[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"similar-Moataz-Elmesmary--Data-Science-Roadmap":3,"tool-Moataz-Elmesmary--Data-Science-Roadmap":61},[4,18,28,36,45,53],{"id":5,"name":6,"github_repo":7,"description_zh":8,"stars":9,"difficulty_score":10,"last_commit_at":11,"category_tags":12,"status":17},4358,"openclaw","openclaw\u002Fopenclaw","OpenClaw 是一款专为个人打造的本地化 AI 助手，旨在让你在自己的设备上拥有完全可控的智能伙伴。它打破了传统 AI 助手局限于特定网页或应用的束缚，能够直接接入你日常使用的各类通讯渠道，包括微信、WhatsApp、Telegram、Discord、iMessage 等数十种平台。无论你在哪个聊天软件中发送消息，OpenClaw 都能即时响应，甚至支持在 macOS、iOS 和 Android 设备上进行语音交互，并提供实时的画布渲染功能供你操控。\n\n这款工具主要解决了用户对数据隐私、响应速度以及“始终在线”体验的需求。通过将 AI 部署在本地，用户无需依赖云端服务即可享受快速、私密的智能辅助，真正实现了“你的数据，你做主”。其独特的技术亮点在于强大的网关架构，将控制平面与核心助手分离，确保跨平台通信的流畅性与扩展性。\n\nOpenClaw 非常适合希望构建个性化工作流的技术爱好者、开发者，以及注重隐私保护且不愿被单一生态绑定的普通用户。只要具备基础的终端操作能力（支持 macOS、Linux 及 Windows WSL2），即可通过简单的命令行引导完成部署。如果你渴望拥有一个懂你",349277,3,"2026-04-06T06:32:30",[13,14,15,16],"Agent","开发框架","图像","数据工具","ready",{"id":19,"name":20,"github_repo":21,"description_zh":22,"stars":23,"difficulty_score":24,"last_commit_at":25,"category_tags":26,"status":17},9989,"n8n","n8n-io\u002Fn8n","n8n 是一款面向技术团队的公平代码（fair-code）工作流自动化平台，旨在让用户在享受低代码快速构建便利的同时，保留编写自定义代码的灵活性。它主要解决了传统自动化工具要么过于封闭难以扩展、要么完全依赖手写代码效率低下的痛点，帮助用户轻松连接 400 多种应用与服务，实现复杂业务流程的自动化。\n\nn8n 特别适合开发者、工程师以及具备一定技术背景的业务人员使用。其核心亮点在于“按需编码”：既可以通过直观的可视化界面拖拽节点搭建流程，也能随时插入 JavaScript 或 Python 代码、调用 npm 包来处理复杂逻辑。此外，n8n 原生集成了基于 LangChain 的 AI 能力，支持用户利用自有数据和模型构建智能体工作流。在部署方面，n8n 提供极高的自由度，支持完全自托管以保障数据隐私和控制权，也提供云端服务选项。凭借活跃的社区生态和数百个现成模板，n8n 让构建强大且可控的自动化系统变得简单高效。",184740,2,"2026-04-19T23:22:26",[16,14,13,15,27],"插件",{"id":29,"name":30,"github_repo":31,"description_zh":32,"stars":33,"difficulty_score":10,"last_commit_at":34,"category_tags":35,"status":17},3808,"stable-diffusion-webui","AUTOMATIC1111\u002Fstable-diffusion-webui","stable-diffusion-webui 是一个基于 Gradio 构建的网页版操作界面，旨在让用户能够轻松地在本地运行和使用强大的 Stable Diffusion 图像生成模型。它解决了原始模型依赖命令行、操作门槛高且功能分散的痛点，将复杂的 AI 绘图流程整合进一个直观易用的图形化平台。\n\n无论是希望快速上手的普通创作者、需要精细控制画面细节的设计师，还是想要深入探索模型潜力的开发者与研究人员，都能从中获益。其核心亮点在于极高的功能丰富度：不仅支持文生图、图生图、局部重绘（Inpainting）和外绘（Outpainting）等基础模式，还独创了注意力机制调整、提示词矩阵、负向提示词以及“高清修复”等高级功能。此外，它内置了 GFPGAN 和 CodeFormer 等人脸修复工具，支持多种神经网络放大算法，并允许用户通过插件系统无限扩展能力。即使是显存有限的设备，stable-diffusion-webui 也提供了相应的优化选项，让高质量的 AI 艺术创作变得触手可及。",162132,"2026-04-05T11:01:52",[14,15,13],{"id":37,"name":38,"github_repo":39,"description_zh":40,"stars":41,"difficulty_score":24,"last_commit_at":42,"category_tags":43,"status":17},1381,"everything-claude-code","affaan-m\u002Feverything-claude-code","everything-claude-code 是一套专为 AI 编程助手（如 Claude Code、Codex、Cursor 等）打造的高性能优化系统。它不仅仅是一组配置文件，而是一个经过长期实战打磨的完整框架，旨在解决 AI 代理在实际开发中面临的效率低下、记忆丢失、安全隐患及缺乏持续学习能力等核心痛点。\n\n通过引入技能模块化、直觉增强、记忆持久化机制以及内置的安全扫描功能，everything-claude-code 能显著提升 AI 在复杂任务中的表现，帮助开发者构建更稳定、更智能的生产级 AI 代理。其独特的“研究优先”开发理念和针对 Token 消耗的优化策略，使得模型响应更快、成本更低，同时有效防御潜在的攻击向量。\n\n这套工具特别适合软件开发者、AI 研究人员以及希望深度定制 AI 工作流的技术团队使用。无论您是在构建大型代码库，还是需要 AI 协助进行安全审计与自动化测试，everything-claude-code 都能提供强大的底层支持。作为一个曾荣获 Anthropic 黑客大奖的开源项目，它融合了多语言支持与丰富的实战钩子（hooks），让 AI 真正成长为懂上",161147,"2026-04-19T23:31:47",[14,13,44],"语言模型",{"id":46,"name":47,"github_repo":48,"description_zh":49,"stars":50,"difficulty_score":24,"last_commit_at":51,"category_tags":52,"status":17},2271,"ComfyUI","Comfy-Org\u002FComfyUI","ComfyUI 是一款功能强大且高度模块化的视觉 AI 引擎，专为设计和执行复杂的 Stable Diffusion 图像生成流程而打造。它摒弃了传统的代码编写模式，采用直观的节点式流程图界面，让用户通过连接不同的功能模块即可构建个性化的生成管线。\n\n这一设计巧妙解决了高级 AI 绘图工作流配置复杂、灵活性不足的痛点。用户无需具备编程背景，也能自由组合模型、调整参数并实时预览效果，轻松实现从基础文生图到多步骤高清修复等各类复杂任务。ComfyUI 拥有极佳的兼容性，不仅支持 Windows、macOS 和 Linux 全平台，还广泛适配 NVIDIA、AMD、Intel 及苹果 Silicon 等多种硬件架构，并率先支持 SDXL、Flux、SD3 等前沿模型。\n\n无论是希望深入探索算法潜力的研究人员和开发者，还是追求极致创作自由度的设计师与资深 AI 绘画爱好者，ComfyUI 都能提供强大的支持。其独特的模块化架构允许社区不断扩展新功能，使其成为当前最灵活、生态最丰富的开源扩散模型工具之一，帮助用户将创意高效转化为现实。",109154,"2026-04-18T11:18:24",[14,15,13],{"id":54,"name":55,"github_repo":56,"description_zh":57,"stars":58,"difficulty_score":24,"last_commit_at":59,"category_tags":60,"status":17},6121,"gemini-cli","google-gemini\u002Fgemini-cli","gemini-cli 是一款由谷歌推出的开源 AI 命令行工具，它将强大的 Gemini 大模型能力直接集成到用户的终端环境中。对于习惯在命令行工作的开发者而言，它提供了一条从输入提示词到获取模型响应的最短路径，无需切换窗口即可享受智能辅助。\n\n这款工具主要解决了开发过程中频繁上下文切换的痛点，让用户能在熟悉的终端界面内直接完成代码理解、生成、调试以及自动化运维任务。无论是查询大型代码库、根据草图生成应用，还是执行复杂的 Git 操作，gemini-cli 都能通过自然语言指令高效处理。\n\n它特别适合广大软件工程师、DevOps 人员及技术研究人员使用。其核心亮点包括支持高达 100 万 token 的超长上下文窗口，具备出色的逻辑推理能力；内置 Google 搜索、文件操作及 Shell 命令执行等实用工具；更独特的是，它支持 MCP（模型上下文协议），允许用户灵活扩展自定义集成，连接如图像生成等外部能力。此外，个人谷歌账号即可享受免费的额度支持，且项目基于 Apache 2.0 协议完全开源，是提升终端工作效率的理想助手。",100752,"2026-04-10T01:20:03",[27,13,15,14],{"id":62,"github_repo":63,"name":64,"description_en":65,"description_zh":66,"ai_summary_zh":66,"readme_en":67,"readme_zh":68,"quickstart_zh":69,"use_case_zh":70,"hero_image_url":71,"owner_login":72,"owner_name":73,"owner_avatar_url":74,"owner_bio":75,"owner_company":76,"owner_location":77,"owner_email":78,"owner_twitter":79,"owner_website":80,"owner_url":81,"languages":75,"stars":82,"forks":83,"last_commit_at":84,"license":85,"difficulty_score":86,"env_os":87,"env_gpu":88,"env_ram":88,"env_deps":89,"category_tags":98,"github_topics":100,"view_count":24,"oss_zip_url":75,"oss_zip_packed_at":75,"status":17,"created_at":121,"updated_at":122,"faqs":123,"releases":124},9945,"Moataz-Elmesmary\u002FData-Science-Roadmap","Data-Science-Roadmap","Data Science Roadmap from A to Z","Data-Science-Roadmap 是一份面向未来的数据科学自学指南，旨在帮助初学者从零开始系统性地掌握该领域核心技能。它解决了新手入行时常见的困惑，例如不清楚数据科学与数据分析、数据工程的区别，不了解项目全生命周期，或是对编程语言选择及必备软硬技能缺乏认知。\n\n这份路线图不仅清晰界定了不同数据岗位的职责差异，还提供了从环境搭建（如 Anaconda、Google Colab）到进阶学习的完整路径。其独特亮点在于汇总了大量免费且高质量的学习资源，并特别推荐了入门视频，帮助用户在动手前先建立正确的行业宏观视角，避免常见误区。\n\n无论是想转行进入数据领域的职场新人、计算机相关专业的学生，还是希望系统梳理知识体系的自学者，都能从中受益。它不依赖昂贵的课程，而是通过结构化的指引，让用户能够按需索取、循序渐进地构建自己的数据科学知识树，是开启数据科学之旅的实用向导。","\u003Cimg align=\"center\" width=\"730\" height=\"720\" src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FMoataz-Elmesmary_Data-Science-Roadmap_readme_86c67a6979d7.png\">\r\n\u003Ch2>     DATA SCIENCE ROADMAP :pirate_flag: 2026 \u003C\u002Fh2>\r\n \r\n### Data Science Roadmap for anyone interested in how to break into the field!\r\n\r\n\u003Ch4>\u003Ci>This repository is intended to provide a free Self-Learning Roadmap to learn the field of Data Science.  I provide some of the best free resources.\u003C\u002Fi>\u003C\u002Fh4>\u003Cbr>\r\n\r\n  [Our Previous Roadmap](https:\u002F\u002Fgithub.com\u002FSeif-Mohamed1\u002FDataScience-Squad) ♥️\u003Cbr>\r\n   :warning:\t\u003Cb>*Before we start,*\u003C\u002Fb> :warning:\r\n#### If you Dont know What`s Data Science or Projects Life Cycle (starting from Business Understanding to Deployment) or Which Programming Language you should go for or Job Descriptions or the required Soft & Hard Skills needed for this field or Data Science Applications or the Most Common Mistakes, then\u003Cbr>\r\n### :pushpin:**[This Video](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=5zRvq7CG6Zw&t=5s) is for you (Highly Recommended :heavy_check_mark:)**\r\n\r\n\r\n\u003Ch2>Data Science vs Data Analytics vs Data Engineering - What's the Difference?\u003C\u002Fh2>\u003Cbr>\r\n  \r\n![aaa](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FMoataz-Elmesmary_Data-Science-Roadmap_readme_803eb1c7ab63.png)\r\n\r\n\u003Ch4>\u003Ci>These terms are wrongly used interchangeably among people. There are distinct differences:\u003C\u002Fi>\u003C\u002Fh4>\r\n\r\n| :small_orange_diamond:\t**Data Science**        | :small_orange_diamond:\t**Data Analytics**           | :small_orange_diamond:\t**Data Engineering**           |\r\n| ------------- | --------------------- | -------------------- |\r\n|\u003Ch5>Is a multidisciplinary field that focuses on looking at raw and structured data sets and providing potential actionable insights. The field of Data Science looks at ensuring we are asking the right questions as opposed to finding exact answers.  Data Scientist require skillsets that are centered on Computer Science, Mathematics, and Statistics.  Data Scientist use several unique techniques to analyze data such as machine learning, trends, linear regressions, and predictive modeling.  The tools Data Scientist use to apply these techniques include Python and R.      \u003C\u002Fh5>|\u003Ch5> Focuses on looking at existing data sets and creating solutions to capture data, process data, and finally organize data to draw actionable insights. This field looks at finding general process, business, and engineering improvements we can make based on questions we don't know the answers to.  Data Analytics require skillsets that are centered on Statistics, Mathematics, and high level understanding of Computer Science. It involves data cleaning, data visualization, and simple modeling.  Common Data Analytic tools used include Microsoft Power Bi, Tableau, and SQL.  \u003C\u002Fh5>|\u003Ch5> Focuses on creating the correct infrastructure and tools required to support the business.  Data Engineers look at what are the optimal ways to store and extract data and involves writing scripts and building data warehouses.  Data Engineering require skillsets that are centered on Software Engineering, Computer Science and high level Data Science.  The tools Data Engineers utilize are mainly Python, Java, Scala, Hadoop, and Spark. \u003C\u002Fh5>|\r\n\r\n# Prepare your workspace\r\n\u003Cdetails>\u003Csummary> \u003Ch3>Tip :one:\t: Pick one and stick to it. (:file_folder:Click)\u003C\u002Fh3>\r\n\u003C\u002Fsummary>\r\n\u003Cbr>\r\n  \r\n[Anaconda](https:\u002F\u002Fwww.anaconda.com\u002Fproducts\u002Fdistribution): It’s a tool kit that fulfills all your necessities in writing and running code. From Powershell prompt to Jupyter Notebook and PyCharm, even R Studio (if interested to try R)\r\n\r\n![a](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FMoataz-Elmesmary_Data-Science-Roadmap_readme_680ae4c259e8.png)\r\n\r\n\r\n[Atom](https:\u002F\u002Fatom.io\u002Fpackages\u002Fide-python): A more advanced Python interface, highly recommended by experts. \u003Cbr \u002F>\r\n[Google Colab](https:\u002F\u002Fcolab.research.google.com\u002Fnotebooks\u002Fintro.ipynb): It’s like a Jupyter Notebook but in the cloud. You don’t need to install anything locally. All the important libraries are already installed. For example NumPy, Pandas, Matplotlib, and Sci-kit Learn \u003Cbr \u002F>\r\n[PyCharm](https:\u002F\u002Fwww.jetbrains.com\u002F): PyCharm is another excellent IDE that enables you to integrate with libraries such as NumPy and Matplotlib, allowing you to work with array viewers and interactive plots. \u003Cbr \u002F>\r\n[Thonny](https:\u002F\u002Fthonny.org\u002F): Thonny is an IDE for teaching and learning programming. Thonny is equipped with a debugger, and supports code completion, and highlights syntax errors.\u003C\u002Fdetails>\r\nMost learning platforms have integrated code exercises where you don’t need to install anything locally. But to learn it right, you should have an IDE installed on your local machine. Suggestions will be a marketplace with many options and few improvements from one platform to another.\r\n\r\n### Tip :two:\t: Focus on one course at least.\r\n### Tip :three: : Don’t chase certifications.\r\n### Tip :four: : Don’t rush for ML without having a good background in programming & maths.\r\n\r\n\r\n## This track is divided into 3 phases :arrow_down:\t:\r\n\r\n####   1. Beginner: you get a basic understanding of data analysis, tools and techniques.\r\n####   2. Intermediate: dive deeper in more complex topics of ML, Math and data engineering.\r\n####   3. Advanced: where we learn more advanced Math, DL and Deployment.\r\n\r\n:bell: For Data Camp courses, github student pack gives 3 free months. Google how to get it.\u003Cbr> \u003Ci>if you already used it, do not hesitate to contact us to have an account with free access. :hibiscus:\u003C\u002Fi>\r\n  \r\n\r\n## Legend\r\n* :video_camera: Video Content\r\n* :closed_book: Online Article Content \u002F Book\r\n### 💡 Roadmap Explanation ▶️ [Youtube Video](https:\u002F\u002Fyoutu.be\u002FHbIPJuvzRLk) :movie_camera:\u003Cimg src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FMoataz-Elmesmary_Data-Science-Roadmap_readme_1afdc385b897.gif\" width=50px height=40px>\r\n\r\n***\r\n\r\n## 🔰 Beginner 🔰\r\n\r\n[Algorithms Book](https:\u002F\u002Fgithub.com\u002Fcjbt\u002FFree-Algorithm-Books\u002Fblob\u002Fmaster\u002Fbook\u002FGrokking%20Algorithms%20-%20An%20illustrated%20guide%20for%20programmers%20and%20other%20curious%20people.pdf) \u003Ci>Every piece of code could be called an algorithm, but this book covers the\r\nmore interesting bits.\u003C\u002Fi>\u003Cbr>\r\n[Specializations (data structures-algorithms)](https:\u002F\u002Fwww.coursera.org\u002Fspecializations\u002Fdata-structures-algorithms)\r\n\r\n**1. Descriptive Statistics** \u003Cimg src=\"https:\u002F\u002Fraw.githubusercontent.com\u002Fmrankitgupta\u002F66DaysOfData\u002Fc8c040f1c85d921db317152567f331354446286a\u002Fstatistics-21.svg\" alt=\"Statistics\" width=\"25\" height=\"25\"\u002F> \u003Cbr>\r\n   📹 [Introduction to Statistics - DataCamp](https:\u002F\u002Fapp.datacamp.com\u002Flearn\u002Fcourses\u002Fintroduction-to-statistics)\u003Cbr>\r\n   📹 [Intro to Descriptive Statistics - YouTube](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=ApEV6IupW7o&list=PLAwxTw4SYaPn22DmaF6x8JtG4TeWOJk_1&index=1) old Udacity Course\u003Cbr>\r\n   📹 [Statistics Fundamentals - StatQuest - YouTube](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLblh5JKOoLUK0FLuzwntyYI10UQFUhsY9)\u003Cbr>\r\n   📹 [Introduction to Statistics - YouTube](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PL0KQuRyPJoe6KjlUM6iNYgt8d0DwI-IGR)\u003Cbr>\r\n   📕 [Online statistics education](http:\u002F\u002Fonlinestatbook.com\u002FOnline_Statistics_Education.pdf)\u003Cbr>\r\n   📹 Arabic Courses [1](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=_Lg1QtwZHvk&list=PLO3fADoO5fwNTr4Zjmz-cacmMh1S0o4Ml&index=1) - [2](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=d5jh5mmwcKI&list=PLY99ZSsxRyJiu6kb4WRRpeEFqK1pAr-EO)\u003Cbr>\r\n   📹 [Intro to Inferential Statistics](https:\u002F\u002Fwww.udacity.com\u002Fcourse\u002Fintro-to-inferential-statistics--ud201)++\u003Cbr>\r\n   📕 [Practical Statistics for Data Scientists](https:\u002F\u002Fgithub.com\u002FMoataz-Elmesmary\u002FData-Science-Roadmap\u002Fblob\u002Fmain\u002FBooks\u002FPractical%20Statistics%20for%20Data%20Scientists.pdf)\u003Cbr>\r\n\r\n**2. Probability**\u003Cbr>\r\n   📹 [Khan Academy](https:\u002F\u002Fwww.khanacademy.org\u002Fmath\u002Fstatistics-probability\u002Fprobability-library)\u003Cbr>\r\n   📹 [Probability Bootcamp by Dr.Steve - Oct 2024- YouTube](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLMrJAkhIeNNR3sNYvfgiKgcStwuPSts9V)\u003Cbr>\r\n   📹 [Arabic Course](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PL158D091D26F47358)\u003Cbr>\r\n   📹 [Probability and Statistics for AI and DS - Arabic (Dr.Hatem Elattar)](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLJM7jJIw2GC2Ihr__bRSeMxzsiFMZEsx7)\u003Cbr>\r\n   📕 [Introduction to Probability](https:\u002F\u002Fdrive.google.com\u002Ffile\u002Fd\u002F15Y0oFNHQRls1qvQNvO3DFLJVhIZvUjTD\u002Fview?usp=sharing)\u003Cbr>\r\n\r\n**3. Programming Languages**\u003Cbr>\r\n\r\n 🔹*R* - *good tool for visualization and statistical analysis.