads files line by line, splits fields by default spaces, and applies pattern-action rules to analyze data, generate reports, and perform complex tasks such as extracting columns, counting records, and scripting with built-in variables, loops, and conditional statements.
This tutorial demonstrates how to employ Python's itchat library to log into WeChat, retrieve the total number of contacts, analyze gender distribution, and visualize the results, providing step‑by‑step code, screenshots, and tips for verifying the data against the official app.
This article examines frequent traps when using NumPy for matrix operations in machine learning, comparing its quirks to MATLAB/Octave and offering practical insights to prevent shape errors, inefficient indexing, confusing syntax, and unintuitive code patterns.
This article crawls Lagou.com to collect 4,500 Python‑related job postings across ten roles, extracts salary, education, experience and skill requirements, visualizes the data with treemaps, rose charts, bar charts and word clouds, and provides detailed insights into each position’s market demands and compensation trends.
These 20 free and open‑source data visualization tools—from JavaScript libraries like D3.js and Chartist.js to user‑friendly platforms such as Datawrapper, Google Data Studio, and Tableau Public—enable businesses and analysts to transform raw data into interactive charts, maps, timelines, and dashboards, improving insight, decision‑making, and profitability.
This article demonstrates how to crawl Qunar’s nationwide tourism listings with Python, extract key fields such as name, level, location and price, handle anti‑scraping measures, and then perform comprehensive data analysis and visualisation—including sales rankings, popularity scores, geographic distribution and geocoding using the Amap API.
The 2018 China Open‑Source Annual Report analyzes GitHub data, introduces the Grank health index, highlights dominant companies and trending technologies such as AI, cloud, and blockchain, and reveals how activity and community decentralisation shape the vitality of Chinese open‑source projects.
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.
The article shares a veteran programmer’s journey from facing a mid‑career tech crisis to leveraging data analysis and soft‑skill expertise, illustrating how shifting focus from pure coding to holistic project and business insight can open new management opportunities and sustain relevance in the evolving tech industry.
This article outlines the five fundamental aspects of big data analysis—visualization, data‑mining algorithms, predictive analytics, semantic engines, and data quality management—and explains four primary analytical approaches: descriptive, diagnostic, predictive, and prescriptive analysis.
The article explains how a data analyst can use statistical and machine‑learning models such as linear regression, tree‑based boosting, STL decomposition, and Prophet for both non‑time‑series forecasting and time‑series monitoring, highlighting data‑quality concerns, feature‑engineering practices, and deployment considerations like PMML packaging.
This article analyzes 18 million PUBG match records using Python to reveal optimal drop locations, high‑probability final‑circle spots, preferred weapons, and the relationship between kill distance, kill count, and winning chances, providing data‑driven strategies for players seeking more chicken dinners.
Using Python to scrape over ten thousand Maoyan comments for the comedy film “The Billionaire” (西虹市首富), this article demonstrates data cleaning, geographic heat‑maps, city‑wise rating analysis, word‑cloud generation, and a simple box‑office forecast based on a comparable movie, illustrating practical web‑scraping and data‑mining techniques.
This tutorial introduces Pandas fundamentals, covering installation, data structures, importing CSV files, inspecting and reshaping data, filtering with boolean masks, indexing, applying functions, grouping, merging, quick plotting, and saving results, all illustrated with clear examples and images.
This report compiles publicly released Chinese government data on second‑class vaccine procurement across 28 provinces, cleans it into a unified CSV format, and uses pandas and echart to reveal a highly skewed distribution dominated by a few large manufacturers while noting data gaps and collection challenges.
This article documents the collection, cleaning, and statistical analysis of publicly released second‑category vaccine procurement data from 28 Chinese provinces, highlighting data sources, processing steps with pandas, top manufacturers, regional market shares, and the challenges encountered during the effort.
This article documents the collection of public second‑class vaccine procurement data from 28 Chinese provinces, describes the CSV schema, outlines the challenges faced during web scraping, and presents a pandas‑driven statistical analysis that highlights top manufacturers and their provincial market shares.
The study aggregates and cleans 2017‑2020 Chinese second‑class vaccine procurement data from 28 provinces into a 1,529‑record CSV, revealing a right‑skewed distribution where a handful of manufacturers—led by Beijing Kexing and Changchun Changsheng—account for the majority of entries, while noting gaps in several regions and encouraging further collaborative refinement.
Using real‑time order and fuel‑point data from the Didi driver app, the author demonstrates a systematic, data‑driven approach to identify the most cost‑effective car models for ride‑hailing across major Chinese cities, complete with methodology, analysis, and city‑specific rankings.
The article argues that programmers need to deeply understand business concepts and data-driven decision making, explaining what business entails, why it matters for engineers, and offering practical methods to acquire business knowledge for more impactful, sustainable tech solutions.
This article demonstrates how to use Python, pandas, and Jupyter Notebook to explore a comprehensive World Cup dataset, clean and enrich the data, visualize win and goal statistics for all teams, and finally predict the top three contenders for the 2018 tournament.
This article demonstrates how to collect historical football data, perform exploratory analysis and feature engineering, and apply a logistic‑regression model in Python to predict the 2018 FIFA World Cup champion, group‑stage results, and knockout‑stage outcomes.
This tutorial shows how to employ Python's itchat library to log into WeChat, retrieve the total number of friends, and analyze their gender distribution, providing step‑by‑step code, screenshots of results, and tips for verifying the data.
By applying a PDCA‑based quality‑operation framework that aligns QA KPIs, drills defect data across dimensions, automates test‑gate checks, and drives continuous improvement actions, Meituan Dianping’s Intelligent Payment team reduced severe defect ratios, met defined metric targets, and boosted iteration efficiency while supporting rapid business growth.
The article explains how QA engineers can systematically measure, analyze, and resolve app slowness by quantifying response times, dissecting device fragmentation, network latency, and backend complexity, and establishing a data‑driven quality loop to improve user experience.
This article explains the fundamentals of A/B testing, illustrates its evolutionary analogy with Darwin's finches, highlights typical mistakes such as limited variants and improper sample division, and provides a step‑by‑step guide for designing rigorous experiments to drive product growth.
This article details Chain Home's OLAP platform architecture and Kylin usage, covering the evolution from early ROLAP to MOLAP multi-dimensional engine, Kylin's basic principles, platform structure, application scenarios, usage specifications, capability extensions, and middleware development.
This article explains why Python is a top choice for beginners and professionals, outlines its rich library ecosystem, and presents a four‑stage learning path—from basic syntax and data structures to advanced features and real‑world projects—helping readers become competent Python developers.
This article introduces Python’s essential features for data analysis and mining, then reviews the most widely used libraries—NumPy, SciPy, Matplotlib, Pandas, Scikit‑Learn, Keras, and Gensim—each accompanied by concise code examples that demonstrate their core capabilities.
This article explains why quantifiable data is essential for evaluating the impact of operational changes, outlines common data‑collection stacks, defines core business and user‑centric metrics, and demonstrates practical monitoring techniques such as PCU analysis, simulated user flows, and intelligent scaling to turn ops work into measurable business value.
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.
