1. **Analyze the Request:** * **Role:** Course editor for GetFromCode (target audience: developers needing source code, tutorials, and practical engineering references). * **Tone:** Engineering-oriented, structured, practical, marketing-free. * **Angle:** Tech stack, project structure, reusable modules, learning path. * **Constraints:** * Only write a guide/introduction, do not list full file directories, do not list zip/mp4 filenames. * No cliches: "Course recommendation", "Skill harvest", "Career development", "Industry expert carefully crafted", "From beginner to advanced". * Do not claim to be AI, do not mention 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. * Structure: 2-3 subheadings, each a paragraph, no too many sub-sections. * Must include: Problem solved, suitable foundation, suggested first sections, independent work after learning, how materials support practice. * Do not repeat the full course title verbatim. Must be specific to this course. * Title filter: Keep only IT/Programming/Dev related parts. * Material clues only for understanding the theme. No copying zip/rar/safetensors/filenames. No fabricating modules/tools/jobs in the directory. 2. **Analyze the Course:** * **Title:** Python数据结构与算法教程 * **Category:** 测试运维 (Test & Ops) * **Summary:** Python is widely used in AI, Ops, Backend, Crawlers. Data structures and algorithms are foundational. This course uses Python to systematically explain common DS&A, helping learners improve dev skills. * **Features:** * Lecture notes, videos (explanation, diagrams, manual simulation), source code. Only videos are paid. Text is free on GitHub (https://github.com/PegasusWang/python_data_structures_and_algorithms). * Progressive lectures, combined with personal learning/usage experience. Real-time GitHub updates. * Demo code implementation ideas. All code written live in videos. * Engineering application and code implementation. Code is directly runnable. Self-contained files. This is where most books fall short. Good engineering practice: coding before talking (engineering practice). 3. **Drafting - Section by Section (incorporating constraints):** * **Subheading 1: Problem Solved & Suitable Foundation** * *Idea:* Python在运维、后端、爬虫等领域应用广泛，但很多开发者学了算法却用不上，或者看懂了理论代码却写不出工程可用的实现。这门课解决的就是“理论与工程落地脱节”的问题。 * *Draft:* 这门课解决的核心问题是：数据结构与算法的理论讲解往往脱离工程实际，导致开发者“看得懂纸面代码，却无法独立写出可运行的工程实现”。在运维、后端开发及自动化测试等场景下，高效的算法与数据结构是优化系统性能的基础。本课程适合具备 Python 基础，希望将算法能力转化为实际工程排障与开发效能的开发者。 * **Subheading 2: Suggested First Sections & Independent Work** * *Idea:* 建议先看基础数据结构（链表、栈、队列、哈希表）和核心排序算法，学完能独立实现并优化这些基础模块，解决实际业务中的性能瓶颈。 * *Draft:* 建议首先阅读基础数据结构（如链表、栈、队列、哈希表）与核心排序算法的章节。学完这些，你将能够独立实现并优化 Python 中的基础模块，应对实际业务中的性能瓶颈与数据组织需求。课程特别强调工程实践，所有代码均为自包含且可直接运行的版本，无需额外配置环境即可调试，这是大部分书籍难以提供的。 * **Subheading 3: Materials & Practice** * *Idea:* 文字内容在GitHub免费获取，配有讲义和现场编写的源代码。资料包线索提到视频包含图示和手动模拟，但文字免费。如何配合练习？直接运行调试代码。 * *Draft:* 资料的配合方式极为直接：文字讲义与源代码完全开源，GitHub 仓库实时更新，无需等待付费视频内容即可开始阅读与编码。课程通过现场演示代码编写的方式，将思路拆解到每一行实现中，避免了阅读源码时的无从下手。建议你边看文字讲义，边在本地运行和调试仓库中的自包含代码文件，通过手动模拟图解加深对执行过程的理解，从而真正掌握工程化实现思路。 4. **Review and Refine (Checking Constraints):** * *Role/Tone:* Engineering, structured, practical. (Check) * *No cliches:* Checked. No "skill harvest", "career development", etc. * *No AI/Other sites:* Checked. * *HTML tags:* Only `