*\u003Cbr>\r\n           📹 [Introduction to R (DataCamp)](https:\u002F\u002Fwww.datacamp.com\u002Fcourses\u002Ffree-introduction-to-r)\u003Cbr>\r\n           📹 [Data Science Specialization - Coursera](https:\u002F\u002Fwww.coursera.org\u002Fspecializations\u002Fjhu-data-science)\u003Cbr>\r\n           📕 [An Introduction to R](https:\u002F\u002Fcran.r-project.org\u002Fdoc\u002Fmanuals\u002FR-intro.pdf)\u003Cbr>\r\n           📕 [R for Data Science](https:\u002F\u002Fr4ds.had.co.nz\u002F)\u003Cbr>\r\n\r\n 🔹*Python*:100: \u003Cbr>\r\n           📹 [Introduction to Python Programming](https:\u002F\u002Fwww.udacity.com\u002Fcourse\u002Fintroduction-to-python--ud1110)\u003Cbr>\r\n           📹 [OOP](https:\u002F\u002Flearn.datacamp.com\u002Fcourses\u002Fobject-oriented-programming-in-python)\u003Cbr>\r\n           📹 Arabic - [Hassouna](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=MxYLqE3Ils8&list=PLHIfW1KZRIfnM9y0sQRwjVz2-IwvnEJep) | [Elzero](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=mvZHDpCHphk&list=PLDoPjvoNmBAyE_gei5d18qkfIe-Z8mocs)\u003Cbr>\r\n           📹 [Python Full Course - FreeCodeCamp on YouTube](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=rfscVS0vtbw)\u003Cbr>\r\n           📕 [Intro to Python for CS and Data Science](https:\u002F\u002Fdrive.google.com\u002Ffile\u002Fd\u002F1rXkYFjw1iKbXCra_B4Ykm0AMRgo6v93w\u002Fview?fbclid=IwAR2lg9omGaAsG3g1ZhHQHja8_uxkZ7QddnOUSxfoceRXShU1V_bl4V63xCQ)\u003Cbr>\r\n           [more in OOP](https:\u002F\u002Fwww.futurelearn.com\u002Fcourses\u002Fobject-oriented-principles)\u003Cbr>\r\n**4. Pandas**\u003Cbr>\r\n           📹 [Corey Schafer-YouTube](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=ZyhVh-qRZPA&list=PL-osiE80TeTsWmV9i9c58mdDCSskIFdDS)\u003Cbr>\r\n           📕 [Kaggle](https:\u002F\u002Fwww.kaggle.com\u002Flearn\u002Fpandas)\u003Cbr>\r\n           📕 [Docs](https:\u002F\u002Fpandas.pydata.org\u002Fpandas-docs\u002Fversion\u002F0.15\u002Ftutorials.html)\u003Cbr>\r\n           📹 [Data School-YouTube](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=yzIMircGU5I&list=PL5-da3qGB5ICCsgW1MxlZ0Hq8LL5U3u9y&index=1)\u003Cbr>\r\n           📹 [Arabic Course](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=3ISW655DemU&list=PLvLvlVqNQGHCb2_ygmr1DQOMOv0yXp84F)\u003Cbr>\r\n           📹 PandasAI🐼[1](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=BtmMNZLxbuI) - [2](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=5w6eZaoDVVk) *Enhances the capabilities of Pandas by integrating Generative AI functionalities into it.* \u003Cbr>\r\n**5. Numpy**\u003Cbr>\r\n           📕 [Kaggle](https:\u002F\u002Fwww.kaggle.com\u002Flegendadnan\u002Fnumpy-tutorial-for-beginners-data-science)  \u003Cimg src=\"https:\u002F\u002Fraw.githubusercontent.com\u002Fmrankitgupta\u002Fmrankitgupta\u002F2a582d085b324cff4917325112229027309ecae3\u002FNumpy-logo.svg\" alt=\"numpy\" width=\"25\" height=\"20\"\u002F>\u003Cbr>\r\n           📹 [NumPy Tutorial by Keith Galli - YouTube](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=5-5CrLmf2vk&list=PLIA_seGogbkGDYq-dnVCsELEIq_7HK7Ca)\u003Cbr>\r\n           📹 [Arabic Course - Elzero](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLUgz8T_NoatsJCH-DmieQhqhSL2WBvlm-)\u003Cbr>\r\n           📕 [Tutorial](http:\u002F\u002Fcs231n.github.io\u002Fpython-numpy-tutorial\u002F)\u003Cbr>\r\n           📕 [Docs](https:\u002F\u002Fnumpy.org\u002Fdoc\u002F1.18\u002Fuser\u002Fquickstart.html)\u003Cbr>\r\n**6. Scipy**\u003Cbr>\r\n           📕 [Tutorial](https:\u002F\u002Fcs231n.github.io\u002Fpython-numpy-tutorial\u002F#scipy)\u003Cbr>\r\n           📕 [Docs](https:\u002F\u002Fdocs.scipy.org\u002Fdoc\u002Fscipy\u002Freference\u002Ftutorial\u002Fgeneral.html)\u003Cbr>\r\n**7. Data Cleaning**: One of the **MOST** important skills that you need to master to become a good data scientist, you need to practice on many datasets to master it.\u003Cbr>\r\n           [Read this](https:\u002F\u002Ftowardsdatascience.com\u002Fthe-ultimate-guide-to-data-cleaning-3969843991d4)\u003Cbr>\r\n           📹 [Course 1](https:\u002F\u002Fwww.datacamp.com\u002Fcourses\u002Fcleaning-data-in-python)\u003Cbr>\r\n           📕 [Notebook1](https:\u002F\u002Fwww.kaggle.com\u002Fbandiatindra\u002Ftelecom-churn-prediction)\u003Cbr\u002F>\r\n           📕 [Notebook2](https:\u002F\u002Fdrive.google.com\u002Fdrive\u002Ffolders\u002F1OQAEQ8rC4j6oBP7AyDU4bKpPr8sSStJI?fbclid=IwAR2dSrbyoZLM-Wm57yEYy8L8PmpPV9hqXdkNf-pURJC5C5xCz7UJB4YpJ7M)\u003Cbr\u002F>\r\n           📕 [Notebook3](https:\u002F\u002Fwww.kaggle.com\u002Fashishg21\u002Fdata-cleaning-and-some-analysis-shoe-prices)\u003Cbr>\r\n           📕 [Kaggle Data cleaning](https:\u002F\u002Fwww.kaggle.com\u002Flearn\u002Fdata-cleaning)\u003Cbr>\r\n**8. Data Visualization** :bar_chart:\t\u003Cbr>\r\n           📹 [Introduction to Data Visualization with Matplotlib](https:\u002F\u002Fapp.datacamp.com\u002Flearn\u002Fcourses\u002Fintroduction-to-data-visualization-with-matplotlib?fbclid=IwAR1OrJSdZ2LVD_c1o3d-_1I7Nhq8OZ3pzTu4010E_XWEmMc0KYsTosz8CIU) or\u003Cbr>\r\n           📹 [ Corey Schafer - Playlist on Youtube](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=UO98lJQ3QGI&list=PL-osiE80TeTvipOqomVEeZ1HRrcEvtZB_) or\u003Cbr>\r\n           📹 [sentdex - Playlist on YouTube](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=q7Bo_J8x_dw&list=PLQVvvaa0QuDfefDfXb9Yf0la1fPDKluPF)\u003Cbr>\r\n           📕 [Kaggle to Data Visualization with Seaborn](https:\u002F\u002Fwww.kaggle.com\u002Flearn\u002Fdata-visualization)\u003Cbr>\r\n           📹 [Playlist-Youtube](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=z7ZINBk8EUk&list=PL998lXKj66MpNd0_XkEXwzTGPxY2jYM2d)\u003Cbr>\r\n           📹 [Course1: Intro to Data Visualization with Seaborn](https:\u002F\u002Flearn.datacamp.com\u002Fcourses\u002Fintroduction-to-data-visualization-with-seaborn)\u003Cbr>\r\n           📹 [Course2: Intermediate Data Visualization with Seaborn\r\n](https:\u002F\u002Flearn.datacamp.com\u002Fcourses\u002Fintermediate-data-visualization-with-seaborn)\u003Cbr>\r\n           📹 [Course3: Understanding and Visualizing with Python](https:\u002F\u002Fwww.coursera.org\u002Flearn\u002Funderstanding-visualization-data)\u003Cbr>\r\n\r\n**9. EDA**\r\nNote: it's already mentioned in the above probability course \u003Cbr>\r\n           📹 [DataCamp-EDA in Python](https:\u002F\u002Flearn.datacamp.com\u002Fcourses\u002Fexploratory-data-analysis-in-python) \u003Cbr>\r\n           📹 [IBM-EDA for Machine Learning](https:\u002F\u002Fwww.coursera.org\u002Flearn\u002Fibm-exploratory-data-analysis-for-machine-learning) \u003Cbr>\r\n\r\n\u003Cimg align=\"right\" width=\"290\" height=\"203\" src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FMoataz-Elmesmary_Data-Science-Roadmap_readme_6f01a9e5954b.png\">\r\n\r\n**10. Dashboards**\u003Cbr>\r\n\r\n *Power BI*\u003Cbr>\r\n           📹 [Power BI - YouTube (Alex)](https:\u002F\u002Fyoutube.com\u002Fplaylist?list=PLUaB-1hjhk8HqnmK0gQhfmIdCbxwoAoys&si=pR4VSrR1P2O-AaBJ)\u003Cbr>\r\n           📹 [Power BI training](https:\u002F\u002Fpowerbi.microsoft.com\u002Fen-us\u002Flearning\u002F)\u003Cbr>\r\n           📹 [Arabic - YouTube (Zanoon)](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=P_Nr0FMyn9w&list=PL69umUTzySPGWMxnmhX9SV5PIEbdnHv63&index=1)\u003Cbr>\r\n           📹 [Arabic - YouTube](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=ykvAWKML9Gk&list=PLof3yw6ZFPFhV75Ptf-5Q88bgUtLOBvOw)\u003Cbr>\r\n           📹 [Guy in a Cube - YouTube](https:\u002F\u002Fwww.youtube.com\u002F@GuyInACube\u002Ffeatured)\u003Cbr>\r\n  *Tableau* \u003Cimg src=\"https:\u002F\u002Fraw.githubusercontent.com\u002Fmrankitgupta\u002Fmrankitgupta\u002Fa768d6bf0a001f03327578ae12f8867e4056cbaf\u002Ftableau-software.svg\" alt=\"tableau\" width=\"20\" height=\"20\"\u002F>\u003Cbr>\r\n           📹 [Data With Baraa - YouTube](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=_TT1D3tH1_c&list=PLNcg_FV9n7qZJqrKcUUCWCWPYCrlcVm9v)\u003Cbr>\r\n           📕 [Tutorial](https:\u002F\u002Fwww.datacamp.com\u002Fcommunity\u002Ftutorials\u002Fdata-visualisation-tableau)\u003Cbr>\r\n           📹 [Tableau Training](https:\u002F\u002Fwww.tableau.com\u002Flearn\u002Ftraining\u002F20201)\u003Cbr>\r\n           📹 [Course - DataCamp](https:\u002F\u002Flearn.datacamp.com\u002Fcourses\u002Fintroduction-to-tableau)\u003Cbr>\r\n           📹 [Simplilearn - YouTube](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=SSq5NwsUNGI&list=PLEiEAq2VkUUJEvrsey26P-Bj4Vk6BLBVC)\u003Cbr>\r\n\r\n\r\n**11. SQL and DB**\u003Cbr>\r\n            📹 SQL for Data Analysis ([Udacity](https:\u002F\u002Fwww.udacity.com\u002Fcourse\u002Fsql-for-data-analysis--ud198)-notes[*l📋l*](https:\u002F\u002Fgithub.com\u002Fjulianjohannesen\u002FUdacity-SQL-Notes\u002Ftree\u002Fmain) or [simplilearn](https:\u002F\u002Fwww.simplilearn.com\u002Ffree-online-course-to-learn-sql-basics-skillup))\u003Cbr>\r\n            📹 [Intro to SQL](https:\u002F\u002Flearn.datacamp.com\u002Fcourses\u002Fintroduction-to-sql) **or** [IBM (SQL for Data Science)](https:\u002F\u002Fwww.coursera.org\u002Flearn\u002Fsql-data-science)\u003Cbr>\r\n            📹 [Intro to Relational Databases in SQL](https:\u002F\u002Flearn.datacamp.com\u002Fcourses\u002Fintroduction-to-relational-databases-in-sql)\u003Cbr>\r\n            📹 Arabic Course ([Theoritical](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PL37D52B7714788190) - [Practical](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PL1DUmTEdeA6J6oDLTveTt4Z7E5qEfFluE)) Eldesouki\u003Cbr>\r\n            📹 Arabic - [ITI by Eng.Ramy](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLSGEGD0dbMKrvd5ppnyFLm7q3xEH97T-t) *Advanced* - *([Labs Answers + Notes + Full Materials](https:\u002F\u002Fgithub.com\u002FMoataz-Elmesmary\u002FITI-SQL-Labs))*\u003Cbr>\r\n            📹 Arabic - [SQL for Data Analysis](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=kb-_GbpH3sQ&t=38s) by Ahmed Sami\u003Cbr>\r\n            📹 [Data With Baraa - YouTube](https:\u002F\u002Fwww.youtube.com\u002F@DataWithBaraa\u002Fplaylists) - [[Materials]](https:\u002F\u002Fdatawithbaraa.substack.com\u002Fp\u002Faccess-to-course-materials)\u003Cbr>\r\n            📹 [365 Data Science - SQL](https:\u002F\u002Fmega.nz\u002Ffolder\u002FwswGEIhb#tsqUggTZyfy5HyRWUkV9sg\u002Ffolder\u002FR1AxXCxB)\u003Cbr>\r\n            📹 [CMU Intro to DB - Fall 2022](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLSE8ODhjZXjaKScG3l0nuOiDTTqpfnWFf) - *\u003C[Schedule📅](https:\u002F\u002F15445.courses.cs.cmu.edu\u002Ffall2022\u002Fschedule.html)>* - [Book📕](https:\u002F\u002Fgithub.com\u002FMoataz-Elmesmary\u002FData-Science-Roadmap\u002Fblob\u002Fmain\u002FBooks\u002FAbraham%20Silberschatz%2C%20Henry%20Korth%20and%20S.%20Sudarshan%20-%20Database%20System%20Concepts.%207-McGraw-Hill%20Education%20(2020).pdf)\u003Cbr>\r\n            📕 [SQL for Data Analysis](https:\u002F\u002Fgithub.com\u002FMoataz-Elmesmary\u002FData-Science-Roadmap\u002Fblob\u002Fmain\u002FBooks\u002FSQL%20for%20Data%20Analysis.pdf)\u003Cbr>\r\n            📝 Practice [InterviewMaster](https:\u002F\u002Fwww.interviewmaster.ai\u002F) & [HackerRank](https:\u002F\u002Fwww.hackerrank.com\u002Fdomains\u002Fsql) & [LeetCode](https:\u002F\u002Fleetcode.com\u002Fstudyplan\u002Ftop-sql-50\u002F) & [DataLemur](https:\u002F\u002Fdatalemur.com\u002F)\r\n\r\n**12. DWH** : *A system used for reporting - A core component of business intelligence.*\u003Cbr>\r\n     *Mostly used by Data Engineers.*\u003Cbr>\r\n            📕 [The Data Warehouse Toolkit](https:\u002F\u002Fgithub.com\u002FMoataz-Elmesmary\u002FData-Science-Roadmap\u002Fblob\u002Fmain\u002FBooks\u002FThe-Data-Warehouse-Toolkit-3rd-Edition.pdf)\u003Cbr>\r\n            📹 [Data Warehousing Tutorial Videos](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PL9ooVrP1hQOEDSc5QEbI8WYVV_EbWKJwX)\u003Cbr>\r\n            📹 [Garage Education](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLxNoJq6k39G_Ffv8Na1oRbob0sVHfFc_T) (Ar)\u003Cbr>\r\n            📹 [Implementing Data Warehouse in Arabic](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PL1565idytjOTwGN63vZK7lNK6pVXpGo3s) (Ar)\u003Cbr>\r\n            📹 [More in Arabic?](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLx5yn1EeCC_6ampJnoF2hHnHMj_-EGkU4) (Ar)\u003Cbr>\r\n            📹 [Data Warehouse - University of Colorado](https:\u002F\u002Fwww.coursera.org\u002Flearn\u002Fdwdesign)\u003Cbr>\r\n            📹 **[SSIS]** [SQL Server Integration Services](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLgOQg5m1pmp84jmXHGNWWYuU3m4bNCmfs) (Ar)\u003Cbr>\r\n            📹 [Project - Building Sales Data Mart Using SSIS](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLcAbhg_RWLaLUaYpAAvOLu2hlyVgZlRjb) (Ar)\u003Cbr>\r\n            📹 [Project - Building DWH Step by Step](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLcAbhg_RWLaLUaYpAAvOLu2hlyVgZlRjb)\u003Cbr>\r\n            📹 [Project - Create DWH Fact and Dimensions](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=8TSUoolAk2I) (Ar)\u003Cbr>\r\n            📹 [Implement SCD in SSIS](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=7uj463csru0) *Continue the playlist*\u003Cbr>\r\n            📹 [CDC in SSIS tutorial](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=QVF1JGFFt8w)\u003Cbr>\r\n\r\n**13. Python Regular Expression**\u003Cbr>\r\n           📕 [Tutorial](https:\u002F\u002Fwww.datacamp.com\u002Fcommunity\u002Ftutorials\u002Fpython-regular-expression-tutorial)\u003Cbr>\r\n           📹 [Regular Expressions by Corey - YouTube](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLDoPjvoNmBAyE_gei5d18qkfIe-Z8mocs)\u003Cbr>\r\n           📹 [Arabic Course - Elzero](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLDoPjvoNmBAyE_gei5d18qkfIe-Z8mocs) *starting from the 95th video.* \u003Cbr>\r\n\r\n**14. Time Series Analysis**\u003Cbr>\r\n            📹 [Track - DataCamp](https:\u002F\u002Flearn.datacamp.com\u002Fskill-tracks\u002Ftime-series-with-python)\u003Cbr>\r\n            📹 [Course - Coursera](https:\u002F\u002Fwww.coursera.org\u002Flearn\u002Fpractical-time-series-analysis)\u003Cbr>\r\n            📕 [Book](https:\u002F\u002Fwww.oreilly.com\u002Flibrary\u002Fview\u002Fpractical-time-series\u002F9781492041641\u002F?fbclid=IwAR20cq7hAdWf6voOd61u-pNzZCHvB0rZhT_BUoGTAXxPBhhi82p8BhxLEsI)\u003Cbr>\r\n            📕 [fbprohet](https:\u002F\u002Ffacebook.github.io\u002Fprophet\u002Fdocs\u002Fquick_start.html)\u003Cbr>\r\n            📹 Arabic Source [Video1](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=TvhaHPq6xLU&list=TLPQMjYwNzIwMjEPGXX6392WJA&index=1) & [Video2](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=mipF7mRVpk0&list=TLPQMjYwNzIwMjEPGXX6392WJA&index=2)\u003Cbr>\r\n  \r\n\u003Ch4>\u003Ci>At The end of the Beginner phase apply what you've learned on a project.\u003C\u002Fi>\u003C\u002Fh4>\r\n\r\n***\r\n\r\n\r\n## 🔰 Intermediate 🔰\r\n\r\n**1. Math for ML**: consists of Linear Algebra, Calculus and PCA. \u003Cbr>\r\n📹 [Mathematics for Machine Learning and Data Science - Andrew Ng](https:\u002F\u002Fwww.coursera.org\u002Fspecializations\u002Fmathematics-for-machine-learning-and-data-science?irclickid=zzy33K1O0xyNUAmxqWUjDwedUkAUtSWUJXKyTY0&irgwc=1&utm_medium=partners&utm_source=impact&utm_campaign=3117765&utm_content=b2c#courses)\u003Cbr>\r\n📹 [Specialization](https:\u002F\u002Fwww.coursera.org\u002Fspecializations\u002Fmathematics-machine-learning)\u003Cbr>\r\n📹 [Mathematics for Machine Learning - Most of the needed basics](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=vLJcduC4lBM&list=PLcQCwsZDEzFmlSc6levE3UV9rZ8yY-D_7)\u003Cbr>\u003Cbr>\r\n:small_blue_diamond:Linear Algebra\u003Cbr>\r\n            📹 [Khan Academy - Linear Algebra](https:\u002F\u002Fwww.khanacademy.org\u002Fmath\u002Flinear-algebra)\u003Cbr>\r\n            📹 [Mathematics for Machine Learning: Linear Algebra](https:\u002F\u002Fwww.coursera.org\u002Flearn\u002Flinear-algebra-machine-learning)\u003Cbr>\r\n            📹 [3Blue1Brown - Essence of Linear Algebra](https:\u002F\u002Fwww.3blue1brown.com\u002Ftopics\u002Flinear-algebra)\u003Cbr>\r\n:small_blue_diamond:Calculus\u003Cbr>\r\n            📹 [Multivariate Calculus - Coursera](https:\u002F\u002Fwww.coursera.org\u002Flearn\u002Fmultivariate-calculus-machine-learning?fbclid=IwAR243aoz0jxs4iUn539pnjSQliXtr7Y5QAsvgeRTietZT_tkyoRU3b6Sq1o)\u003Cbr>\r\n            📹 [Essence of calculus - Youtube](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLZHQObOWTQDMsr9K-rj53DwVRMYO3t5Yr)\u003Cbr>\r\n:small_blue_diamond:PCA\u003Cbr>\r\n            📹 [PCA - Coursera](https:\u002F\u002Fwww.coursera.org\u002Flearn\u002Fpca-machine-learning)\u003Cbr>\r\n\r\n\r\n**2. Machine Learning**\u003Cbr>\r\n            📹 [Coursera - Old Course by Andrew Ng (Octave\u002FMatlab)](https:\u002F\u002Fwww.coursera.org\u002Flearn\u002Fmachine-learning)\u003Cbr>\r\n            📹 [Coursera Andrew`s new ML Specialization (Python)](https:\u002F\u002Fwww.coursera.org\u002Fspecializations\u002Fmachine-learning-introduction?