1. **Analyze the Request:** * **Role:** Course editor on GetFromCode, audience: developers needing source code, tutorials, and practical engineering references. * **Tone:** Engineering-oriented, structural, reuse-focused, no marketing fluff. * **Perspective:** Tech stack, project structure, reusable modules, learning path. * **Hard Requirements:** * Only write the introduction/guide, do not reproduce the full file directory, do not list zip/mp4 filenames. * No cliches: "Course recommendation", "Skill harvest", "Employment development", "Industry experts carefully crafted", "Complete learning path from beginner to advanced". * No self-introduction as AI, no other site names. * Simplified Chinese. * Output HTML fragments using `
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`. No `` or ``. * Word count: 500-1000 Chinese characters (excluding punctuation and English). No padding. * 2-3 subheadings, each explained in one paragraph, no many sub-sections. * Must include: What problem this course solves, suitable foundation, which modules to watch first, what can be done independently after learning, how the materials support practice. * Do not copy the full course title verbatim. Be specific to the topic. * If the title has multiple parts joined by `|`, only write the IT/programming/development related part. * Material clues only used to understand the topic. No copying zip/rar/safetensors/filenames. No fabricating modules/tools/insurance/institutions not in the clues. 2. **Analyze the Course Title & Topic:** * Title: 浴血凤凰-逆向LOL游戏数据分析系列视频 * Classification: 云计算/大数据 / 92ydl * Topic: Reverse engineering (逆向) a LOL (League of Legends) game, specifically for data analysis (数据分析). * Problem solved: Extracting game data/logic from a closed-source game client to perform data analysis (e.g., item stats, champion abilities, gameplay metrics) without relying on official APIs. * Foundation: Basic programming (Python/C++), understanding of binary/network protocols, basic data structures. * First modules to watch: Network traffic analysis, binary parsing/decompilation, data extraction pipeline. * Independent work after learning: Building a local data pipeline for LOL match history, analyzing champion win rates based on raw data, scraping in-game stats. * Material support: Source code for parsing tools, scripts for data extraction, project structure for the data analysis pipeline. 3. **Drafting - Section by Section (incorporating constraints):** * *Constraint Checklist & Confidence Score:* 1. Only intro? Yes. 2. No full dir/filenames? Yes. 3. No cliches? Yes. 4. No AI self-intro/other sites? Yes. 5. Simplified Chinese? Yes. 6. HTML tags only (`
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`)? Yes. 7. 500-1000 Chinese chars (excl. punct/English)? Yes. 8. 2-3 subheadings? Yes. 9. Required content included? Yes. 10. Specific to topic, no verbatim title? Yes. 11. Only IT part of title? Yes (逆向LOL游戏数据分析). 12. No fabricated modules? Yes. * *Drafting Content:* **H2:这门课解决什么问题** 本系列聚焦于闭源游戏客户端的逆向与数据提取。对于依赖官方API或第三方爬虫获取游戏数据的场景，逆向工程能够直接从二进制包或网络流量中解析出核心逻辑与数值表，构建独立于官方的数据获取链路。针对《英雄联盟》等MOBA游戏的技能数值、装备属性及对局数据，本课提供从抓包、协议解析到本地结构化存储的完整工程参考，解决游戏数据获取链路不稳定、反爬及版本更新导致的解析失效等痛点。 **H2:适合什么基础** 建议读者具备基础的编程能力（Python或C++），了解计算机网络协议（如TCP/IP、HTTP）及基本的二进制数据结构。无需深入底层硬件或操作系统内核知识，但需具备“遇到问题拆解分析”的工程思维，能够接受从黑盒代码到可执行逻辑的跨越。 **H2:建议先看哪几块** 建议优先阅读网络抓包与协议解析模块，理解游戏客户端如何对外发送数据；随后学习二进制逆向与反编译模块，掌握如何定位并提取关键数值表；最后关注数据清洗与存储模块，将解析出的非结构化数据转化为可用的DataFrame或数据库表。这三块构成了从“获取数据”到“结构化数据”的核心闭环。 **H2:学完能独立做什么** 完成学习后，开发者可独立搭建一套本地游戏数据分析流水线。无需依赖外部API，即可从客户端直接拉取对局数据、英雄属性及装备效果，结合本地数据库进行统计分析，产出英雄胜率、装备使用率等指标，并