_hsenc=p2ANqtz-_R9x3Nm07uCw6YAw9VpCpdjRdfaUFyxdOcvgDljRt7j_NXiahN1plnI_Ob9jn0jSNipuE_Y08llrfPSt_1P7EBvj4LuImpBTKG3bsR6Z9bzjzBoRY&_hsmi=216611333&action=enroll&utm_campaign=mls-launch-2022&utm_content=216613012&utm_medium=email&utm_source=hs_email#courses)\u003Cbr>\r\n            📹 [Machine Learning - StatQuest - YouTube](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLblh5JKOoLUICTaGLRoHQDuF_7q2GfuJF)\u003Cbr>\r\n            📹 [Machine Learning Stanford Full Course on YouTube by Andrew](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=PPLop4L2eGk&list=PLLssT5z_DsK-h9vYZkQkYNWcItqhlRJLN)\u003Cbr>\r\n            📹 [CS480\u002F680 Intro to Machine Learning - Spring 2019 - University of Waterloo](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLdAoL1zKcqTW-uzoSVBNEecKHsnug_M0k)\u003Cbr>\r\n            📹 [SYDE 522 – Machine Intelligence (Winter 2018, University of Waterloo)](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PL4upCU5bnihwCX93Gv6AQnKmVMwx4AZoT)\u003Cbr>\r\n            📹 [Machine Learning for Engineers 2022](https:\u002F\u002Fapmonitor.com\u002Fpds\u002F) \u002F ([YouTube](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=Gh5rbBLh4JY&list=PLLBUgWXdTBDg1K1bu60lHypSzSP-WSBmx))\u003Cbr>\r\n            📹 [Introduction to Machine Learning Course - Udacity](https:\u002F\u002Fwww.udacity.com\u002Fcourse\u002Fintro-to-machine-learning--ud120)\u003Cbr>\r\n            📹 [Hesham Asem - Arabic content](https:\u002F\u002Fwww.youtube.com\u002Fc\u002FHeshamAsem\u002Fplaylists)\u003Cbr>\r\n            📹 [IBM ML with Python](https:\u002F\u002Fwww.coursera.org\u002Flearn\u002Fmachine-learning-with-python)\u003Cbr>\r\n            📹 [Machine Learning From Scratch - YouTube (Python Engineer)](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=ngLyX54e1LU&list=PLqnslRFeH2Upcrywf-u2etjdxxkL8nl7E)\u003Cbr>\r\n            📕 Hands On ML ([1st](https:\u002F\u002Fdrive.google.com\u002Ffile\u002Fd\u002F1uro1p6SlYolSkF0fbFKau0pOQ9ENZqny\u002Fview?usp=sharing) & [2nd](https:\u002F\u002Fdrive.google.com\u002Ffile\u002Fd\u002F1rS95FTNfiVG4WjGnPjd73GqrmEKey4N1\u002Fview?usp=sharing) & [3rd](https:\u002F\u002Fdrive.google.com\u002Ffile\u002Fd\u002F11VeqPJw8s9SC9Ru7IVeQhiTyV_9TliOE\u002Fview?usp=sharing)) Editions | Code: [![View on Github](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FExamples-Notebooks-orange?logo=Github)](https:\u002F\u002Fgithub.com\u002Fageron\u002Fhandson-ml?fbclid=IwAR3s31KlwkLKyrEwuEd4UMOcvHN1Q9Z2LLGzPg5vP4UKSwjriHxU0uO405c)\u003Cbr>\r\n            📹 [ML Algorithms in Practice](https:\u002F\u002Fwww.coursera.org\u002Fspecializations\u002Fmachine-learning-algorithms-real-world?utm_medium=email&utm_source=marketing&utm_campaign=A39CcMUuEempyReieZALEQ)\u003Cbr>\r\n            📹 [ML scientist](https:\u002F\u002Flearn.datacamp.com\u002Fcareer-tracks\u002Fmachine-learning-scientist-with-python?version=1)\u003Cbr>\r\n            📹 [Project](https:\u002F\u002Fwww.coursera.org\u002Flearn\u002Fapplied-data-science-capstone)\u003Cbr>\r\n\r\n**3. Web Scraping\u002FAPIs**\u003Cbr>\r\n            📹 [course](https:\u002F\u002Flearn.datacamp.com\u002Fcourses\u002Fweb-scraping-with-python)\u003Cbr>\r\n            📕 [intro2](https:\u002F\u002Fwww.dataquest.io\u002Fblog\u002Fweb-scraping-tutorial-python\u002F)\u003Cbr>\r\n            📕 [Tutorial](https:\u002F\u002Frealpython.com\u002Fbeautiful-soup-web-scraper-python\u002F)\u003Cbr>\r\n            📕 [Book for both topics](https:\u002F\u002Fb-ok.africa\u002Fbook\u002F3515980\u002F5d50aa)\u003Cbr>\r\nAPIs \u003Cbr>\r\n            📕 [Tutorial](https:\u002F\u002Fwww.dataquest.io\u002Fblog\u002Fpython-api-tutorial\u002F)\u003Cbr>\r\n            📕 [Article](https:\u002F\u002Fmedium.com\u002Fm\u002Fglobal-identity?redirectUrl=https%3A%2F%2Ftowardsdatascience.com%2Fhow-to-pull-data-from-an-api-using-python-requests-edcc8d6441b1)\u003Cbr>\r\n            📕 [Tutorial](https:\u002F\u002Frapidapi.com\u002Fblog\u002Fhow-to-use-an-api-with-python\u002F)\u003Cbr>\r\n**4. Stats.**\u003Cbr>\r\n            📕 [This stats - Book](https:\u002F\u002Fgreenteapress.com\u002Fthinkstats\u002Fthinkstats.pdf)\u003Cbr>\r\n            📕 [Think Bayes - Book](https:\u002F\u002Fwww.greenteapress.com\u002Fthinkbayes\u002Fthinkbayes.pdf)\u003Cbr>\r\n**5. Advanced SQL**\u003Cbr>\r\n            📹 [Joining Data in SQL - DataCamp](https:\u002F\u002Flearn.datacamp.com\u002Fcourses\u002Fjoining-data-in-postgresql)\u003Cbr>\r\n            📹 [Intermediate SQL - DataCamp](https:\u002F\u002Flearn.datacamp.com\u002Fcourses\u002Fjoining-data-in-postgresql)\u003Cbr>\r\n            📹 [More advanced SQL](https:\u002F\u002Fwww.coursera.org\u002Flecture\u002Fdata-driven-astronomy\u002Fmore-advanced-sql-GDmo5)\u003Cbr>\r\n\r\n**7. Feature Engineering**\u003Cbr>\r\n            📕 [Tutorial](https:\u002F\u002Fwww.kaggle.com\u002Flearn\u002Ffeature-engineering)\u003Cbr>\r\n            📕 [Article](https:\u002F\u002Fwww.medium.com\u002Fm\u002Fglobal-identity?redirectUrl=https%3A%2F%2Ftowardsdatascience.com%2Ffeature-engineering-for-machine-learning-3a5e293a5114)\u003Cbr>\r\n            📕 [Book](https:\u002F\u002Fdrive.google.com\u002Ffile\u002Fd\u002F1BkJYO0tqMYptTWUDQ7X0vd2aygohHRm8\u002Fview?usp=sharing)\u003Cbr>\r\n**8. interpret Shapley-based explanations of ML models.**\u003Cbr\u002F>\r\n           📕 [SHAP](https:\u002F\u002Fshap.readthedocs.io\u002Fen\u002Flatest\u002F)\u003Cbr\u002F>\r\n           📕 [Kaggle ML explainability](https:\u002F\u002Fwww.kaggle.com\u002Flearn\u002Fmachine-learning-explainability)\u003Cbr\u002F>\r\n\u003Ch4>\u003Ci>After finishing this level apply to 2 or 3 good sized projects.\u003C\u002Fi>\u003C\u002Fh4>\r\n\r\n\u003Ci>Read this book, please\u003C\u002Fi> :open_book: [Introduction to Statistical Learning with Applications in R](https:\u002F\u002Fgithub.com\u002FMoataz-Elmesmary\u002FData-Science-Roadmap\u002Fblob\u002Fmain\u002FBooks\u002FIntroduction%20to%20Statistical%20Learning%20with%20Applications%20in%20R.pdf) بقولك اقرأه\u003Cimg src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FMoataz-Elmesmary_Data-Science-Roadmap_readme_9035c3ba63ff.gif\" width=\"35\">\u003Cbr>\r\n***\r\n## 🔰 Advanced 🔰\r\n\r\n**1. Deep Learning** \u003Cbr>\r\n           📹 [Deep Learning Fundamentals](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLZbbT5o_s2xq7LwI2y8_QtvuXZedL6tQU)\u003Cbr>\r\n           📹 [Introduction to\r\nDeep Learning - MIT](http:\u002F\u002Fintrotodeeplearning.com\u002F?fbclid=IwAR35rIygYlCn84DV7mlHvdvs4sMUm2D6RLYVwFpp2nT2t1Zj1GGy3QAWQvQ)\u003Cbr>\r\n           📹 [Specialization](https:\u002F\u002Fwww.coursera.org\u002Fspecializations\u002Fdeep-learning)\u003Cbr>\r\n           📕 [Dive into Deep Learning (En)](https:\u002F\u002Fd2l.ai\u002Fd2l-en.pdf?fbclid=IwAR0sVdA8VFYpNZCpYZHgo_kl_HYrjcjDfjEka26D8xRWAhbhh6mmSNIXg3U) | (Ar) version :arrow_right:[Part1](https:\u002F\u002Fdrive.google.com\u002Ffile\u002Fd\u002F1SrmT_r8dNK42IqyS0gwXtbLCZbk5G8eu\u002Fview?fbclid=IwAR1Xcf8PNKkPJMg0uHRE1QyIW4_BMxISIdoB8pPaepw38njhaIf04MYM218) & [Part2](https:\u002F\u002Fdrive.google.com\u002Ffile\u002Fd\u002F1UqEu0amRfAvJD0L1HosIn3UJi0FkNemU\u002Fview?fbclid=IwAR1og8pkWr1gT3jdUwqikCZVrOCpyrm0x6ZRL63Kitwhki35pazHdo_ScJI) \u003Cbr>\r\n           📹 [Deep Learning UC Berkely](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLZSO_6-bSqHQHBCoGaObUljoXAyyqhpFW)\u003Cbr>\r\n           📕 [github of Dive into DL](https:\u002F\u002Fgithub.com\u002Fd2l-ai\u002Fd2l-en?fbclid=IwAR0QN35b-NHHWq_zKISA1cbI063aRqqoKqR_0e3cpnT5h58GkcNbCIJs3iw)\u003Cbr>\r\n           📹 [Stanford Lecture - Convolutional Neural Networks for Visual Recognition](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=vT1JzLTH4G4&list=PL3FW7Lu3i5JvHM8ljYj-zLfQRF3EO8sYv)\u003Cbr>\r\n           📹 [University of Waterloo - ML \u002F DL](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLdAoL1zKcqTW-uzoSVBNEecKHsnug_M0k)\u003Cbr>\r\n           📕 [Deep Learning for coders with fastai & PyTorch](https:\u002F\u002Fdl.ebooksworld.ir\u002Fbooks\u002FDeep.Learning.for.Coders.with.fastai.and.PyTorch.Howard.Gugger.OReilly.9781492045526.EBooksWorld.ir.pdf)\u003Cbr>\r\n\r\n\r\n**2. Tensorflow**\u003Cbr>\r\n           📹 [Specialization](https:\u002F\u002Fwww.coursera.org\u002Fspecializations\u002Ftensorflow-in-practice)\u003Cbr>\r\n           📹 [Youtube](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLZbbT5o_s2xrwRnXk_yCPtnqqo4_u2YGL)\u003Cbr>\r\n            [fast.ai's Deep Learning Courses](https:\u002F\u002Fwww.fast.ai\u002F)\u003Cbr> \r\n ###### \u003Ci>TensorFlow beats PyTorch in visualization capabilities and deploying trained models. Go for PyTorch if you want flexibility, debugging capabilities, and short training duration.\u003C\u002Fi>\r\n\r\n**3. PyTorch**\u003Cbr>\r\n           📹 [PyTorch (UC Berkeley - Youtube) - Lec3 (The 5 parts)](https:\u002F\u002Fm.youtube.com\u002Fwatch?v=AOypIa_8RXg&list=PL_iWQOsE6TfXxKgI1GgyV1B_Xa0DxE5eH&index=11)\u003Cbr>\r\n           📹 [PyTorch - Dr. Data Science - Youtube](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=vVQs4h6HUvA&list=PLLeO8f6PhlKb_FAC7qxOBtxT9-8EPDAqk)\u003Cbr>\r\n           📹 [Pytorch Tutorial - Aladdin - Youtube](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=2S1dgHpqCdk&list=PLhhyoLH6IjfxeoooqP9rhU3HJIAVAJ3Vz)\u003Cbr>\r\n           📹 [PyTorch Course (2022) - Youtube](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=v43SlgBcZ5Y&list=PLkdGijFCNuVk9fO1IMfdV1Igob0FUHhkB)\u003Cbr>\r\n           📕 [Deep Learning With Pytorch](https:\u002F\u002Fdrive.google.com\u002Ffile\u002Fd\u002F1-KG_ufeg7zw2iLgG5RrJSFpyonLwulgF\u002Fview?usp=sharing)\u003Cbr>\r\n           📕 [Machine Learning with PyTorch and Scikit-Learn -2022](https:\u002F\u002Fdl.ebooksworld.ir\u002Fbooks\u002FMachine.Learning.with.PyTorch.and.Scikit-Learn.Sebastian.Raschka.Packt.9781801819312.EBooksWorld.ir.pdf)\u003Cbr>\r\n\r\n**4. Advanced Data Science**\u003Cbr>\r\n           📹 [Advanced Data Science with IBM Specialization](https:\u002F\u002Fwww.coursera.org\u002Fspecializations\u002Fadvanced-data-science-ibm) *Includes Apache Spark*\u003Cbr>\r\n ☠️*Advanced ML Topics🧠 | Lecs (YouTube)* \u003Cbr>\r\n           📹 [Stanford CS330: Deep Multi-Task and Meta Learning I Autumn 2022](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLoROMvodv4rNjRoawgt72BBNwL2V7doGI) - [Materials](https:\u002F\u002Fcs330.stanford.edu\u002F)\u003Cbr>\r\n           📹 [18.409 Algorithmic Aspects of Machine Learning Spring 2015 - MIT](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLB3sDpSRdrOvI1hYXNsa6Lety7K8FhPpx)\u003Cbr>\r\n ☠️*ML based Computer Vision | Lecs (YouTube)* \u003Cbr>\r\n           📹 [CS 198-126: Modern Computer Vision Fall 2022 (UC Berkeley)](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLzWRmD0Vi2KVsrCqA4VnztE4t71KnTnP5)\u003Cbr>\r\n           📹 [NOC:Deep Learning For Visual Computing - IIT Kharagpur](https:\u002F\u002Fnptel.ac.in\u002Fcourses\u002F108105103)\u003Cbr>\r\n           📹 [Deep Learning for Computer Vision - Michigan](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PL5-TkQAfAZFbzxjBHtzdVCWE0Zbhomg7r)\u003Cbr>\r\n\r\n**5. NLP** \u003Cbr>\r\n           📹 [Specialization - Coursera](https:\u002F\u002Fwww.coursera.org\u002Fspecializations\u002Fnatural-language-processing)\u003Cbr>\r\n           📹 [Arabic - Ahmed El Sallab](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLxmZ0b-n395VxzEUL8-Dy257zSqYZe4yU)\u003Cbr>\r\n           📹 [Stanford CS224N Lectures - Winter 2021- YouTube](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?app=desktop&list=PLoROMvodv4rMFqRtEuo6SGjY4XbRIVRd4&si=k91y-bepIiPjHMrj&fbclid=IwAR2h6KcYboHCjG9YBIEB08srgYSesqZ5UHXr0ni8yxOqrxNV3-_TGxq0Csg)\u003Cbr>\r\n           📹 [Stanford XCS224U Lectures - Spring 2021- YouTube](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?app=desktop&list=PLoROMvodv4rMFqRtEuo6SGjY4XbRIVRd4&si=k91y-bepIiPjHMrj&fbclid=IwAR2h6KcYboHCjG9YBIEB08srgYSesqZ5UHXr0ni8yxOqrxNV3-_TGxq0Csg)\u003Cbr>\r\n           📹 [Introduction to Natural Language Processing in Python](https:\u002F\u002Fwww.datacamp.com\u002Fcourses\u002Fnatural-language-processing-fundamentals-in-python)\u003Cbr>\r\n 🔸*LLMS [What`s Large Language Model](https:\u002F\u002Fwww.snowflake.com\u002Fguides\u002Fwhat-large-language-model-and-what-can-llms-do-data-science)?* \u003Cbr>\r\n           📹 [Generative AI for Everyone (Andrew Nj) - Coursera](https:\u002F\u002Fwww.coursera.org\u002Flearn\u002Fgenerative-ai-for-everyone?utm_campaign=genai4e-launch&utm_medium=institutions&utm_source=deeplearning-ai#modules)🆕\u003Cbr>\r\n           📹 [Generative AI with LLMs](https:\u002F\u002Fwww.deeplearning.ai\u002Fcourses\u002Fgenerative-ai-with-llms\u002F)\u003Cbr>\r\n           📹 [Stanford CS236: Deep Generative Models I 2023 - YouTube](https:\u002F\u002Fwww.deeplearning.ai\u002Fcourses\u002Fgenerative-ai-with-llms\u002F)\u003Cbr>\r\n           📹 [Stanford CS25 - Transformers United 2023 - YouTube](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLoROMvodv4rNiJRchCzutFw5ItR_Z27CM)\u003Cbr>\r\n           📹 [Recent Advances on Foundation Models - Winter 2024 - University of Waterloo](https:\u002F\u002Fcs.uwaterloo.ca\u002F~wenhuche\u002Fteaching\u002Fcs886\u002F)\u003Cbr>\r\n           📹 [Understanding LLMs Foundations and Safety UC Berkeley - Spring 2024 - YouTube](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLJ66BAXN6D8H_gRQJGjmbnS5qCWoxJNfe)\u003Cbr>\r\n           📹 [LLM Foundations](https:\u002F\u002Ffullstackdeeplearning.com\u002Fllm-bootcamp\u002Fspring-2023\u002Fllm-foundations\u002F)\u003Cbr>\r\n           📹 How ChatGPTs \u002F Transformers work?[1](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=bQ5BoolX9Ag) - [2](https:\u002F\u002Fjalammar.github.io\u002Fhow-gpt3-works-visualizations-animations\u002F) - [3](https:\u002F\u002Fjalammar.github.io\u002Fillustrated-transformer\u002F) *overview & Maths behind*\u003Cbr>\r\n           📹 [Prompt Engineering](https:\u002F\u002Ffullstackdeeplearning.com\u002Fllm-bootcamp\u002Fspring-2023\u002Fprompt-engineering\u002F) | ([Ar](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=A-sNuzZgY8g&list=PLvLvlVqNQGHDNUshQJBWWCIRGgC0PN7VL)) *If you want to get the most out of LLMs*\u003Cbr>\r\n           📹 [LLMOps](https:\u002F\u002Ffullstackdeeplearning.com\u002Fllm-bootcamp\u002Fspring-2023\u002Fllmops\u002F) *A Lec going through the entire LLM pipeline*\u003Cbr>\r\n\r\n\r\n**6. Inferential Statistics** \u003Cbr>\r\n\u003Cimg align=\"right\" width=\"158\" height=\"200\" src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FMoataz-Elmesmary_Data-Science-Roadmap_readme_c8dccf082d9b.png\">\r\n           📹 [Specialization, 2nd & 3rd courses](https:\u002F\u002Fwww.coursera.org\u002Fspecializations\u002Fstatistics-with-python)\u003Cbr>\r\n           📹 [course](https:\u002F\u002Fwww.coursera.org\u002Flearn\u002Fstatistical-inferences)\u003Cbr>\r\n**7. Bayesian Statistics**\u003Cbr>\r\n           📹 [1 - From Concept to Data Analysis](https:\u002F\u002Fwww.coursera.org\u002Flearn\u002Fbayesian-statistics)\u003Cbr>\r\n           📹 [2 - Techniques and Models](https:\u002F\u002Fwww.coursera.org\u002Flearn\u002Fmcmc-bayesian-statistics)\u003Cbr>\r\n           📹 [3 - Mixture Models](https:\u002F\u002Fwww.coursera.org\u002Flearn\u002Fmixture-models)\u003Cbr>\r\n**8. Model Deployment** \u003Cbr>\r\n           📕 [Flask tutorial](https:\u002F\u002Ftowardsdatascience.com\u002Fdeploying-a-deep-learning-model-using-flask-3ec166ef59fb)\u003Cbr>\r\n           📹 [TensorFlow: Data and Deployment Specialization](https:\u002F\u002Fwww.coursera.org\u002Fspecializations\u002Ftensorflow-data-and-deployment)\u003Cbr>\r\n           📹 [Deploy Models with TensorFlow Serving and Flask](https:\u002F\u002Fwww.coursera.org\u002Fprojects\u002Fdeploy-models-tensorflow-serving-flask)\u003Cbr>\r\n           📹 [How to Deploy a Machine Learning Model to Google Cloud - Daniel Bourke](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=fw6NMQrYc6w)\u003Cbr>\r\n           if you`re interested in more deployment methods, search for (_FastAPI - Heroku - chitra_)\u003Cbr>\r\n        \r\n**9. MLOps** : is a combination of Model Deployment, Model Serving, Model Monitoring, and Model Maintenance.       \r\n           🔗 [MLOps-zoomcamp](https:\u002F\u002Fgithub.com\u002FDataTalksClub\u002Fmlops-zoomcamp)\u003Cbr>\r\n           🔗 [MLOps-guide](https:\u002F\u002Fgithub.com\u002FNyandwi\u002Fmachine_learning_complete\u002Fblob\u002Fmain\u002F010_mlops\u002F1_mlops_guide.md)\u003Cbr>\r\n           📕 [Practical MLOps](https:\u002F\u002Fdrive.google.com\u002Ffile\u002Fd\u002F17RhXQ2ix6rFMaas3HI7bnM_GL8lS7u3f\u002Fview?usp=sharing)\u003Cbr>\r\n**10. Probabilistic Graphical Models**    \r\n           📹 [Specialization - Coursera](https:\u002F\u002Fwww.coursera.org\u002Fspecializations\u002Fprobabilistic-graphical-models)\u003Cbr>\r\n           📹 [Spring 2016, University of Utah - YouTube](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLbuogVdPnkCpvxdF-Gy3gwaBObx7AnQut)\u003Cbr>\r\n\u003Cp align=\"center\">\r\n\u003Cimg src=\"https:\u002F\u002Fi.imgur.com\u002FdBaSKWF.gif\" height=\"20\" width=\"100%\">\r\n\r\n:star2:\t\u003Ci>Read these books, they will be beneficial to you.\u003C\u002Fi>\u003Cbr>\r\n  :open_book: [Bayesian Reasoning and Machine Learning](https:\u002F\u002Fdrive.google.com\u002Ffile\u002Fd\u002F18fh0orqSNAaIyhLkVwh9cGuWBywCBbuw\u002Fview?usp=sharing)\u003Cbr>\r\n  :open_book: [The Elements of Statistical Learning](https:\u002F\u002Fdrive.google.com\u002Ffile\u002Fd\u002F1ePRkuB9Zm5Fkw-1-VG8prQXfj8pI6dWX\u002Fview?usp=sharing)\u003Cbr>\r\n  :open_book: [Pattern Recognition and Machine Learning - Bishop](https:\u002F\u002Fdrive.google.com\u002Ffile\u002Fd\u002F1QkQj_azL6O7qUzshB8lPzueYWj0TRwEu\u002Fview?usp=sharing) (Advanced)\u003Cbr>\r\n#####    \u003Ci> Recommended by [Eng.Mohamed Hammad](https:\u002F\u002Fwww.linkedin.com\u002Fposts\u002Fmohamed-hammad-a720a622_%D9%83%D8%AA%D8%A7%D8%A8-%D9%83%D9%84-%D9%85%D8%B1%D9%87-%D8%A7%D8%AD%D8%AA%D8%A7%D8%AC%D9%87-%D9%88%D8%A7%D8%B1%D8%AC%D8%B9%D9%84%D9%87-%D8%A7%D8%A8%D9%82%D9%8A-%D8%B9%D8%A7%D9%88%D8%B2-%D9%83%D9%84-%D8%A7%D9%84%D9%84%D9%8A-activity-7080526619525693441-nNn0?utm_source=share&utm_medium=member_desktop).\u003C\u002Fi> \u003Cimg src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FMoataz-Elmesmary_Data-Science-Roadmap_readme_1afdc385b897.gif\" width=50px height=40px>\r\n***\r\n\u003Cimg align=\"right\" width=\"309\" height=\"250\" src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FMoataz-Elmesmary_Data-Science-Roadmap_readme_fbe1bfc07ae5.png\">\r\n\u003Ch3> 📌PROJECTS ⏬\u003C\u002Fh3>\u003Cbr>\r\n\r\n   🎥[Deena Gergis - End to end Project](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLatl6hdtJ0RnbkReSAuel6PeCPO155FpG)\u003Cbr>\r\n   🎥[Machine Learning Projects - Youtube](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=fiz1ORTBGpY&list=PLfFghEzKVmjvuSA67LszN1dZ-Dd_pkus6)\u003Cbr>\r\n   💻[Top 10 Data Science Projects for Beginners](https:\u002F\u002Fwww.kdnuggets.com\u002F2021\u002F06\u002Ftop-10-data-science-projects-beginners.html)\u003Cbr>\r\n   💻[12 Data Science Projects for Beginners and Experts](https:\u002F\u002Fbuiltin.com\u002Fdata-science\u002Fdata-science-projects)\u003Cbr>\r\n   💻[Data Science Projects & Ideas](https:\u002F\u002Fnevonprojects.com\u002Fdata-science-projects-solutions\u002F)\u003Cbr>\r\n   💻[Top 310+ Machine Learning Projects for 2023](https:\u002F\u002Fdata-flair.training\u002Fblogs\u002Fmachine-learning-project-ideas\u002F)\u003Cbr>\r\n   💻[10 End-to-End Guided Data Science Projects](https:\u002F\u002Fpub.towardsai.net\u002F10-end-to-end-guided-data-science-projects-to-build-your-portfolio-b7b9047fe6c9)\u003Cbr>\r\n   🎥[Real-World ML Tutorial w\u002F Scikit Learn](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=M9Itm95JzL0)\u003Cbr>\r\n   💻[Python Codes in Data Science](https:\u002F\u002Fgithub.com\u002FRubensZimbres\u002FRepo-2017\u002F)\u003Cbr>\r\n   🎥[End To End ML Project With Dockers,Github Actions And Deployment](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=MJ1vWb1rGwM)\u003Cbr>\r\n   💻[12 free Data Science projects to practice Python and Pandas (resolve interactive online)](https:\u002F\u002Fwww.datawars.io\u002Farticles\u002F12-free-data-science-projects-to-practice-python-and-pandas)\u003Cbr>\r\n\r\n***\r\n\u003Ch3>📌 Common Tools ⤵️\u003C\u002Fh3>\u003Cbr>\r\n\u003Cimg align=\"right\" width=\"158\" height=\"85\" src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FMoataz-Elmesmary_Data-Science-Roadmap_readme_9c50ca7f68af.png\">\r\n\r\nEnglish | Arabic | Book\r\n--- | --- | ---\r\n:movie_camera: [Git - Udacity](https:\u002F\u002Fwww.udacity.com\u002Fcourse\u002Fversion-control-with-git--ud123) | :movie_camera: [شخبط وانت مطمن ](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=Q6G-J54vgKc)🚀 | :closed_book: [Pro Git](https:\u002F\u002Fgit-scm.com\u002Fbook\u002Fen\u002Fv2)\r\n📖 [w3schools](https:\u002F\u002Fwww.w3schools.com\u002Fgit\u002F) | :movie_camera: [almadrasa](https:\u002F\u002Falmdrasa.com\u002Ftracks\u002Fprogramming-foundations\u002Fcourses\u002Fgit-github\u002F)\r\n  | :movie_camera: [Elzero](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLDoPjvoNmBAw4eOj58MZPakHjaO3frVMF) \r\n\r\n***       \r\n### :pushpin: **More Books :atom::atom: [:pushpin: Check This!](https:\u002F\u002Fdrive.google.com\u002Fdrive\u002Ffolders\u002F1iW7IPrVUqsHumgXUMH_rgeBLpJjRDCmJ?usp=sharing)** \t\u003Cbr>\r\n\u003Cimg align=\"right\" width=\"250\" height=\"197\" src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FMoataz-Elmesmary_Data-Science-Roadmap_readme_a39818037123.png\">\r\n\r\n    📕 [:fire:\t\u003Cb>12\u003C\u002Fb> Free Important Books :fire:](https:\u002F\u002Fgithub.com\u002FMoataz-Elmesmary\u002FData-Science-Roadmap\u002Ftree\u002Fmain\u002FBooks)\u003Cbr>\r\n          📕 [Mathematics for Machine Learning ](https:\u002F\u002Fmml-book.github.io\u002F)\u003Cbr>\r\n          📕 [An Introduction to Statistical Learning](https:\u002F\u002Fwww.statlearning.com\u002F)\u003Cbr>\r\n          📕 [Understanding ML: From Theory to Algorithms ](https:\u002F\u002Fwww.cs.huji.ac.il\u002F~shais\u002FUnderstandingMachineLearning\u002Funderstanding-machine-learning-theory-algorithms.pdf)\u003Cbr>\r\n          📕 [Probabilistic Machine Learning: An Introduction](https:\u002F\u002Fprobml.github.io\u002Fpml-book\u002Fbook1.html)\u003Cbr>\r\n          📕 [storytelling with data](https:\u002F\u002Fdrive.google.com\u002Ffile\u002Fd\u002F1OQu6ZWImGnHbuI_WJOLPdSvKWCABSWMH\u002Fview?usp=sharing) ✔️Important data visualization guide.\u003Cbr>\r\n        \r\n***\r\n\u003Cdetails>\r\n\u003Csummary> \u003Ch3> :pushpin:  \u003Cb>Collection of the best Cheat sheets\u003C\u002Fb>\u003C\u002Fh3>\u003C\u002Fsummary>\r\n\r\n1. [Importing Data](https:\u002F\u002Flnkd.in\u002Fe3jnyTEi)\r\n\r\n2. Pandas\r\n\r\n   - [(1)](https:\u002F\u002Flnkd.in\u002FeiXuBbWh_)\r\n   - [(2)](https:\u002F\u002Flnkd.in\u002Fe8PKwQQQ)\r\n   - [(3)](https:\u002F\u002Flnkd.in\u002FewQfqe8q)\r\n\r\n3. [Matplotlib](https:\u002F\u002Flnkd.in\u002FejxbW8ak)\r\n\r\n4. [Seaborn](https:\u002F\u002Flnkd.in\u002FejhxUp2K)\r\n\r\n5. [Probability](https:\u002F\u002Flnkd.in\u002Fe4Jxx6xP)\r\n\r\n6. [Supervised Learning](https:\u002F\u002Fgithub.com\u002Fafshinea\u002Fstanford-cs-229-machine-learning\u002Fblob\u002Fmaster\u002Fen\u002Fcheatsheet-supervised-learning.pdf)\r\n\r\n7. [Unsupervised Learning](https:\u002F\u002Fgithub.com\u002Fafshinea\u002Fstanford-cs-229-machine-learning\u002Fblob\u002Fmaster\u002Fen\u002Fcheatsheet-unsupervised-learning.pdf)\r\n\r\n8. [Deep Learning](https:\u002F\u002Fgithub.com\u002Fafshinea\u002Fstanford-cs-229-machine-learning\u002Fblob\u002Fmaster\u002Fen\u002Fcheatsheet-deep-learning.pdf)\r\n\r\n9. [Machine Learning Tips and Tricks](https:\u002F\u002Fgithub.com\u002Fafshinea\u002Fstanford-cs-229-machine-learning\u002Fblob\u002Fmaster\u002Fen\u002Fcheatsheet-machine-learning-tips-and-tricks.pdf)\r\n\r\n10. [Probabilities and Statistics](https:\u002F\u002Fgithub.com\u002Fafshinea\u002Fstanford-cs-229-machine-learning\u002Fblob\u002Fmaster\u002Fen\u002Frefresher-probabilities-statistics.pdf)\r\n\r\n11. [Comprehensive Stanford Master Cheat Sheet](https:\u002F\u002Fgithub.com\u002Fafshinea\u002Fstanford-cs-229-machine-learning\u002Fblob\u002Fmaster\u002Fen\u002Fsuper-cheatsheet-machine-learning.pdf)\r\n\r\n12. [Linear Algebra and Calculus](https:\u002F\u002Fgithub.com\u002Fafshinea\u002Fstanford-cs-229-machine-learning\u002Fblob\u002Fmaster\u002Fen\u002Frefresher-algebra-calculus.pdf)\r\n\r\n13. [Data Science Cheat Sheet](https:\u002F\u002Fs3.amazonaws.com\u002Fassets.datacamp.com\u002Fblog_assets\u002FPythonForDataScience.pdf)\r\n\r\n14. [Keras Cheat Sheet](https:\u002F\u002Fs3.amazonaws.com\u002Fassets.datacamp.com\u002Fblog_assets\u002FKeras_Cheat_Sheet_Python.pdf)\r\n\r\n15. [Deep Learning with Keras Cheat Sheet](https:\u002F\u002Fgithub.com\u002Frstudio\u002Fcheatsheets\u002Fraw\u002Fmaster\u002Fkeras.pdf)\r\n\r\n16. [Visual Guide to Neural Network Infrastructures](http:\u002F\u002Fwww.asimovinstitute.org\u002Fwp-content\u002Fuploads\u002F2016\u002F09\u002Fneuralnetworks.png)\r\n\r\n17. [Skicit-Learn Python Cheat Sheet](https:\u002F\u002Fs3.amazonaws.com\u002Fassets.datacamp.com\u002Fblog_assets\u002FScikit_Learn_Cheat_Sheet_Python.pdf)\r\n\r\n18. [Scikit-learn Cheat Sheet: Choosing the Right Estimator](https:\u002F\u002Fscikit-learn.org\u002Fstable\u002Ftutorial\u002Fmachine_learning_map\u002F)\r\n\r\n19. [Tensorflow Cheat Sheet](https:\u002F\u002Fgithub.com\u002Fkailashahirwar\u002Fcheatsheets-ai\u002Fblob\u002Fmaster\u002FPDFs\u002FTensorflow.pdf)\r\n\r\n20. [Machine Learning Test Cheat Sheet](https:\u002F\u002Fwww.cheatography.com\u002Flulu-0012\u002Fcheat-sheets\u002Ftest-ml\u002Fpdf\u002F)\r\n\r\n21. [Machine Learning Cheat Sheets (Recommended Guide)](https:\u002F\u002Fdrive.google.com\u002Ffile\u002Fd\u002F1rQRJvWk5s9rUCesri0apxutbF4eDHR69\u002Fview?usp=sharing) *راجع المواضيع اللي في الشيت دي يا عزيزي وشوف اللي ناقصك* \u003C\u002Fdetails> \r\n***\r\n\r\n### The best way to practice is to take part in competitions. \u003Cimg src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FMoataz-Elmesmary_Data-Science-Roadmap_readme_79bda4b417e4.gif\"  width=\"30px\" height=\"30px\">\u003Cimg src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FMoataz-Elmesmary_Data-Science-Roadmap_readme_79bda4b417e4.gif\"  width=\"30px\" height=\"30px\">\t\u003Cbr>\r\n**Competitions will make you even more proficient in Data Science.**\u003Cbr>\r\nWhen we talk about top data science competitions, [**Kaggle**](https:\u002F\u002Fwww.kaggle.com\u002F) is one of the most popular platforms for data science. Kaggle has a lot of competitions where you can participate according to your knowledge level.\u003Cbr>\r\n\r\n**You can also check these platforms for data science competitions-**\u003Cbr>\r\n        - [Driven Data](https:\u002F\u002Fwww.drivendata.org\u002Fcompetitions\u002F)\u003Cbr>\r\n        - [Codalab](https:\u002F\u002Fcompetitions.codalab.org\u002F)\u003Cbr>\r\n        - [Iron Viz](https:\u002F\u002Fwww.tableau.com\u002Fcommunity\u002Firon-viz)\u003Cbr>\r\n        - [Topcoder](https:\u002F\u002Fwww.topcoder.com\u002Fchallenges)\u003Cbr>\r\n        - [CrowdANALYTIX Community](https:\u002F\u002Fwww.crowdanalytix.com\u002Fcommunity)\u003Cbr>\r\n        - [Bitgrit](https:\u002F\u002Fbitgrit.net\u002F)\u003Cbr>\r\n\r\n        \r\n***\r\n\u003Cp align=\"center\">\u003Cstrong> Interview Preparation: Your Roadmap to Success 🚀 \u003C\u002Fstrong>\u003C\u002Fp>\r\n\r\n\u003Cb> 📓 Data Science Interview Questions: \u003C\u002Fb> :arrow_forward:\r\n  - [(1)](https:\u002F\u002Fgithub.com\u002FyoussefHosni\u002FData-Science-Interview-Questions)\r\n - [(2)](https:\u002F\u002Fgithub.com\u002Falexeygrigorev\u002Fdata-science-interviews)\r\n - [(3)](https:\u002F\u002Fgithub.com\u002Frbhatia46\u002FData-Science-Interview-Resources)\r\n - [(4)](https:\u002F\u002Fgithub.com\u002Fiamtodor\u002Fdata-science-interview-questions-and-answers)\r\n - [(5)](https:\u002F\u002Fgithub.com\u002Fmilaan9\u002FDataScience_Interview_Questions)\r\n - [(6) Arabic Podcast](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=YjloQOreudk):headphones:\u003Cbr>\r\n                    - [(7) 30 days of interview preparation](https:\u002F\u002Fgithub.com\u002FMoataz-Elmesmary\u002FData-Science-Roadmap\u002Fblob\u002Fmain\u002F30%20days%20of%20interview%20preparation.pdf):book:\u003Cbr>\t\r\n\u003Cb> 🚀 Practical Interview Questions from Actual Companies:\u003C\u002Fb> [Data Analysis](https:\u002F\u002Fprepare.sh\u002Finterviews\u002Fdata-analysis) & [Data Engineering](https:\u002F\u002Fprepare.sh\u002Finterviews\u002Fdata-engineering) by \u003Ci>@Prepare.sh\u003C\u002Fi>.\r\n\r\n***\r\n\u003Cimg align=\"right\" width=\"190\" height=\"145\" src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FMoataz-Elmesmary_Data-Science-Roadmap_readme_ff45f4212246.png\">\r\n\u003Cdetails>\u003Csummary>🎧\u003Cb>Data Science Podcasts: 🎙️\u003C\u002Fb>\u003Cbr> \u003Ci>The Best Way to Stay Up-to-Date on the Latest Data Science Trends and Developments\u003C\u002Fi>\u003Cimg src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FMoataz-Elmesmary_Data-Science-Roadmap_readme_026c64708ff4.gif\" width=\"29px\">\u003C\u002Fsummary>\r\n\r\n\r\n\u003Cbr>\r\n\r\nPodcasts  | About      | Produced by\r\n-- | --------------------------- | --\r\n[Data Science at Home](https:\u002F\u002Fdatascienceathome.com\u002F)|A podcast that provides practical advice and tutorials on data science topics.|Greg Linhardt, a data scientist and machine learning engineer at Google AI\r\n[Data Stories](https:\u002F\u002Fdatastori.es\u002F)|An interview-driven podcast that tells the stories of data scientists and how they're using their skills to make a difference in the world.| Kirill Eremenko, a data scientist and machine learning engineer at Netflix\r\n[O'Reilly Data Show](https:\u002F\u002Fwww.oreilly.com\u002Fradar\u002Ftopics\u002Foreilly-data-show-podcast\u002F)|A podcast that covers a wide range of data science topics, from machine learning to artificial intelligence to big data.|Ben Lorica, the Chief Data Scientist at O'Reilly\r\n[Learning Machines 101](https:\u002F\u002Fwww.learningmachines101.com\u002F) |Mathematics, statistics, and algorithms that power the machine learning systems that we rely on every day.|Richard Golden, a machine learning engineer and researcher at Google AI\r\n[Data Engineering Podcast](https:\u002F\u002Fwww.dataengineeringpodcast.com\u002F) |Tools, techniques, and difficulties associated with the discipline of data engineering. Databases, workflows, automation, and data manipulation.|Tobias Macey, a data engineer at Netflix\r\n[Data Science Mixer](https:\u002F\u002Fcommunity.alteryx.com\u002Ft5\u002FData-Science-Mixer\u002Fbg-p\u002Fmixer)  |A great resource for anyone who wants to learn more about data science and the latest trends in the field. It is also a great way to get inspired by the work of other data scientists and machine learning engineers.|Alteryx, a data science and analytics software company\r\n[Chai Time Data Science Show](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLLvvXm0q8zUbiNdoIazGzlENMXvZ9bd3x) |Interviews top data scientists, practitioners, and researchers from around the world.|Sanyam Bhutani, a data scientist and machine learning engineer at Google AI.\r\n[Becoming a Data Scientist](https:\u002F\u002Fwww.becomingadatascientist.com\u002Fcategory\u002Fpodcast\u002F)|Podcast that interviews data scientists about their journey to becoming a data scientist.|Renee Teate, a data scientist and machine learning engineer at Google AI.\r\n[AI Today Podcast](https:\u002F\u002Fwww.aidatatoday.com\u002Faitoday\u002F)|Explores the latest trends and developments in artificial intelligence.|Ron Schmelzer and Kathleen Walch\r\n[Gradient Dissent](https:\u002F\u002Fwandb.ai\u002Ffully-connected\u002Fpodcast)|A weekly podcast that explores the latest research in machine learning and artificial intelligence.|Chris Olah, a machine learning engineer at Google AI\r\n[Data Skeptic](https:\u002F\u002Fdataskeptic.com\u002F)|A podcast that challenges the conventional wisdom in data science and asks tough questions about the ethics and implications of data-driven decision making.|Kyle Polich, a data scientist and machine learning engineer\r\n[Linear Digressions](https:\u002F\u002Flineardigressions.com\u002F)|A podcast that covers a wide range of data science topics, from the technical to the theoretical.|Ben Recht and Noah Smith, two machine learning researchers at the University of California, Berkeley\r\n[The Data Engineering Show](https:\u002F\u002Fwww.dataengineeringshow.com\u002F)|For data engineering and BI practitioners to go beyond theory, and learn from the biggest influencers in tech about their practical day to day data challenges.|Eldad Farkash and Benjamin Wagner, who are both data engineering experts with experience at companies like Firebolt and Sisense\r\n[DataTalks.Club](https:\u002F\u002Fpodcasters.spotify.com\u002Fpod\u002Fshow\u002Fdatatalksclub)|A weekly online community of data enthusiasts and practitioners that learn from each other and share their knowledge and experiences through meetups, workshops, and a podcast.|A rotating cast of data experts\r\n[Datacast](https:\u002F\u002Fjameskle.com\u002Fwrites\u002Fcategory\u002FDatacast)|Top data scientists and practitioners in the data and AI infrastructure space.|James Le, who is a data infrastructure expert with experience at companies like Google and Netflix\r\n[How to Get an Analytics Job Podcast](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLBvzkZLydYX0D28bbnfRCV6M4zMQrhXsd)|A great resource for anyone who is interested in a career in analytics. The guests share their insights and advice on how to get started in analytics and how to succeed in an analytics career.|John David Ariansen, an analytics agency owner and career coach\r\n[The Analytics Power Hour](https:\u002F\u002Fanalyticshour.io\u002F)|Five awesome people, an occasional guest, and drinks all around tackling the hottest data and analytics topics of the day.|Tim Wilson, Michael Helbling, Josh Crowhurst, and Val Kroll. They are all analytics experts from different companies\r\n\u003C\u002Fdetails>\r\n\r\n\u003Cbr>\r\n\r\n\u003Cdetails>\u003Csummary>    :eyes: Arabic Podcasts??\u003C\u002Fsummary>\r\n\r\n######        :trollface:شايفك ياللي زهقان في المواصلات\r\n   📻[Arabic Data Podcast](https:\u002F\u002Fwww.youtube.com\u002F@arabic_data_podcast) | [Spotify](https:\u002F\u002Fopen.spotify.com\u002Fshow\u002F6xo79RT4NP73wQA39TgAq1) by Eng. Kareem Abdelsalam\u003Cbr>\r\n   📻[lإلي البيانات وما بعدها](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=3znPvz6P2oM&list=PL9yAM5pvSfU5EdppOCf-YvttRsabeAmbN) by Eng. Youssef Hosni\u003Cbr>\r\n   📻[Garage Education](https:\u002F\u002Fwww.youtube.com\u002F@GarageEducation\u002Fplaylists) by Eng. Mostafa Alaa\u003Cbr>\r\n   📻[Data Science بالعربي](https:\u002F\u002Fwww.boomplay.com\u002Fpodcasts\u002F29169)\u003Cbr>\r\n\u003C\u002Fdetails>\r\n\r\n***\r\n\r\n:pushpin:\t**Data Analysis Recommendations.**\u003Cbr>\r\n\u003Cimg align=\"right\" width=\"150\" height=\"150\" src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FMoataz-Elmesmary_Data-Science-Roadmap_readme_7329ba64f22e.png\">\r\n         Books (📕 [The Data Analysis Workshop](https:\u002F\u002Fdrive.google.com\u002Ffile\u002Fd\u002F1BjKsffA2SCY0jY8OIIzgQgM0ZS7E9v_v\u002Fview?fbclid=IwAR2_GBlrX7VYoo8WCRO9R2qqrYEqtytoGrObxy1QHWcQ7sRaFjRLb0GmuxM) &\r\n        📕 [Head First Data Analysis](https:\u002F\u002Fdrive.google.com\u002Ffile\u002Fd\u002F1HXHkwrgsSJLYSeB6I0wPUXIGGnm2-HQ6\u002Fview?fbclid=IwAR27M-dlPN6o0YuZg3bXH6_DP9L2fBhkKDEkChvO4SPG-SXfkxrzuoGP5RM))\u003Cbr>\r\n        [Google Data Analytics Professional Certificate](https:\u002F\u002Fwww.coursera.org\u002Fprofessional-certificates\u002Fgoogle-data-analytics)\u003Cbr>\r\n         [IBM Data Analyst Professional Certificate](https:\u002F\u002Fwww.coursera.org\u002Fprofessional-certificates\u002Fibm-data-analyst?fbclid=IwAR1IajEEe2yydVWRt3hbj4qLioXP6oR-fdbw8f1kHAVpAXSA4Z8Eww1Y-fs)\u003Cbr>\r\n         [Google Advanced Data Analytics Professional Certificate :new:](https:\u002F\u002Fwww.coursera.org\u002Fprofessional-certificates\u002Fgoogle-advanced-data-analytics?irclickid=zzy33K1O0xyNUAmxqWUjDwedUkAQlBwwJ21EwA0&irgwc=1&utm_medium=partners&utm_source=impact&utm_campaign=2624140&utm_content=b2c)\u003Cbr>\r\n         [Alex The Analyst - YouTube📺](https:\u002F\u002Fwww.youtube.com\u002F@AlexTheAnalyst\u002Fplaylists)\u003Cbr>\r\n         [FWD - (The 3 Levels)](https:\u002F\u002Fegfwd.com\u002F?fbclid=IwAR1phYmHHgi0L4E9nOPZcSfAdHWsDs9EvBh3dJgO6gXN4B1A-nV8vspGggs)\u003Cbr>\r\n         [Arabic - ITI - BI Dev Track](https:\u002F\u002Fgithub.com\u002FMoataz-Elmesmary\u002FITI-Business-Intelligence-Development)\u003Cbr>\r\n        *Note: A good knowledge & projects in just [Excel](https:\u002F\u002Fwww.coursera.org\u002Flearn\u002Fexcel-basics-data-analysis-ibm), SQL & Power BI \u002F Tableau can bring you great opportunities*.\u003Cbr>\r\n          -\u003Cimg src=\"https:\u002F\u002Fraw.githubusercontent.com\u002Fmrankitgupta\u002F66DaysOfData\u002F60139fb461ef56a19afd68ea4094f6069f27ce49\u002Ficons8-microsoft-excel%20(1).svg\" alt=\"excel\" width=\"25\" height=\"25\"\u002F> Excel More Resources: ([Arabic 1📹](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=9Z5MPeyuLhg&t=397s) - [Arabic 2📹](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=uRs8_EJqTFo&list=PLXlHqMRg9lAYiiutr-Ou0J1uU20T-5a4-&pp=iAQB) - [Books :page_facing_up: and cheat sheets for revising](https:\u002F\u002Fdrive.google.com\u002Fdrive\u002Ffolders\u002F1CAUKDb5jv1pMez1WO74ogkpX44UMW_ky))\u003Cbr>\r\n\u003Cp align=\"center\">\r\n\u003Cimg src=\"https:\u002F\u002Fi.imgur.com\u002FdBaSKWF.gif\" height=\"20\" width=\"100%\">\r\n\r\n:pushpin:\t**[Data Engineering](https:\u002F\u002Fyoutu.be\u002FqWru-b6m030) Recommendations.**\u003Cbr>\r\n         Books (📕 [Fundamentals of Data Engineering](https:\u002F\u002Fdrive.google.com\u002Ffile\u002Fd\u002F1CbQFN0Lw8o6v4KlF64LsCyaooMccT45T\u002Fview?usp=sharing) &\r\n        📕 [Designing Data-Intensive Applications](https:\u002F\u002Fdrive.google.com\u002Ffile\u002Fd\u002F1CrzA--WWNcxxQwLqzg1yPfiI3FaEo49z\u002Fview?usp=sharing))\u003Cbr>\r\n\u003Cimg align=\"right\" width=\"150\" height=\"150\" src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FMoataz-Elmesmary_Data-Science-Roadmap_readme_0284bdf0e62c.png\">\r\n         Arabic Podcast, [Starting a Career in Data Engineering.](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=OtaBhXjrbX4)\u003Cbr>\r\n         For Arab, I recommend 2 YouTube Channels: ([Garage Education](https:\u002F\u002Fwww.youtube.com\u002F@GarageEducation) & [Big Data بالعربي](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLrooD4hY1QqAK5pbBpcthLuMa-cXnXJLE))\u003Cbr>\r\n         [Roadmap 1](https:\u002F\u002Fgithub.com\u002FOmarEhab007\u002FData_Engineering_Mentorship) - *(Recommended)*\u003Cbr> \r\n         [Roadmap 2](https:\u002F\u002Fwww.educba.com\u002Fdata-engineer-roadmap\u002F)\u003Cbr> \r\n         [Roadmap 3](https:\u002F\u002Fgithub.com\u002Fdatastacktv\u002Fdata-engineer-roadmap)\u003Cbr>\r\n         [IBM Data Engineering Professional Certificate](https:\u002F\u002Fwww.coursera.org\u002Fprofessional-certificates\u002Fibm-data-engineer)\u003Cbr>\r\n        *Note: A good knowledge & projects in SQL, Python, Apache [Spark](https:\u002F\u002Fwww.udacity.com\u002Fcourse\u002Flearn-spark-at-udacity--ud2002)\u002FHadoop, Data Modeling and [[Data Warehouse](https:\u002F\u002Fwww.coursera.org\u002Flearn\u002Fdwdesign) - {Arabic-[Starting from the 7th video](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLxNoJq6k39G_m6DYjpz-V92DkaQEiXxkF)} can bring you great opportunities. Start with them then go for the other tools,concepts and cloud platforms*.\u003Cbr>\r\n***\r\n\u003Cdetails>\u003Csummary>:file_folder: \u003Cb>CV \u002F Resumes :memo: \u003C\u002Fb>  \r\n \u003Cimg src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FMoataz-Elmesmary_Data-Science-Roadmap_readme_665e7aaaf5cf.gif\" width=\"75\">\r\n  \u003Ca href=\"https:\u002F\u002Fgit.io\u002Ftyping-svg\">\r\n    \u003Cimg src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FMoataz-Elmesmary_Data-Science-Roadmap_readme_b29bbe702d4d.png\">\r\n\u003C\u002Fsummary>\r\n  \r\n- [Common mistakes by Yehia Arafa Mostafa](https:\u002F\u002Fwww.facebook.com\u002Fyehia.arafa.mostafa\u002Fposts\u002F110086229517000)\u003Cbr>\r\n- [CV Tips by Omar Yasser](https:\u002F\u002Fmedium.com\u002F@oyaraouf\u002Fcv-tips-5faaec55ec07)\u003Cbr>\r\n- [This Is What A GOOD Resume Should Look Like by careercup](https:\u002F\u002Fwww.careercup.com\u002Fresume)\u003Cbr>\r\n- After you have made your beta-version resume, check those [reviews from Mostafa Nageeb](https:\u002F\u002Fwww.facebook.com\u002Fstory.php?story_fbid=2928705840553931&id=445112032246670)\u003Cbr>\r\n- [After Graduation by Yasser Alaa](https:\u002F\u002Fwww.linkedin.com\u002Ffeed\u002Fupdate\u002Furn:li:activity:6964595411839799296\u002F)\u003Cbr>\r\n- [How to make Data Science Resume](https:\u002F\u002Fenhancv.com\u002Fresume-examples\u002Fdata-scientist\u002F)\u003Cbr>\r\n- [Data Science Resume Guide](https:\u002F\u002Fwww.beamjobs.com\u002Fresumes\u002Fdata-science-resume-example-guide)\u003Cbr>\r\n- Resume\u002FCV building for Data Jobs (Arabic)\u003Cbr>\r\n  📹[Video 1](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=R0hsJiNxdDE)\u003Cbr>\r\n  📹[Video 2](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=CrTO0hrC-zQ)\r\n\u003C\u002Fdetails>\r\n\r\n***\r\n:pushpin: [\u003Cb>\u003Ci>Data & AI Companies in Egypt\u003C\u002Fi>\u003C\u002Fb>](https:\u002F\u002Ftrello.com\u002Fb\u002Fu4HH9Anu\u002Fdata-ai-jobs-in-egypt)   -   [\u003Ci>AI\u002FML Driven Companies In Egypt\u003C\u002Fi>](https:\u002F\u002Fgithub.com\u002Fharryadel\u002FAI-ML-Driven-Companies-In-Egypt)\r\n***\r\n\r\n\u003Ch2>Contact Me :iphone:\u003C\u002Fh2> \t \u003Cbr>\r\n  \r\n\u003Ca href=\"https:\u002F\u002Fwww.facebook.com\u002FMoatazElmesmary\u002F\" title=\"Facebook\">\u003Cimg src=\"https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FFacebook-%234267B2?style=flat&logo=Facebook&logoColor=white\"\u002F>\u003C\u002Fa>\r\n\u003Ca href=\"https:\u002F\u002Ftwitter.com\u002FMoatazElmesmary\" title=\"twitter\">\u003Cimg src=\"https:\u002F\u002Fimg.shields.io\u002Ftwitter\u002Furl?label=twitter&style=social&url=https%3A%2F%2Fimg.shields.io%2Ftwitter%2F%3Flabel%3Dtwitter%26style%3Dsocial\"\u002F>\u003C\u002Fa>\r\n\u003Ca href=\"https:\u002F\u002Fwww.linkedin.com\u002Fin\u002FMoatazElmesmary\u002F\" title=\"LinkedIn\">\u003Cimg src=\"https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FLinkedIn-%230177B5?style=flat&logo=linkedin&logoColor=white\"\u002F>\u003C\u002Fa>\u003Ch1 align=\"center\">\r\n[![Typing SVG](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FMoataz-Elmesmary_Data-Science-Roadmap_readme_70ddd29c2533.png)](https:\u002F\u002Fgit.io\u002Ftyping-svg)\u003C\u002Fh1>\r\n\r\n\r\n\r\n","\u003Cimg align=\"center\" width=\"730\" height=\"720\" src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FMoataz-Elmesmary_Data-Science-Roadmap_readme_86c67a6979d7.png\">\r\n\u003Ch2>     数据科学路线图 :pirate_flag: 2026 \u003C\u002Fh2>\r\n \r\n### 面向所有对如何进入数据科学领域感兴趣的人的数据科学路线图！\r\n\r\n\u003Ch4>\u003Ci>本仓库旨在提供一份免费的自学路线图，帮助大家学习数据科学领域。我整理了一些最优质的免费资源。\u003C\u002Fi>\u003C\u002Fh4>\u003Cbr>\r\n\r\n  [我们之前的路线图](https:\u002F\u002Fgithub.com\u002FSeif-Mohamed1\u002FDataScience-Squad) ♥️\u003Cbr>\r\n   :warning:\t\u003Cb>*在开始之前,*\u003C\u002Fb> :warning:\r\n#### 如果你还不了解什么是数据科学、项目生命周期（从业务理解到部署）、该选择哪种编程语言、岗位职责、这一领域所需的软硬技能、数据科学的应用场景以及常见错误，那么\u003Cbr>\r\n### :pushpin:**[这个视频](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=5zRvq7CG6Zw&t=5s) 就是为你准备的（强烈推荐 :heavy_check_mark:)**\r\n\r\n\r\n\u003Ch2>数据科学 vs 数据分析 vs 数据工程——它们有什么区别？\u003C\u002Fh2>\u003Cbr>\r\n  \r\n![aaa](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FMoataz-Elmesmary_Data-Science-Roadmap_readme_803eb1c7ab63.png)\r\n\r\n\u003Ch4>\u003Ci>这些术语经常被人们混用。实际上它们有着明显的区别：\u003C\u002Fi>\u003C\u002Fh4>\r\n\r\n| :small_orange_diamond:\t**数据科学**        | :small_orange_diamond:\t**数据分析**           | :small_orange_diamond:\t**数据工程**           |\r\n| ------------- | --------------------- | -------------------- |\r\n|\u003Ch5>数据科学是一个多学科交叉的领域，专注于分析原始数据和结构化数据集，以提供潜在的可操作性洞察。它更注重提出正确的问题，而非直接寻找确切的答案。数据科学家需要掌握计算机科学、数学和统计学等领域的技能。他们使用机器学习、趋势分析、线性回归和预测建模等多种独特技术来分析数据。常用工具包括 Python 和 R。\u003C\u002Fh5>|\u003Ch5>数据分析侧重于研究现有数据集，构建用于采集、处理和组织数据的解决方案，从而得出可操作的见解。该领域致力于发现基于未知问题的一般性流程、业务和工程改进方案。数据分析人员需具备统计学、数学以及较高层次的计算机科学知识。其工作内容包括数据清洗、数据可视化和简单建模。常用工具有 Microsoft Power BI、Tableau 和 SQL。\u003C\u002Fh5>|\u003Ch5>数据工程专注于构建支持业务所需的正确基础设施和工具。数据工程师关注如何以最优方式存储和提取数据，并编写脚本、搭建数据仓库。数据工程人员需要掌握软件工程、计算机科学以及高级数据科学知识。主要使用的工具有 Python、Java、Scala、Hadoop 和 Spark。\u003C\u002Fh5>|\r\n\r\n# 准备你的工作环境\r\n\u003Cdetails>\u003Csummary> \u003Ch3>提示一：选一个工具并坚持使用。（:file_folder:点击）\u003C\u002Fh3>\r\n\u003C\u002Fsummary>\r\n\u003Cbr>\r\n  \r\n[Anaconda](https:\u002F\u002Fwww.anaconda.com\u002Fproducts\u002Fdistribution)：这是一个功能齐全的工具包，能满足你编写和运行代码的所有需求。无论是 PowerShell 提示符、Jupyter Notebook 还是 PyCharm，甚至 R Studio（如果你有兴趣尝试 R 语言）都能轻松应对。\r\n\r\n![a](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FMoataz-Elmesmary_Data-Science-Roadmap_readme_680ae4c259e8.png)\r\n\r\n\r\n[Atom](https:\u002F\u002Fatom.io\u002Fpackages\u002Fide-python)：这是一款更为高级的 Python 编辑器，深受专家推荐。\u003Cbr \u002F>\r\n[Google Colab](https:\u002F\u002Fcolab.research.google.com\u002Fnotebooks\u002Fintro.ipynb)：类似于云端的 Jupyter Notebook，无需在本地安装任何东西。重要的库如 NumPy、Pandas、Matplotlib 和 Sci-kit Learn 都已预装好。\u003Cbr \u002F>\r\n[PyCharm](https:\u002F\u002Fwww.jetbrains.com\u002F)：PyCharm 是另一款优秀的集成开发环境，可以与 NumPy 和 Matplotlib 等库无缝集成，方便你使用数组查看器和交互式图表。\u003Cbr \u002F>\r\n[Thonny](https:\u002F\u002Fthonny.org\u002F)：Thonny 是一款专为编程教学设计的 IDE。它配备了调试器，支持代码补全，并能高亮显示语法错误。\u003C\u002Fdetails>\r\n大多数在线学习平台都内置了代码练习功能，无需在本地安装任何东西。但要真正学好，还是建议在本地电脑上安装一个 IDE。市面上有许多选择，各平台的功能也各有优劣，可以根据自己的需求进行挑选。\r\n\r\n### 提示二：至少专注于一门课程。\r\n### 提示三：不要盲目追求认证。\r\n### 提示四：如果没有扎实的编程和数学基础，就不要急于学习机器学习。\r\n\r\n\r\n## 本路线图分为三个阶段 :arrow_down:\t:\r\n\r\n####   1. 初级：掌握数据分析的基础知识、工具和技术。\r\n####   2. 中级：深入学习更复杂的机器学习、数学和数据工程主题。\r\n####   3. 高级：学习更高级的数学、深度学习和模型部署技术。\r\n\r\n:bell: 对于 DataCamp 的课程，GitHub 学生包可以提供 3 个月的免费试用。请自行搜索如何获取。\u003Cbr> \u003Ci>如果你已经使用过，欢迎随时联系我们，申请免费访问权限。 :hibiscus:\u003C\u002Fi>\r\n  \r\n\r\n## 图例\r\n* :video_camera: 视频内容\r\n* :closed_book: 在线文章内容 \u002F 书籍\r\n### 💡 路线图说明 ▶️ [YouTube 视频](https:\u002F\u002Fyoutu.be\u002FHbIPJuvzRLk) :movie_camera:\u003Cimg src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FMoataz-Elmesmary_Data-Science-Roadmap_readme_1afdc385b897.gif\" width=50px height=40px>\r\n\r\n***\n\n## 🔰 初学者 🔰\n\n[算法书籍](https:\u002F\u002Fgithub.com\u002Fcjbt\u002FFree-Algorithm-Books\u002Fblob\u002Fmaster\u002Fbook\u002FGrokking%20Algorithms%20-%20An%20illustrated%20guide%20for%20programmers%20and%20other%20curious%20people.pdf) \u003Ci>每一段代码都可以称为算法，但本书涵盖了其中更有趣的部分。\u003C\u002Fi>\u003Cbr>\n[专项课程（数据结构与算法）](https:\u002F\u002Fwww.coursera.org\u002Fspecializations\u002Fdata-structures-algorithms)\n\n**1. 描述性统计** \u003Cimg src=\"https:\u002F\u002Fraw.githubusercontent.com\u002Fmrankitgupta\u002F66DaysOfData\u002Fc8c040f1c85d921db317152567f331354446286a\u002Fstatistics-21.svg\" alt=\"Statistics\" width=\"25\" height=\"25\"\u002F> \u003Cbr>\n   📹 [统计学导论 - DataCamp](https:\u002F\u002Fapp.datacamp.com\u002Flearn\u002Fcourses\u002Fintroduction-to-statistics)\u003Cbr>\n   📹 [描述性统计入门 - YouTube](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=ApEV6IupW7o&list=PLAwxTw4SYaPn22DmaF6x8JtG4TeWOJk_1&index=1) 老版Udacity课程\u003Cbr>\n   📹 [统计学基础 - StatQuest - YouTube](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLblh5JKOoLUK0FLuzwntyYI10UQFUhsY9)\u003Cbr>\n   📹 [统计学导论 - YouTube](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PL0KQuRyPJoe6KjlUM6iNYgt8d0DwI-IGR)\u003Cbr>\n   📕 [在线统计教育](http:\u002F\u002Fonlinestatbook.com\u002FOnline_Statistics_Education.pdf)\u003Cbr>\n   📹 阿拉伯语课程 [1](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=_Lg1QtwZHvk&list=PLO3fADoO5fwNTr4Zjmz-cacmMh1S0o4Ml&index=1) - [2](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=d5jh5mmwcKI&list=PLY99ZSsxRyJiu6kb4WRRpeEFqK1pAr-EO)\u003Cbr>\n   📹 [推断统计入门](https:\u002F\u002Fwww.udacity.com\u002Fcourse\u002Fintro-to-inferential-statistics--ud201)++\u003Cbr>\n   📕 [数据科学家实用统计学](https:\u002F\u002Fgithub.com\u002FMoataz-Elmesmary\u002FData-Science-Roadmap\u002Fblob\u002Fmain\u002FBooks\u002FPractical%20Statistics%20for%20Data%20Scientists.pdf)\u003Cbr>\n\n**2. 概率**\u003Cbr>\n   📹 [可汗学院](https:\u002F\u002Fwww.khanacademy.org\u002Fmath\u002Fstatistics-probability\u002Fprobability-library)\u003Cbr>\n   📹 [史蒂夫博士的概率训练营 - 2024年10月 - YouTube](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLMrJAkhIeNNR3sNYvfgiKgcStwuPSts9V)\u003Cbr>\n   📹 [阿拉伯语课程](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PL158D091D26F47358)\u003Cbr>\n   📹 [面向AI和DS的概率与统计 - 阿拉伯语（哈特姆·埃拉塔尔博士）](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLJM7jJIw2GC2Ihr__bRSeMxzsiFMZEsx7)\u003Cbr>\n   📕 [概率论导论](https:\u002F\u002Fdrive.google.com\u002Ffile\u002Fd\u002F15Y0oFNHQRls1qvQNvO3DFLJVhIZvUjTD\u002Fview?usp=sharing)\u003Cbr>\n\n**3. 编程语言**\u003Cbr>\n\n 🔹*R* - *非常适合可视化和统计分析。*\u003Cbr>\n           📹 [R语言入门（DataCamp）](https:\u002F\u002Fwww.datacamp.com\u002Fcourses\u002Ffree-introduction-to-r)\u003Cbr>\n           📹 [数据科学专项课程 - Coursera](https:\u002F\u002Fwww.coursera.org\u002Fspecializations\u002Fjhu-data-science)\u003Cbr>\n           📕 [R语言简介](https:\u002F\u002Fcran.r-project.org\u002Fdoc\u002Fmanuals\u002FR-intro.pdf)\u003Cbr>\n           📕 [R语言用于数据科学](https:\u002F\u002Fr4ds.had.co.nz\u002F)\u003Cbr>\n\n 🔹*Python*:100: \u003Cbr>\n           📹 [Python编程入门](https:\u002F\u002Fwww.udacity.com\u002Fcourse\u002Fintroduction-to-python--ud1110)\u003Cbr>\n           📹 [面向对象编程](https:\u002F\u002Flearn.datacamp.com\u002Fcourses\u002Fobject-oriented-programming-in-python)\u003Cbr>\n           📹 阿拉伯语 - [哈苏纳](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=MxYLqE3Ils8&list=PLHIfW1KZRIfnM9y0sQRwjVz2-IwvnEJep) | [埃尔佐罗](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=mvZHDpCHphk&list=PLDoPjvoNmBAyE_gei5d18qkfIe-Z8mocs)\u003Cbr>\n           📹 [Python完整课程 - FreeCodeCamp on YouTube](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=rfscVS0vtbw)\u003Cbr>\n           📕 [面向CS和数据科学的Python入门](https:\u002F\u002Fdrive.google.com\u002Ffile\u002Fd\u002F1rXkYFjw1iKbXCra_B4Ykm0AMRgo6v93w\u002Fview?fbclid=IwAR2lg9omGaAsG3g1ZhHQHja8_uxkZ7QddnOUSxfoceRXShU1V_bl4V63xCQ)\u003Cbr>\n           [更多关于面向对象编程的内容](https:\u002F\u002Fwww.futurelearn.com\u002Fcourses\u002Fobject-oriented-principles)\u003Cbr>\n**4. Pandas**\u003Cbr>\n           📹 [Corey Schafer-YouTube](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=ZyhVh-qRZPA&list=PL-osiE80TeTsWmV9i9c58mdDCSskIFdDS)\u003Cbr>\n           📕 [Kaggle](https:\u002F\u002Fwww.kaggle.com\u002Flearn\u002Fpandas)\u003Cbr>\n           📕 [文档](https:\u002F\u002Fpandas.pydata.org\u002Fpandas-docs\u002Fversion\u002F0.15\u002Ftutorials.html)\u003Cbr>\n           📹 [Data School-YouTube](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=yzIMircGU5I&list=PL5-da3qGB5ICCsgW1MxlZ0Hq8LL5U3u9y&index=1)\u003Cbr>\n           📹 [阿拉伯语课程](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=3ISW655DemU&list=PLvLvlVqNQGHCb2_ygmr1DQOMOv0yXp84F)\u003Cbr>\n           📹 PandasAI🐼[1](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=BtmMNZLxbuI) - [2](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=5w6eZaoDVVk) *通过将生成式AI功能集成到Pandas中来增强其能力。* \u003Cbr>\n**5. Numpy**\u003Cbr>\n           📕 [Kaggle](https:\u002F\u002Fwww.kaggle.com\u002Flegendadnan\u002Fnumpy-tutorial-for-beginners-data-science)  \u003Cimg src=\"https:\u002F\u002Fraw.githubusercontent.com\u002Fmrankitgupta\u002Fmrankitgupta\u002F2a582d085b324cff4917325112229027309ecae3\u002FNumpy-logo.svg\" alt=\"numpy\" width=\"25\" height=\"20\"\u002F>\u003Cbr>\n           📹 [Keith Galli的NumPy教程 - YouTube](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=5-5CrLmf2vk&list=PLIA_seGogbkGDYq-dnVCsELEIq_7HK7Ca)\u003Cbr>\n           📹 [阿拉伯语课程 - Elzero](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLUgz8T_NoatsJCH-DmieQhqhSL2WBvlm-)\u003Cbr>\n           📕 [教程](http:\u002F\u002Fcs231n.github.io\u002Fpython-numpy-tutorial\u002F)\u003Cbr>\n           📕 [文档](https:\u002F\u002Fnumpy.org\u002Fdoc\u002F1.18\u002Fuser\u002Fquickstart.html)\u003Cbr>\n**6. Scipy**\u003Cbr>\n           📕 [教程](https:\u002F\u002Fcs231n.github.io\u002Fpython-numpy-tutorial\u002F#scipy)\u003Cbr>\n           📕 [文档](https:\u002F\u002Fdocs.scipy.org\u002Fdoc\u002Fscipy\u002Freference\u002Ftutorial\u002Fgeneral.html)\u003Cbr>\n**7. 数据清洗**：成为优秀数据科学家所需掌握的**最重要**技能之一，你需要通过处理大量数据集来熟练掌握。\u003Cbr>\n           [阅读此文](https:\u002F\u002Ftowardsdatascience.com\u002Fthe-ultimate-guide-to-data-cleaning-3969843991d4)\u003Cbr>\n           📹 [课程1](https:\u002F\u002Fwww.datacamp.com\u002Fcourses\u002Fcleaning-data-in-python)\u003Cbr>\n           📕 [Notebook1](https:\u002F\u002Fwww.kaggle.com\u002Fbandiatindra\u002Ftelecom-churn-prediction)\u003Cbr\u002F>\n           📕 [Notebook2](https:\u002F\u002Fdrive.google.com\u002Fdrive\u002Ffolders\u002F1OQAEQ8rC4j6oBP7AyDU4bKpPr8sSStJI?fbclid=IwAR2dSrbyoZLM-Wm57yEYy8L8PmpPV9hqXdkNf-pURJC5C5xCz7UJB4YpJ7M)\u003Cbr\u002F>\n           📕 [Notebook3](https:\u002F\u002Fwww.kaggle.com\u002Fashishg21\u002Fdata-cleaning-and-some-analysis-shoe-prices)\u003Cbr>\n           📕 [Kaggle数据清洗](https:\u002F\u002Fwww.kaggle.com\u002Flearn\u002Fdata-cleaning)\u003Cbr>\n**8. 数据可视化** :bar_chart:\t\u003Cbr>\n           📹 [使用Matplotlib进行数据可视化导论](https:\u002F\u002Fapp.datacamp.com\u002Flearn\u002Fcourses\u002Fintroduction-to-data-visualization-with-matplotlib?fbclid=IwAR1OrJSdZ2LVD_c1o3d-_1I7Nhq8OZ3pzTu4010E_XWEmMc0KYsTosz8CIU) 或\u003Cbr>\n           📹 [Corey Schafer - YouTube播放列表](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=UO98lJQ3QGI&list=PL-osiE80TeTvipOqomVEeZ1HRrcEvtZB_) 或\u003Cbr>\n           📹 [sentdex - YouTube播放列表](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=q7Bo_J8x_dw&list=PLQVvvaa0QuDfefDfXb9Yf0la1fPDKluPF)\u003Cbr>\n           📕 [Kaggle使用Seaborn进行数据可视化](https:\u002F\u002Fwww.kaggle.com\u002Flearn\u002Fdata-visualization)\u003Cbr>\n           📹 [YouTube播放列表](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=z7ZINBk8EUk&list=PL998lXKj66MpNd0_XkEXwzTGPxY2jYM2d)\u003Cbr>\n           📹 [课程1：Seaborn数据可视化入门](https:\u002F\u002Flearn.datacamp.com\u002Fcourses\u002Fintroduction-to-data-visualization-with-seaborn)\u003Cbr>\n           📹 [课程2：Seaborn中级数据可视化](https:\u002F\u002Flearn.datacamp.com\u002Fcourses\u002Fintermediate-data-visualization-with-seaborn)\u003Cbr>\n           📹 [课程3：理解与用Python进行可视化](https:\u002F\u002Fwww.coursera.org\u002Flearn\u002Funderstanding-visualization-data)\u003Cbr>\n\n**9. EDA**\n注：已在上述概率课程中提及\u003Cbr>\n           📹 [DataCamp-Python中的EDA](https:\u002F\u002Flearn.datacamp.com\u002Fcourses\u002Fexploratory-data-analysis-in-python) \u003Cbr>\n           📹 [IBM-面向机器学习的EDA](https:\u002F\u002Fwww.coursera.org\u002Flearn\u002Fibm-exploratory-data-analysis-for-machine-learning) \u003Cbr>\n\n\u003Cimg align=\"right\" width=\"290\" height=\"203\" src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FMoataz-Elmesmary_Data-Science-Roadmap_readme_6f01a9e5954b.png\">\n\n**10. 仪表板**\u003Cbr>\n\n *Power BI*\u003Cbr>\n           📹 [Power BI - YouTube (Alex)](https:\u002F\u002Fyoutube.com\u002Fplaylist?list=PLUaB-1hjhk8HqnmK0gQhfmIdCbxwoAoys&si=pR4VSrR1P2O-AaBJ)\u003Cbr>\n           📹 [Power BI培训](https:\u002F\u002Fpowerbi.microsoft.com\u002Fen-us\u002Flearning\u002F)\u003Cbr>\n           📹 [阿拉伯语 - YouTube (Zanoon)](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=P_Nr0FMyn9w&list=PL69umUTzySPGWMxnmhX9SV5PIEbdnHv63&index=1)\u003Cbr>\n           📹 [阿拉伯语 - YouTube](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=ykvAWKML9Gk&list=PLof3yw6ZFPFhV75Ptf-5Q88bgUtLOBvOw)\u003Cbr>\n           📹 [Guy in a Cube - YouTube](https:\u002F\u002Fwww.youtube.com\u002F@GuyInACube\u002Ffeatured)\u003Cbr>\n  *Tableau* \u003Cimg src=\"https:\u002F\u002Fraw.githubusercontent.com\u002Fmrankitgupta\u002Fmrankitgupta\u002Fa768d6bf0a001f03327578ae12f8867e4056cbaf\u002Ftableau-software.svg\" alt=\"tableau\" width=\"20\" height=\"20\"\u002F>\u003Cbr>\n           📹 [Data With Baraa - YouTube](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=_TT1D3tH1_c&list=PLNcg_FV9n7qZJqrKcUUCWCWPYCrlcVm9v)\u003Cbr>\n           📕 [教程](https:\u002F\u002Fwww.datacamp.com\u002Fcommunity\u002Ftutorials\u002Fdata-visualisation-tableau)\u003Cbr>\n           📹 [Tableau培训](https:\u002F\u002Fwww.tableau.com\u002Flearn\u002Ftraining\u002F20201)\u003Cbr>\n           📹 [课程 - DataCamp](https:\u002F\u002Flearn.datacamp.com\u002Fcourses\u002Fintroduction-to-tableau)\u003Cbr>\n           📹 [Simplilearn - YouTube](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=SSq5NwsUNGI&list=PLEiEAq2VkUUJEvrsey26P-Bj4Vk6BLBVC)\u003Cbr>\n\n\n**11. SQL与数据库**\u003Cbr>\n            📹 用于数据分析的SQL（[Udacity](https:\u002F\u002Fwww.udacity.com\u002Fcourse\u002Fsql-for-data-analysis--ud198)-笔记[*l📋l*](https:\u002F\u002Fgithub.com\u002Fjulianjohannesen\u002FUdacity-SQL-Notes\u002Ftree\u002Fmain)或[simplilearn](https:\u002F\u002Fwww.simplilearn.com\u002Ffree-online-course-to-learn-sql-basics-skillup))\u003Cbr>\n            📹 [SQL入门](https:\u002F\u002Flearn.datacamp.com\u002Fcourses\u002Fintroduction-to-sql) **或** [IBM（用于数据科学的SQL）](https:\u002F\u002Fwww.coursera.org\u002Flearn\u002Fsql-data-science)\u003Cbr>\n            📹 [SQL中的关系数据库入门](https:\u002F\u002Flearn.datacamp.com\u002Fcourses\u002Fintroduction-to-relational-databases-in-sql)\u003Cbr>\n            📹 阿拉伯语课程（[理论](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PL37D52B7714788190) - [实践](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PL1DUmTEdeA6J6oDLTveTt4Z7E5qEfFluE)) Eldesouki\u003Cbr>\n            📹 阿拉伯语 - [ITI由Ramy工程师主讲](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLSGEGD0dbMKrvd5ppnyFLm7q3xEH97T-t) *高级* - *([实验室答案+笔记+全套资料](https:\u002F\u002Fgithub.com\u002FMoataz-Elmesmary\u002FITI-SQL-Labs))*\u003Cbr>\n            📹 阿拉伯语 - [用于数据分析的SQL](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=kb-_GbpH3sQ&t=38s) 由Ahmed Sami讲解\u003Cbr>\n            📹 [Data With Baraa - YouTube](https:\u002F\u002Fwww.youtube.com\u002F@DataWithBaraa\u002Fplaylists) - [[资料]](https:\u002F\u002Fdatawithbaraa.substack.com\u002Fp\u002Faccess-to-course-materials)\u003Cbr>\n            📹 [365 Data Science - SQL](https:\u002F\u002Fmega.nz\u002Ffolder\u002FwswGEIhb#tsqUggTZyfy5HyRWUkV9sg\u002Ffolder\u002FR1AxXCxB)\u003Cbr>\n            📹 [CMU数据库导论 - 2022年秋季](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLSE8ODhjZXjaKScG3l0nuOiDTTqpfnWFf) - *\u003C[时间表📅](https:\u002F\u002F15445.courses.cs.cmu.edu\u002Ffall2022\u002Fschedule.html)>* - [书📕](https:\u002F\u002Fgithub.com\u002FMoataz-Elmesmary\u002FData-Science-Roadmap\u002Fblob\u002Fmain\u002FBooks\u002FAbraham%20Silberschatz%2C%20Henry%20Korth%20and%20S.%20Sudarshan%20-%20Database%20System%20Concepts.%207-McGraw-Hill%20Education%20(2020).pdf)\u003Cbr>\n            📕 [用于数据分析的SQL](https:\u002F\u002Fgithub.com\u002FMoataz-Elmesmary\u002FData-Science-Roadmap\u002Fblob\u002Fmain\u002FBooks\u002FSQL%20for%20Data%20Analysis.pdf)\u003Cbr>\n            📝 练习 [InterviewMaster](https:\u002F\u002Fwww.interviewmaster.ai\u002F) 和 [HackerRank](https:\u002F\u002Fwww.hackerrank.com\u002Fdomains\u002Fsql) 以及 [LeetCode](https:\u002F\u002Fleetcode.com\u002Fstudyplan\u002Ftop-sql-50\u002F) 和 [DataLemur](https:\u002F\u002Fdatalemur.com\u002F)\n\n**12. DWH**：*用于报表的系统——商业智能的核心组成部分。*\u003Cbr>\n     *主要由数据工程师使用。*\u003Cbr>\n            📕 [数据仓库工具包](https:\u002F\u002Fgithub.com\u002FMoataz-Elmesmary\u002FData-Science-Roadmap\u002Fblob\u002Fmain\u002FBooks\u002FThe-Data-Warehouse-Toolkit-3rd-Edition.pdf)\u003Cbr>\n            📹 [数据仓储教程视频](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PL9ooVrP1hQOEDSc5QEbI8WYVV_EbWKJwX)\u003Cbr>\n            📹 [Garage Education](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLxNoJq6k39G_Ffv8Na1oRbob0sVHfFc_T) (阿)\u003Cbr>\n            📹 [阿拉伯语的数据仓库实施](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PL1565idytjOTwGN63vZK7lNK6pVXpGo3s) (阿)\u003Cbr>\n            📹 [更多阿拉伯语内容？](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLx5yn1EeCC_6ampJnoF2hHnHMj_-EGkU4) (阿)\u003Cbr>\n            📹 [科罗拉多大学的数据仓库](https:\u002F\u002Fwww.coursera.org\u002Flearn\u002Fdwdesign)\u003Cbr>\n            📹 **[SSIS]** [SQL Server集成服务](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLgOQg5m1pmp84jmXHGNWWYuU3m4bNCmfs) (阿)\u003Cbr>\n            📹 [项目 - 使用SSIS构建销售数据集市](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLcAbhg_RWLaLUaYpAAvOLu2hlyVgZlRjb) (阿)\u003Cbr>\n            📹 [项目 - 逐步构建DWH](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLcAbhg_RWLaLUaYpAAvOLu2hlyVgZlRjb) (阿)\u003Cbr>\n            📹 [项目 - 创建DWH事实与维度](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=8TSUoolAk2I) (阿)\u003Cbr>\n            📹 [在SSIS中实现SCD](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=7uj463csru0) *继续播放列表*\u003Cbr>\n            📹 [CDC在SSIS中的教程](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=QVF1JGFFt8w)\u003Cbr>\n\n**13. Python正则表达式**\u003Cbr>\n           📕 [教程](https:\u002F\u002Fwww.datacamp.com\u002Fcommunity\u002Ftutorials\u002Fpython-regular-expression-tutorial)\u003Cbr>\n           📹 [Corey的正则表达式 - YouTube](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLDoPjvoNmBAyE_gei5d18qkfIe-Z8mocs)\u003Cbr>\n           📹 [阿拉伯语课程 - Elzero](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLDoPjvoNmBAyE_gei5d18qkfIe-Z8mocs) *从第95集开始。* \u003Cbr>\n\n**14. 时间序列分析**\u003Cbr>\n            📹 [Track - DataCamp](https:\u002F\u002Flearn.datacamp.com\u002Fskill-tracks\u002Ftime-series-with-python)\u003Cbr>\n            📹 [课程 - Coursera](https:\u002F\u002Fwww.coursera.org\u002Flearn\u002Fpractical-time-series-analysis)\u003Cbr>\n            📕 [书籍](https:\u002F\u002Fwww.oreilly.com\u002Flibrary\u002Fview\u002Fpractical-time-series\u002F9781492041641\u002F?fbclid=IwAR20cq7hAdWf6voOd61u-pNzZCHvB0rZhT_BUoGTAXxPBhhi82p8BhxLEsI)\u003Cbr>\n            📕 [fbprohet](https:\u002F\u002Ffacebook.github.io\u002Fprophet\u002Fdocs\u002Fquick_start.html)\u003Cbr>\n            📹 阿拉伯语来源 [视频1](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=TvhaHPq6xLU&list=TLPQMjYwNzIwMjEPGXX6392WJA&index=1) & [视频2](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=mipF7mRVpk0&list=TLPQMjYwNzIwMjEPGXX6392WJA&index=2)\u003Cbr>\n  \n\u003Ch4>\u003Ci>在初学者阶段结束时，将所学知识应用于一个项目中。\u003C\u002Fi>\u003C\u002Fh4>\n\n***\n\n## 🔰 中级 🔰\n\n**1. 机器学习数学基础**: 包括线性代数、微积分和主成分分析。 \u003Cbr>\n📹 [机器学习与数据科学数学 - 吴恩达](https:\u002F\u002Fwww.coursera.org\u002Fspecializations\u002Fmathematics-for-machine-learning-and-data-science?irclickid=zzy33K1O0xyNUAmxqWUjDwedUkAUtSWUJXKyTY0&irgwc=1&utm_medium=partners&utm_source=impact&utm_campaign=3117765&utm_content=b2c#courses)\u003Cbr>\n📹 [专项课程](https:\u002F\u002Fwww.coursera.org\u002Fspecializations\u002Fmathematics-machine-learning)\u003Cbr>\n📹 [机器学习数学 - 大部分所需的基础知识](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=vLJcduC4lBM&list=PLcQCwsZDEzFmlSc6levE3UV9rZ8yY-D_7)\u003Cbr>\u003Cbr>\n:small_blue_diamond:线性代数\u003Cbr>\n            📹 [可汗学院 - 线性代数](https:\u002F\u002Fwww.khanacademy.org\u002Fmath\u002Flinear-algebra)\u003Cbr>\n            📹 [机器学习中的线性代数](https:\u002F\u002Fwww.coursera.org\u002Flearn\u002Flinear-algebra-machine-learning)\u003Cbr>\n            📹 [3Blue1Brown - 线性代数的本质](https:\u002F\u002Fwww.3blue1brown.com\u002Ftopics\u002Flinear-algebra)\u003Cbr>\n:small_blue_diamond:微积分\u003Cbr>\n            📹 [多元微积分 - Coursera](https:\u002F\u002Fwww.coursera.org\u002Flearn\u002Fmultivariate-calculus-machine-learning?fbclid=IwAR243aoz0jxs4iUn539pnjSQliXtr7Y5QAsvgeRTietZT_tkyoRU3b6Sq1o)\u003Cbr>\n            📹 [微积分的本质 - YouTube](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLZHQObOWTQDMsr9K-rj53DwVRMYO3t5Yr)\u003Cbr>\n:small_blue_diamond:PCA\u003Cbr>\n            📹 [PCA - Coursera](https:\u002F\u002Fwww.coursera.org\u002Flearn\u002Fpca-machine-learning)\u003Cbr>\n\n\n**2. 机器学习**\u003Cbr>\n            📹 [Coursera - 吴恩达旧课程（Octave\u002FMatlab）](https:\u002F\u002Fwww.coursera.org\u002Flearn\u002Fmachine-learning)\u003Cbr>\n            📹 [Coursera 吴恩达新机器学习专项课程（Python）](https:\u002F\u002Fwww.coursera.org\u002Fspecializations\u002Fmachine-learning-introduction?_hsenc=p2ANqtz-_R9x3Nm07uCw6YAw9VpCpdjRdfaUFyxdOcvgDljRt7j_NXiahN1plnI_Ob9jn0jSNipuE_Y08llrfPSt_1P7EBvj4LuImpBTKG3bsR6Z9bzjzBoRY&_hsmi=216611333&action=enroll&utm_campaign=mls-launch-2022&utm_content=216613012&utm_medium=email&utm_source=hs_email#courses)\u003Cbr>\n            📹 [机器学习 - StatQuest - YouTube](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLblh5JKOoLUICTaGLRoHQDuF_7q2GfuJF)\u003Cbr>\n            📹 [斯坦福大学完整机器学习课程 - 吴恩达](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=PPLop4L2eGk&list=PLLssT5z_DsK-h9vYZkQkYNWcItqhlRJLN)\u003Cbr>\n            📹 [2019年春季滑铁卢大学 CS480\u002F680 机器学习导论](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLdAoL1zKcqTW-uzoSVBNEecKHsnug_M0k)\u003Cbr>\n            📹 [SYDE 522 – 机器智能（2018年冬季，滑铁卢大学）](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PL4upCU5bnihwCX93Gv6AQnKmVMwx4AZoT)\u003Cbr>\n            📹 [2022年工程师机器学习](https:\u002F\u002Fapmonitor.com\u002Fpds\u002F) \u002F ([YouTube](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=Gh5rbBLh4JY&list=PLLBUgWXdTBDg1K1bu60lHypSzSP-WSBmx))\u003Cbr>\n            📹 [Udacity 机器学习入门课程](https:\u002F\u002Fwww.udacity.com\u002Fcourse\u002Fintro-to-machine-learning--ud120)\u003Cbr>\n            📹 [Hesham Asem - 阿拉伯语内容](https:\u002F\u002Fwww.youtube.com\u002Fc\u002FHeshamAsem\u002Fplaylists)\u003Cbr>\n            📹 [IBM 使用 Python 的机器学习](https:\u002F\u002Fwww.coursera.org\u002Flearn\u002Fmachine-learning-with-python)\u003Cbr>\n            📹 [从零开始的机器学习 - YouTube（Python 工程师）](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=ngLyX54e1LU&list=PLqnslRFeH2Upcrywf-u2etjdxxkL8nl7E)\u003Cbr>\n            📕 动手学机器学习（[第一版](https:\u002F\u002Fdrive.google.com\u002Ffile\u002Fd\u002F1uro1p6SlYolSkF0fbFKau0pOQ9ENZqny\u002Fview?usp=sharing) & [第二版](https:\u002F\u002Fdrive.google.com\u002Ffile\u002Fd\u002F1rS95FTNfiVG4WjGnPjd73GqrmEKey4N1\u002Fview?usp=sharing) & [第三版](https:\u002F\u002Fdrive.google.com\u002Ffile\u002Fd\u002F11VeqPJw8s9SC9Ru7IVeQhiTyV_9TliOE\u002Fview?usp=sharing)）| 代码：[![在 Github 上查看](https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FExamples-Notebooks-orange?logo=Github)](https:\u002F\u002Fgithub.com\u002Fageron\u002Fhandson-ml?fbclid=IwAR3s31KlwkLKyrEwuEd4UMOcvHN1Q9Z2LLGzPg5vP4UKSwjriHxU0uO405c)\u003Cbr>\n            📹 [机器学习算法实战](https:\u002F\u002Fwww.coursera.org\u002Fspecializations\u002Fmachine-learning-algorithms-real-world?utm_medium=email&utm_source=marketing&utm_campaign=A39CcMUuEempyReieZALEQ)\u003Cbr>\n            📹 [机器学习科学家](https:\u002F\u002Flearn.datacamp.com\u002Fcareer-tracks\u002Fmachine-learning-scientist-with-python?version=1)\u003Cbr>\n            📹 [项目](https:\u002F\u002Fwww.coursera.org\u002Flearn\u002Fapplied-data-science-capstone)\u003Cbr>\n\n**3. 网页抓取\u002FAPI**\u003Cbr>\n            📹 [课程](https:\u002F\u002Flearn.datacamp.com\u002Fcourses\u002Fweb-scraping-with-python)\u003Cbr>\n            📕 [入门2](https:\u002F\u002Fwww.dataquest.io\u002Fblog\u002Fweb-scraping-tutorial-python\u002F)\u003Cbr>\n            📕 [教程](https:\u002F\u002Frealpython.com\u002Fbeautiful-soup-web-scraper-python\u002F)\u003Cbr>\n            📕 [两主题合集](https:\u002F\u002Fb-ok.africa\u002Fbook\u002F3515980\u002F5d50aa)\u003Cbr>\nAPIs \u003Cbr>\n            📕 [教程](https:\u002F\u002Fwww.dataquest.io\u002Fblog\u002Fpython-api-tutorial\u002F)\u003Cbr>\n            📕 [文章](https:\u002F\u002Fmedium.com\u002Fm\u002Fglobal-identity?redirectUrl=https%3A%2F%2Ftowardsdatascience.com%2Fhow-to-pull-data-from-an-api-using-python-requests-edcc8d6441b1)\u003Cbr>\n            📕 [教程](https:\u002F\u002Frapidapi.com\u002Fblog\u002Fhow-to-use-an-api-with-python\u002F)\u003Cbr>\n**4. 统计学。**\u003Cbr>\n            📕 [这本统计学 - 书籍](https:\u002F\u002Fgreenteapress.com\u002Fthinkstats\u002Fthinkstats.pdf)\u003Cbr>\n            📕 [思考贝叶斯 - 书籍](https:\u002F\u002Fwww.greenteapress.com\u002Fthinkbayes\u002Fthinkbayes.pdf)\u003Cbr>\n**5. 高级 SQL**\u003Cbr>\n            📹 [SQL 数据连接 - DataCamp](https:\u002F\u002Flearn.datacamp.com\u002Fcourses\u002Fjoining-data-in-postgresql)\u003Cbr>\n            📹 [中级 SQL - DataCamp](https:\u002F\u002Flearn.datacamp.com\u002Fcourses\u002Fjoining-data-in-postgresql)\u003Cbr>\n            📹 [更高级的 SQL](https:\u002F\u002Fwww.coursera.org\u002Flecture\u002Fdata-driven-astronomy\u002Fmore-advanced-sql-GDmo5)\u003Cbr>\n\n**7. 特征工程**\u003Cbr>\n            📕 [教程](https:\u002F\u002Fwww.kaggle.com\u002Flearn\u002Ffeature-engineering)\u003Cbr>\n            📕 [文章](https:\u002F\u002Fwww.medium.com\u002Fm\u002Fglobal-identity?redirectUrl=https%3A%2F%2Ftowardsdatascience.com%2Ffeature-engineering-for-machine-learning-3a5e293a5114)\u003Cbr>\n            📕 [书籍](https:\u002F\u002Fdrive.google.com\u002Ffile\u002Fd\u002F1BkJYO0tqMYptTWUDQ7X0vd2aygohHRm8\u002Fview?usp=sharing)\u003Cbr>\n**8. 解释基于 Shapley 值的机器学习模型。**\u003Cbr\u002F>\n           📕 [SHAP](https:\u002F\u002Fshap.readthedocs.io\u002Fen\u002Flatest\u002F)\u003Cbr\u002F>\n           📕 [Kaggle 机器学习可解释性](https:\u002F\u002Fwww.kaggle.com\u002Flearn\u002Fmachine-learning-explainability)\u003Cbr\u002F>\n\u003Ch4>\u003Ci>完成这一阶段后，申请参与2到3个规模较大的项目。\u003C\u002Fi>\u003C\u002Fh4>\n\n\u003Ci>请阅读这本书\u003C\u002Fi> :open_book: [R语言应用的统计学习导论](https:\u002F\u002Fgithub.com\u002FMoataz-Elmesmary\u002FData-Science-Roadmap\u002Fblob\u002Fmain\u002FBooks\u002FIntroduction%20to%20Statistical%20Learning%20with%20Applications%20in%20R.pdf) 我说你读它\u003Cimg src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FMoataz-Elmesmary_Data-Science-Roadmap_readme_9035c3ba63ff.gif\" width=\"35\">\u003Cbr>\n***\n\n## 🔰 高级 🔰\n\n**1. 深度学习** \u003Cbr>\n           📹 [深度学习基础](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLZbbT5o_s2xq7LwI2y8_QtvuXZedL6tQU)\u003Cbr>\n           📹 [麻省理工学院深度学习导论](http:\u002F\u002Fintrotodeeplearning.com\u002F?fbclid=IwAR35rIygYlCn84DV7mlHvdvs4sMUm2D6RLYVwFpp2nT2t1Zj1GGy3QAWQvQ)\u003Cbr>\n           📹 [专项课程](https:\u002F\u002Fwww.coursera.org\u002Fspecializations\u002Fdeep-learning)\u003Cbr>\n           📕 [深入浅出深度学习（英文版）](https:\u002F\u002Fd2l.ai\u002Fd2l-en.pdf?fbclid=IwAR0sVdA8VFYpNZCpYZHgo_kl_HYrjcjDfjEka26D8xRWAhbhh6mmSNIXg3U) | （阿拉伯语版）：arrow_right:[第一部分](https:\u002F\u002Fdrive.google.com\u002Ffile\u002Fd\u002F1SrmT_r8dNK42IqyS0gwXtbLCZbk5G8eu\u002Fview?fbclid=IwAR1Xcf8PNKkPJMg0uHRE1QyIW4_BMxISIdoB8pPaepw38njhaIf04MYM218) & [第二部分](https:\u002F\u002Fdrive.google.com\u002Ffile\u002Fd\u002F1UqEu0amRfAvJD0L1HosIn3UJi0FkNemU\u002Fview?fbclid=IwAR1og8pkWr1gT3jdUwqikCZVrOCpyrm0x6ZRL63Kitwhki35pazHdo_ScJI) \u003Cbr>\n           📹 [加州大学伯克利分校深度学习课程](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLZSO_6-bSqHQHBCoGaObUljoXAyyqhpFW)\u003Cbr>\n           📕 [《深入浅出深度学习》GitHub仓库](https:\u002F\u002Fgithub.com\u002Fd2l-ai\u002Fd2l-en?fbclid=IwAR0QN35b-NHHWq_zKISA1cbI063aRqqoKqR_0e3cpnT5h58GkcNbCIJs3iw)\u003Cbr>\n           📹 [斯坦福大学讲座——用于视觉识别的卷积神经网络](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=vT1JzLTH4G4&list=PL3FW7Lu3i5JvHM8ljYj-zLfQRF3EO8sYv)\u003Cbr>\n           📹 [滑铁卢大学机器学习\u002F深度学习课程](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLdAoL1zKcqTW-uzoSVBNEecKHsnug_M0k)\u003Cbr>\n           📕 [使用fastai和PyTorch进行编码的深度学习](https:\u002F\u002Fdl.ebooksworld.ir\u002Fbooks\u002FDeep.Learning.for.Coders.with.fastai.and.PyTorch.Howard.Gugger.OReilly.9781492045526.EBooksWorld.ir.pdf)\u003Cbr>\n\n\n**2. TensorFlow**\u003Cbr>\n           📹 [专项课程](https:\u002F\u002Fwww.coursera.org\u002Fspecializations\u002Ftensorflow-in-practice)\u003Cbr>\n           📹 [YouTube频道](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLZbbT5o_s2xrwRnXk_yCPtnqqo4_u2YGL)\u003Cbr>\n            [fast.ai的深度学习课程](https:\u002F\u002Fwww.fast.ai\u002F)\u003Cbr> \n ###### \u003Ci>TensorFlow在模型可视化和部署方面优于PyTorch。如果你需要更高的灵活性、调试能力和更短的训练时间，则选择PyTorch。\u003C\u002Fi>\n\n**3. PyTorch**\u003Cbr>\n           📹 [PyTorch（UC Berkeley - YouTube）- 第3讲（共5部分）](https:\u002F\u002Fm.youtube.com\u002Fwatch?v=AOypIa_8RXg&list=PL_iWQOsE6TfXxKgI1GgyV1B_Xa0DxE5eH&index=11)\u003Cbr>\n           📹 [数据科学博士 - YouTube](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=vVQs4h6HUvA&list=PLLeO8f6PhlKb_FAC7qxOBtxT9-8EPDAqk)\u003Cbr>\n           📹 [Aladdin的PyTorch教程 - YouTube](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=2S1dgHpqCdk&list=PLhhyoLH6IjfxeoooqP9rhU3HJIAVAJ3Vz)\u003Cbr>\n           📹 [2022年PyTorch课程 - YouTube](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=v43SlgBcZ5Y&list=PLkdGijFCNuVk9fO1IMfdV1Igob0FUHhkB)\u003Cbr>\n           📕 [使用PyTorch进行深度学习](https:\u002F\u002Fdrive.google.com\u002Ffile\u002Fd\u002F1-KG_ufeg7zw2iLgG5RrJSFpyonLwulgF\u002Fview?usp=sharing)\u003Cbr>\n           📕 [使用PyTorch和Scikit-Learn的机器学习 - 2022年](https:\u002F\u002Fdl.ebooksworld.ir\u002Fbooks\u002FMachine.Learning.with.PyTorch.and.Scikit-Learn.Sebastian.Raschka.Packt.9781801819312.EBooksWorld.ir.pdf)\u003Cbr>\n\n**4. 高级数据科学**\u003Cbr>\n           📹 [IBM高级数据科学专项课程](https:\u002F\u002Fwww.coursera.org\u002Fspecializations\u002Fadvanced-data-science-ibm) *包含Apache Spark*\u003Cbr>\n ☠️*高级机器学习主题🧠 | 讲座（YouTube）* \u003Cbr>\n           📹 [斯坦福CS330：深度多任务与元学习 I 秋季2022](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLoROMvodv4rNjRoawgt72BBNwL2V7doGI) - [资料](https:\u002F\u002Fcs330.stanford.edu\u002F)\u003Cbr>\n           📹 [麻省理工学院18.409机器学习的算法方面 春季2015](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLB3sDpSRdrOvI1hYXNsa6Lety7K8FhPpx)\u003Cbr>\n ☠️*基于机器学习的计算机视觉 | 讲座（YouTube）* \u003Cbr>\n           📹 [UC Berkeley CS 198-126：现代计算机视觉 秋季2022](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLzWRmD0Vi2KVsrCqA4VnztE4t71KnTnP5)\u003Cbr>\n           📹 [印度理工学院卡拉格布尔分校：面向视觉计算的深度学习](https:\u002F\u002Fnptel.ac.in\u002Fcourses\u002F108105103)\u003Cbr>\n           📹 [密歇根大学计算机视觉深度学习](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PL5-TkQAfAZFbzxjBHtzdVCWE0Zbhomg7r)\u003Cbr>\n\n**5. 自然语言处理** \u003Cbr>\n           📹 [Coursera专项课程 - 自然语言处理](https:\u002F\u002Fwww.coursera.org\u002Fspecializations\u002Fnatural-language-processing)\u003Cbr>\n           📹 [阿拉伯语 - Ahmed El Sallab](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLxmZ0b-n395VxzEUL8-Dy257zSqYZe4yU)\u003Cbr>\n           📹 [斯坦福CS224N讲座 - 冬季2021 - YouTube](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?app=desktop&list=PLoROMvodv4rMFqRtEuo6SGjY4XbRIVRd4&si=k91y-bepIiPjHMrj&fbclid=IwAR2h6KcYboHCjG9YBIEB08srgYSesqZ5UHXr0ni8yxOqrxNV3-_TGxq0Csg)\u003Cbr>\n           📹 [斯坦福XCS224U讲座 - 春季2021 - YouTube](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?app=desktop&list=PLoROMvodv4rMFqRtEuo6SGjY4XbRIVRd4&si=k91y-bepIiPjHMrj&fbclid=IwAR2h6KcYboHCjG9YBIEB08srgYSesqZ5UHXr0ni8yxOqrxNV3-_TGxq0Csg)\u003Cbr>\n           📹 [Python中的自然语言处理基础](https:\u002F\u002Fwww.datacamp.com\u002Fcourses\u002Fnatural-language-processing-fundamentals-in-python)\u003Cbr>\n 🔸*LLMs [什么是大型语言模型](https:\u002F\u002Fwww.snowflake.com\u002Fguides\u002Fwhat-large-language-model-and-what-can-llms-do-data-science)?* \u003Cbr>\n           📹 [安德鲁·吴的“人人可用的生成式AI” - Coursera](https:\u002F\u002Fwww.coursera.org\u002Flearn\u002Fgenerative-ai-for-everyone?utm_campaign=genai4e-launch&utm_medium=institutions&utm_source=deeplearning-ai#modules)🆕\u003Cbr>\n           📹 [使用LLMs的生成式AI](https:\u002F\u002Fwww.deeplearning.ai\u002Fcourses\u002Fgenerative-ai-with-llms\u002F)\u003Cbr>\n           📹 [斯坦福CS236：深度生成模型 I 2023 - YouTube](https:\u002F\u002Fwww.deeplearning.ai\u002Fcourses\u002Fgenerative-ai-with-llms\u002F)\u003Cbr>\n           📹 [斯坦福CS25：Transformer联合 2023 - YouTube](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLoROMvodv4rNiJRchCzutFw5ItR_Z27CM)\u003Cbr>\n           📹 [滑铁卢大学关于基础模型的最新进展 - 冬季2024](https:\u002F\u002Fcs.uwaterloo.ca\u002F~wenhuche\u002Fteaching\u002Fcs886\u002F)\u003Cbr>\n           📹 [加州大学伯克利分校理解LLMs的基础与安全 - 春季2024 - YouTube](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLJ66BAXN6D8H_gRQJGjmbnS5qCWoxJNfe)\u003Cbr>\n           📹 [LLM基础](https:\u002F\u002Ffullstackdeeplearning.com\u002Fllm-bootcamp\u002Fspring-2023\u002Fllm-foundations\u002F)\u003Cbr>\n           📹 ChatGPTs \u002F Transformers是如何工作的？[1](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=bQ5BoolX9Ag) - [2](https:\u002F\u002Fjalammar.github.io\u002Fhow-gpt3-works-visualizations-animations\u002F) - [3](https:\u002F\u002Fjalammar.github.io\u002Fillustrated-transformer\u002F) *概述及背后的数学原理*\u003Cbr>\n           📹 [提示工程](https:\u002F\u002Ffullstackdeeplearning.com\u002Fllm-bootcamp\u002Fspring-2023\u002Fprompt-engineering\u002F) | ([阿拉伯语](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=A-sNuzZgY8g&list=PLvLvlVqNQGHDNUshQJBWWCIRGgC0PN7VL)) *如果你想充分发挥LLMs的潜力*\u003Cbr>\n           📹 [LLMOps](https:\u002F\u002Ffullstackdeeplearning.com\u002Fllm-bootcamp\u002Fspring-2023\u002Fllmops\u002F) *一堂课全面讲解整个LLM流程*\u003Cbr>\n\n\n**6. 推断统计学** \u003Cbr>\n\u003Cimg align=\"right\" width=\"158\" height=\"200\" src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FMoataz-Elmesmary_Data-Science-Roadmap_readme_c8dccf082d9b.png\">\n           📹 [专项课程，第2和第3门课](https:\u002F\u002Fwww.coursera.org\u002Fspecializations\u002Fstatistics-with-python)\u003Cbr>\n           📹 [课程](https:\u002F\u002Fwww.coursera.org\u002Flearn\u002Fstatistical-inferences)\u003Cbr>\n**7. 贝叶斯统计学**\u003Cbr>\n           📹 [1 - 从概念到数据分析](https:\u002F\u002Fwww.coursera.org\u002Flearn\u002Fbayesian-statistics)\u003Cbr>\n           📹 [2 - 技术与模型](https:\u002F\u002Fwww.coursera.org\u002Flearn\u002Fmcmc-bayesian-statistics)\u003Cbr>\n           📹 [3 - 混合模型](https:\u002F\u002Fwww.coursera.org\u002Flearn\u002Fmixture-models)\u003Cbr>\n**8. 模型部署** \u003Cbr>\n           📕 [Flask教程](https:\u002F\u002Ftowardsdatascience.com\u002Fdeploying-a-deep-learning-model-using-flask-3ec166ef59fb)\u003Cbr>\n           📹 [TensorFlow：数据与部署专项课程](https:\u002F\u002Fwww.coursera.org\u002Fspecializations\u002Ftensorflow-data-and-deployment)\u003Cbr>\n           📹 [使用TensorFlow Serving和Flask部署模型](https:\u002F\u002Fwww.coursera.org\u002Fprojects\u002Fdeploy-models-tensorflow-serving-flask)\u003Cbr>\n           📹 [如何将机器学习模型部署到Google Cloud - Daniel Bourke](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=fw6NMQrYc6w)\u003Cbr>\n           如果你对更多部署方法感兴趣，请搜索 (_FastAPI - Heroku - chitra_)\u003Cbr>\n        \n**9. MLOps**：是模型部署、模型服务、模型监控和模型维护的结合。       \n           🔗 [MLOps-zoomcamp](https:\u002F\u002Fgithub.com\u002FDataTalksClub\u002Fmlops-zoomcamp)\u003Cbr>\n           🔗 [MLOps指南](https:\u002F\u002Fgithub.com\u002FNyandwi\u002Fmachine_learning_complete\u002Fblob\u002Fmain\u002F010_mlops\u002F1_mlops_guide.md)\u003Cbr>\n           📕 [实用MLOps](https:\u002F\u002Fdrive.google.com\u002Ffile\u002Fd\u002F17RhXQ2ix6rFMaas3HI7bnM_GL8lS7u3f\u002Fview?usp=sharing)\u003Cbr>\n**10. 概率图模型**    \n           📹 [Coursera专项课程 - 概率图模型](https:\u002F\u002Fwww.coursera.org\u002Fspecializations\u002Fprobabilistic-graphical-models)\u003Cbr>\n           📹 [犹他大学春季2016 - YouTube](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLbuogVdPnkCpvxdF-Gy3gwaBObx7AnQut)\u003Cbr>\n\u003Cp align=\"center\">\n\u003Cimg src=\"https:\u002F\u002Fi.imgur.com\u002FdBaSKWF.gif\" height=\"20\" width=\"100%\">\n\n:star2:\t\u003Ci>请阅读这些书籍，它们对你将大有裨益。\u003C\u002Fi>\u003Cbr>\n  :open_book: [贝叶斯推理与机器学习](https:\u002F\u002Fdrive.google.com\u002Ffile\u002Fd\u002F18fh0orqSNAaIyhLkVwh9cGuWBywCBbuw\u002Fview?usp=sharing)\u003Cbr>\n  :open_book: [统计学习要素](https:\u002F\u002Fdrive.google.com\u002Ffile\u002Fd\u002F1ePRkuB9Zm5Fkw-1-VG8prQXfj8pI6dWX\u002Fview?usp=sharing)\u003Cbr>\n  :open_book: [模式识别与机器学习 - Bishop](https:\u002F\u002Fdrive.google.com\u002Ffile\u002Fd\u002F1QkQj_azL6O7qUzshB8lPzueYWj0TRwEu\u002Fview?usp=sharing) (进阶)\u003Cbr>\n#####    \u003Ci>由[Eng.Mohamed Hammad](https:\u002F\u002Fwww.linkedin.com\u002Fposts\u002Fmohamed-hammad-a720a622_%D9%83%D8%AA%D8%A7%D8%A8-%D9%83%D9%84-%D9%85%D8%B1%D9%87-%D8%A7%D8%AD%D8%AA%D8%A7%D8%AC%D9%87-%D9%88%D8%A7%D8%B1%D8%AC%D8%B9%D9%84%D9%87-%D8%A7%D8%A8%D9%82%D9%8A-%D8%B9%D8%A7%D9%88%D8%B2-%D9%83%D9%84-%D8%A7%D9%84%D9%84%D9%8A-activity-7080526619525693441-nNn0?utm_source=share&utm_medium=member_desktop)推荐。\u003C\u002Fi> \u003Cimg src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FMoataz-Elmesmary_Data-Science-Roadmap_readme_1afdc385b897.gif\" width=50px height=40px>\n***\n\u003Cimg align=\"right\" width=\"309\" height=\"250\" src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FMoataz-Elmesmary_Data-Science-Roadmap_readme_fbe1bfc07ae5.png\">\n\u003Ch3> 📌项目 ⏬\u003C\u002Fh3>\u003Cbr>\n\n   🎥[Deena Gergis - 端到端项目](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLatl6hdtJ0RnbkReSAuel6PeCPO155FpG)\u003Cbr>\n   🎥[机器学习项目 - YouTube](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=fiz1ORTBGpY&list=PLfFghEzKVmjvuSA67LszN1dZ-Dd_pkus6)\u003Cbr>\n   💻[适合初学者的十大数据科学项目](https:\u002F\u002Fwww.kdnuggets.com\u002F2021\u002F06\u002Ftop-10-data-science-projects-beginners.html)\u003Cbr>\n   💻[12个适合初学者和专家的数据科学项目](https:\u002F\u002Fbuiltin.com\u002Fdata-science\u002Fdata-science-projects)\u003Cbr>\n   💻[数据科学项目与创意](https:\u002F\u002Fnevonprojects.com\u002Fdata-science-projects-solutions\u002F)\u003Cbr>\n   💻[2023年超过310个机器学习项目创意](https:\u002F\u002Fdata-flair.training\u002Fblogs\u002Fmachine-learning-project-ideas\u002F)\u003Cbr>\n   💻[10个端到端指导的数据科学项目，用于构建你的作品集](https:\u002F\u002Fpub.towardsai.net\u002F10-end-to-end-guided-data-science-projects-to-build-your-portfolio-b7b9047fe6c9)\u003Cbr>\n   🎥[使用Scikit Learn的真实世界ML教程](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=M9Itm95JzL0)\u003Cbr>\n   💻[数据科学中的Python代码](https:\u002F\u002Fgithub.com\u002FRubensZimbres\u002FRepo-2017\u002F)\u003Cbr>\n   🎥[使用Docker、GitHub Actions和部署的端到端ML项目](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=MJ1vWb1rGwM)\u003Cbr>\n   💻[12个免费的数据科学项目，用于练习Python和Pandas（可在线交互解决）](https:\u002F\u002Fwww.datawars.io\u002Farticles\u002F12-free-data-science-projects-to-practice-python-and-pandas)\u003Cbr>\n\n***\n\u003Ch3>📌 常用工具 ⤵️\u003C\u002Fh3>\u003Cbr>\n\u003Cimg align=\"right\" width=\"158\" height=\"85\" src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FMoataz-Elmesmary_Data-Science-Roadmap_readme_9c50ca7f68af.png\">\n\n英语 | 阿拉伯语 | 书籍\n--- | --- | ---\n:movie_camera: [Git - Udacity](https:\u002F\u002Fwww.udacity.com\u002Fcourse\u002Fversion-control-with-git--ud123) | :movie_camera: [Shakhab wa Anta Mutmain ](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=Q6G-J54vgKc)🚀 | :closed_book: [Pro Git](https:\u002F\u002Fgit-scm.com\u002Fbook\u002Fen\u002Fv2)\n📖 [w3schools](https:\u002F\u002Fwww.w3schools.com\u002Fgit\u002F) | :movie_camera: [almadrasa](https:\u002F\u002Falmdrasa.com\u002Ftracks\u002Fprogramming-foundations\u002Fcourses\u002Fgit-github\u002F)\n  | :movie_camera: [Elzero](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLDoPjvoNmBAw4eOj58MZPakHjaO3frVMF) \n\n***\n\n### :pushpin: **更多书籍 :atom::atom: [:pushpin: 看这里！](https:\u002F\u002Fdrive.google.com\u002Fdrive\u002Ffolders\u002F1iW7IPrVUqsHumgXUMH_rgeBLpJjRDCmJ?usp=sharing)** \t\u003Cbr>\n\u003Cimg align=\"right\" width=\"250\" height=\"197\" src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FMoataz-Elmesmary_Data-Science-Roadmap_readme_a39818037123.png\">\n\n    📕 [:fire:\t\u003Cb>12\u003C\u002Fb> 本重要的免费书籍 :fire:](https:\u002F\u002Fgithub.com\u002FMoataz-Elmesmary\u002FData-Science-Roadmap\u002Ftree\u002Fmain\u002FBooks)\u003Cbr>\n          📕 [机器学习中的数学 ](https:\u002F\u002Fmml-book.github.io\u002F)\u003Cbr>\n          📕 [统计学习导论](https:\u002F\u002Fwww.statlearning.com\u002F)\u003Cbr>\n          📕 [理解机器学习：从理论到算法 ](https:\u002F\u002Fwww.cs.huji.ac.il\u002F~shais\u002FUnderstandingMachineLearning\u002Funderstanding-machine-learning-theory-algorithms.pdf)\u003Cbr>\n          📕 [概率机器学习：导论](https:\u002F\u002Fprobml.github.io\u002Fpml-book\u002Fbook1.html)\u003Cbr>\n          📕 [用数据讲故事](https:\u002F\u002Fdrive.google.com\u002Ffile\u002Fd\u002F1OQu6ZWImGnHbuI_WJOLPdSvKWCABSWMH\u002Fview?usp=sharing) ✔️重要的数据可视化指南。\u003Cbr>\n        \n***\n\u003Cdetails>\n\u003Csummary> \u003Ch3> :pushpin:  \u003Cb>最佳速查表合集\u003C\u002Fb>\u003C\u002Fh3>\u003C\u002Fsummary>\n\n1. [导入数据](https:\u002F\u002Flnkd.in\u002Fe3jnyTEi)\n\n2. Pandas\n\n   - [(1)](https:\u002F\u002Flnkd.in\u002FeiXuBbWh_)\n   - [(2)](https:\u002F\u002Flnkd.in\u002Fe8PKwQQQ)\n   - [(3)](https:\u002F\u002Flnkd.in\u002FewQfqe8q)\n\n3. [Matplotlib](https:\u002F\u002Flnkd.in\u002FejxbW8ak)\n\n4. [Seaborn](https:\u002F\u002Flnkd.in\u002FejhxUp2K)\n\n5. [概率](https:\u002F\u002Flnkd.in\u002Fe4Jxx6xP)\n\n6. [监督学习](https:\u002F\u002Fgithub.com\u002Fafshinea\u002Fstanford-cs-229-machine-learning\u002Fblob\u002Fmaster\u002Fen\u002Fcheatsheet-supervised-learning.pdf)\n\n7. [无监督学习](https:\u002F\u002Fgithub.com\u002Fafshinea\u002Fstanford-cs-229-machine-learning\u002Fblob\u002Fmaster\u002Fen\u002Fcheatsheet-unsupervised-learning.pdf)\n\n8. [深度学习](https:\u002F\u002Fgithub.com\u002Fafshinea\u002Fstanford-cs-229-machine-learning\u002Fblob\u002Fmaster\u002Fen\u002Fcheatsheet-deep-learning.pdf)\n\n9. [机器学习技巧与窍门](https:\u002F\u002Fgithub.com\u002Fafshinea\u002Fstanford-cs-229-machine-learning\u002Fblob\u002Fmaster\u002Fen\u002Fcheatsheet-machine-learning-tips-and-tricks.pdf)\n\n10. [概率与统计](https:\u002F\u002Fgithub.com\u002Fafshinea\u002Fstanford-cs-229-machine-learning\u002Fblob\u002Fmaster\u002Fen\u002Frefresher-probabilities-statistics.pdf)\n\n11. [综合斯坦福大师级速查表](https:\u002F\u002Fgithub.com\u002Fafshinea\u002Fstanford-cs-229-machine-learning\u002Fblob\u002Fmaster\u002Fen\u002Fsuper-cheatsheet-machine-learning.pdf)\n\n12. [线性代数与微积分](https:\u002F\u002Fgithub.com\u002Fafshinea\u002Fstanford-cs-229-machine-learning\u002Fblob\u002Fmaster\u002Fen\u002Frefresher-algebra-calculus.pdf)\n\n13. [数据科学速查表](https:\u002F\u002Fs3.amazonaws.com\u002Fassets.datacamp.com\u002Fblog_assets\u002FPythonForDataScience.pdf)\n\n14. [Keras速查表](https:\u002F\u002Fs3.amazonaws.com\u002Fassets.datacamp.com\u002Fblog_assets\u002FKeras_Cheat_Sheet_Python.pdf)\n\n15. [使用Keras的深度学习速查表](https:\u002F\u002Fgithub.com\u002Frstudio\u002Fcheatsheets\u002Fraw\u002Fmaster\u002Fkeras.pdf)\n\n16. [神经网络架构的可视化指南](http:\u002F\u002Fwww.asimovinstitute.org\u002Fwp-content\u002Fuploads\u002F2016\u002F09\u002Fneuralnetworks.png)\n\n17. [Scikit-Learn Python速查表](https:\u002F\u002Fs3.amazonaws.com\u002Fassets.datacamp.com\u002Fblog_assets\u002FScikit_Learn_Cheat_Sheet_Python.pdf)\n\n18. [Scikit-learn速查表：选择合适的估计器](https:\u002F\u002Fscikit-learn.org\u002Fstable\u002Ftutorial\u002Fmachine_learning_map\u002F)\n\n19. [TensorFlow速查表](https:\u002F\u002Fgithub.com\u002Fkailashahirwar\u002Fcheatsheets-ai\u002Fblob\u002Fmaster\u002FPDFs\u002FTensorflow.pdf)\n\n20. [机器学习测试速查表](https:\u002F\u002Fwww.cheatography.com\u002Flulu-0012\u002Fcheat-sheets\u002Ftest-ml\u002Fpdf\u002F)\n\n21. [机器学习速查表（推荐指南）](https:\u002F\u002Fdrive.google.com\u002Ffile\u002Fd\u002F1rQRJvWk5s9rUCesri0apxutbF4eDHR69\u002Fview?usp=sharing) *请仔细查看这份速查表中的内容，找出你所欠缺的部分* \u003C\u002Fdetails> \n***\n\n### 练习的最佳方式就是参加竞赛。\u003Cimg src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FMoataz-Elmesmary_Data-Science-Roadmap_readme_79bda4b417e4.gif\"  width=\"30px\" height=\"30px\">\u003Cimg src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FMoataz-Elmesmary_Data-Science-Roadmap_readme_79bda4b417e4.gif\"  width=\"30px\" height=\"30px\">\t\u003Cbr>\n**竞赛会让你在数据科学领域更加熟练。**\u003Cbr>\n说到顶尖的数据科学竞赛，[**Kaggle**](https:\u002F\u002Fwww.kaggle.com\u002F) 是最受欢迎的数据科学平台之一。Kaggle 上有许多不同难度级别的竞赛，你可以根据自己的知识水平选择参与。\u003Cbr>\n\n**你也可以查看以下这些数据科学竞赛平台：**\u003Cbr>\n        - [Driven Data](https:\u002F\u002Fwww.drivendata.org\u002Fcompetitions\u002F)\u003Cbr>\n        - [Codalab](https:\u002F\u002Fcompetitions.codalab.org\u002F)\u003Cbr>\n        - [Iron Viz](https:\u002F\u002Fwww.tableau.com\u002Fcommunity\u002Firon-viz)\u003Cbr>\n        - [Topcoder](https:\u002F\u002Fwww.topcoder.com\u002Fchallenges)\u003Cbr>\n        - [CrowdANALYTIX Community](https:\u002F\u002Fwww.crowdanalytix.com\u002Fcommunity)\u003Cbr>\n        - [Bitgrit](https:\u002F\u002Fbitgrit.net\u002F)\u003Cbr>\n\n***\n\u003Cp align=\"center\">\u003Cstrong> 面试准备：通往成功的路线图 🚀 \u003C\u002Fstrong>\u003C\u002Fp>\n\n\u003Cb> 📓 数据科学面试题：\u003C\u002Fb> :arrow_forward:\n  - [(1)](https:\u002F\u002Fgithub.com\u002FyoussefHosni\u002FData-Science-Interview-Questions)\n - [(2)](https:\u002F\u002Fgithub.com\u002Falexeygrigorev\u002Fdata-science-interviews)\n - [(3)](https:\u002F\u002Fgithub.com\u002Frbhatia46\u002FData-Science-Interview-Resources)\n - [(4)](https:\u002F\u002Fgithub.com\u002Fiamtodor\u002Fdata-science-interview-questions-and-answers)\n - [(5)](https:\u002F\u002Fgithub.com\u002Fmilaan9\u002FDataScience_Interview_Questions)\n - [(6) 阿拉伯语播客](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=YjloQOreudk):headphones:\u003Cbr>\n                    - [(7) 30天面试准备](https:\u002F\u002Fgithub.com\u002FMoataz-Elmesmary\u002FData-Science-Roadmap\u002Fblob\u002Fmain\u002F30%20days%20of%20interview%20preparation.pdf):book:\u003Cbr>\t\n\u003Cb> 🚀 来自真实公司的实用面试题：\u003C\u002Fb> [数据分析](https:\u002F\u002Fprepare.sh\u002Finterviews\u002Fdata-analysis) & [数据工程](https:\u002F\u002Fprepare.sh\u002Finterviews\u002Fdata-engineering) by \u003Ci>@Prepare.sh\u003C\u002Fi>。\n\n***\n\u003Cimg align=\"right\" width=\"190\" height=\"145\" src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FMoataz-Elmesmary_Data-Science-Roadmap_readme_ff45f4212246.png\">\n\u003Cdetails>\u003Csummary>🎧\u003Cb>数据科学播客：🎙️\u003C\u002Fb>\u003Cbr> \u003Ci>紧跟最新数据科学趋势与发展的最佳方式\u003C\u002Fi>\u003Cimg src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FMoataz-Elmesmary_Data-Science-Roadmap_readme_026c64708ff4.gif\" width=\"29px\">\u003C\u002Fsummary>\n\n\n\u003Cbr>\n\n播客  | 简介      | 制作方\n-- | --------------------------- | --\n[Data Science at Home](https:\u002F\u002Fdatascienceathome.com\u002F)|一个提供数据科学主题实用建议和教程的播客。|谷歌AI的数据科学家兼机器学习工程师格雷格·林哈特\n[Data Stories](https:\u002F\u002Fdatastori.es\u002F)|一个以访谈为主的数据科学故事播客，讲述数据科学家如何运用技能改变世界。|奈飞的数据科学家兼机器学习工程师基里尔·埃雷门科\n[O'Reilly Data Show](https:\u002F\u002Fwww.oreilly.com\u002Fradar\u002Ftopics\u002Foreilly-data-show-podcast\u002F)|涵盖广泛数据科学话题的播客，从机器学习到人工智能再到大数据。|奥莱利公司的首席数据科学家本·洛里卡\n[Learning Machines 101](https:\u002F\u002Fwww.learningmachines101.com\u002F) |支撑我们日常使用的机器学习系统的数学、统计学和算法。|谷歌AI的机器学习工程师兼研究员理查德·戈登\n[Data Engineering Podcast](https:\u002F\u002Fwww.dataengineeringpodcast.com\u002F) |与数据工程学科相关的工具、技术和挑战。包括数据库、工作流、自动化以及数据处理。|奈飞的数据工程师托比亚斯·梅西\n[Data Science Mixer](https:\u002F\u002Fcommunity.alteryx.com\u002Ft5\u002FData-Science-Mixer\u002Fbg-p\u002Fmixer)  |对于任何想了解更多关于数据科学及该领域最新趋势的人来说都是极好的资源。同时也是一个从其他数据科学家和机器学习工程师的工作中获得灵感的好方法。|数据科学与分析软件公司Alteryx\n[Chai Time Data Science Show](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLLvvXm0q8zUbiNdoIazGzlENMXvZ9bd3x) |采访来自世界各地的顶级数据科学家、从业者和研究人员。|谷歌AI的数据科学家兼机器学习工程师萨尼姆·布塔尼。\n[Becoming a Data Scientist](https:\u002F\u002Fwww.becomingadatascientist.com\u002Fcategory\u002Fpodcast\u002F) |一个采访数据科学家们成为数据科学家历程的播客。|谷歌AI的数据科学家兼机器学习工程师蕾妮·蒂特。\n[AI Today Podcast](https:\u002F\u002Fwww.aidatatoday.com\u002Faitoday\u002F) |探讨人工智能领域的最新趋势和发展。|罗恩·施梅尔策和凯瑟琳·沃尔奇\n[Gradient Dissent](https:\u002F\u002Fwandb.ai\u002Ffully-connected\u002Fpodcast) |每周一期的播客，探索机器学习和人工智能领域的最新研究。|谷歌AI的机器学习工程师克里斯·欧拉\n[Data Skeptic](https:\u002F\u002Fdataskeptic.com\u002F) |一个挑战数据科学传统观念，并就数据驱动决策的伦理与影响提出尖锐问题的播客。|数据科学家兼机器学习工程师凯尔·波利奇\n[Linear Digressions](https:\u002F\u002Flineardigressions.com\u002F) |一个涵盖广泛数据科学话题的播客，从技术到理论都有涉及。|加州大学伯克利分校的两位机器学习研究员本·雷希特和诺亚·史密斯\n[The Data Engineering Show](https:\u002F\u002Fwww.dataengineeringshow.com\u002F) |专为数据工程和BI从业者设计，帮助他们超越理论，在实践中向科技界的领军人物学习如何应对日常数据挑战。|埃尔达德·法尔卡什和本杰明·瓦格纳，两位在Firebolt和Sisense等公司拥有丰富经验的数据工程专家\n[DataTalks.Club](https:\u002F\u002Fpodcasters.spotify.com\u002Fpod\u002Fshow\u002Fdatatalksclub) |一个由数据爱好者和从业者组成的每周线上社区，大家通过聚会、研讨会和播客互相学习、分享知识与经验。|由多位数据专家轮流主持\n[Datacast](https:\u002F\u002Fjameskle.com\u002Fwrites\u002Fcategory\u002FDatacast) |数据和AI基础设施领域的顶尖数据科学家和从业者。|詹姆斯·勒，一位在谷歌和奈飞等公司有经验的数据基础设施专家\n[How to Get an Analytics Job Podcast](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLBvzkZLydYX0D28bbnfRCV6M4zMQrhXsd) |对于任何对分析行业感兴趣的人来说都是绝佳资源。嘉宾们分享他们在进入分析领域以及如何在这一职业中取得成功方面的见解和建议。|约翰·大卫·阿里森森，一家分析机构的所有者兼职业教练\n[The Analytics Power Hour](https:\u002F\u002Fanalyticshour.io\u002F) |五位杰出人士、偶尔的一位嘉宾以及畅饮美酒，共同探讨当下最热门的数据与分析话题。|蒂姆·威尔逊、迈克尔·赫尔布林、乔什·克劳赫斯特和瓦尔·克罗尔。他们均来自不同公司的分析专家\n\u003C\u002Fdetails>\n\n\u003Cbr>\n\n\u003Cdetails>\u003Csummary>    :eyes: 阿拉伯语播客？？\u003C\u002Fsummary>\n\n######        :trollface:看你这通勤路上多无聊啊\n   📻[阿拉伯数据播客](https:\u002F\u002Fwww.youtube.com\u002F@arabic_data_podcast) | [Spotify](https:\u002F\u002Fopen.spotify.com\u002Fshow\u002F6xo79RT4NP73wQA39TgAq1) 由工程师卡里姆·阿卜杜勒萨拉姆制作\u003Cbr>\n   📻[通往数据及其未来之路](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=3znPvz6P2oM&list=PL9yAM5pvSfU5EdppOCf-YvttRsabeAmbN) 由工程师优素福·侯赛尼制作\u003Cbr>\n   📻[车库教育](https:\u002F\u002Fwww.youtube.com\u002F@GarageEducation\u002Fplaylists) 由工程师穆斯塔法·阿拉制作\u003Cbr>\n   📻[用阿拉伯语谈数据科学](https:\u002F\u002Fwww.boomplay.com\u002Fpodcasts\u002F29169)\u003Cbr>\n\u003C\u002Fdetails>\n\n***\n\n:pushpin: **数据分析推荐。**\u003Cbr>\n\u003Cimg align=\"right\" width=\"150\" height=\"150\" src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FMoataz-Elmesmary_Data-Science-Roadmap_readme_7329ba64f22e.png\">\n         书籍（📕 [数据分析工作坊](https:\u002F\u002Fdrive.google.com\u002Ffile\u002Fd\u002F1BjKsffA2SCY0jY8OIIzgQgM0ZS7E9v_v\u002Fview?fbclid=IwAR2_GBlrX7VYoo8WCRO9R2qqrYEqtytoGrObxy1QHWcQ7sRaFjRLb0GmuxM) &\n        📕 [Head First 数据分析](https:\u002F\u002Fdrive.google.com\u002Ffile\u002Fd\u002F1HXHkwrgsSJLYSeB6I0wPUXIGGnm2-HQ6\u002Fview?fbclid=IwAR27M-dlPN6o0YuZg3bXH6_DP9L2fBhkKDEkChvO4SPG-SXfkxrzuoGP5RM))\u003Cbr>\n        [谷歌数据分析专业证书](https:\u002F\u002Fwww.coursera.org\u002Fprofessional-certificates\u002Fgoogle-data-analytics)\u003Cbr>\n         [IBM 数据分析师专业证书](https:\u002F\u002Fwww.coursera.org\u002Fprofessional-certificates\u002Fibm-data-analyst?fbclid=IwAR1IajEEe2yydVWRt3hbj4qLioXP6oR-fdbw8f1kHAVpAXSA4Z8Eww1Y-fs)\u003Cbr>\n         [谷歌高级数据分析专业证书 :new:](https:\u002F\u002Fwww.coursera.org\u002Fprofessional-certificates\u002Fgoogle-advanced-data-analytics?irclickid=zzy33K1O0xyNUAmxqWUjDwedUkAQlBwwJ21EwA0&irgwc=1&utm_medium=partners&utm_source=impact&utm_campaign=2624140&utm_content=b2c)\u003Cbr>\n         [Alex The Analyst - YouTube📺](https:\u002F\u002Fwww.youtube.com\u002F@AlexTheAnalyst\u002Fplaylists)\u003Cbr>\n         [FWD - (三个层次)](https:\u002F\u002Fegfwd.com\u002F?fbclid=IwAR1phYmHHgi0L4E9nOPZcSfAdHWsDs9EvBh3dJgO6gXN4B1A-nV8vspGggs)\u003Cbr>\n         [阿拉伯语 - ITI - BI开发路线](https:\u002F\u002Fgithub.com\u002FMoataz-Elmesmary\u002FITI-Business-Intelligence-Development)\u003Cbr>\n        *注：只要掌握好[Excel](https:\u002F\u002Fwww.coursera.org\u002Flearn\u002Fexcel-basics-data-analysis-ibm)、SQL以及Power BI\u002FTableau，并有一些相关项目经验，就能为你带来很多机会*。\u003Cbr>\n          -\u003Cimg src=\"https:\u002F\u002Fraw.githubusercontent.com\u002Fmrankitgupta\u002F66DaysOfData\u002F60139fb461ef56a19afd68ea4094f6069f27ce49\u002Ficons8-microsoft-excel%20(1).svg\" alt=\"excel\" width=\"25\" height=\"25\"\u002F> Excel更多资源：([阿拉伯语1📹](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=9Z5MPeyuLhg&t=397s) - [阿拉伯语2📹](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=uRs8_EJqTFo&list=PLXlHqMRg9lAYiiutr-Ou0J1uU20T-5a4-&pp=iAQB) - [书籍 :page_facing_up: 和复习备忘录](https:\u002F\u002Fdrive.google.com\u002Fdrive\u002Ffolders\u002F1CAUKDb5jv1pMez1WO74ogkpX44UMW_ky))\u003Cbr>\n\u003Cp align=\"center\">\n\u003Cimg src=\"https:\u002F\u002Fi.imgur.com\u002FdBaSKWF.gif\" height=\"20\" width=\"100%\">\n\n:pushpin: **[数据工程](https:\u002F\u002Fyoutu.be\u002FqWru-b6m030)推荐。**\u003Cbr>\n         书籍（📕 [数据工程基础](https:\u002F\u002Fdrive.google.com\u002Ffile\u002Fd\u002F1CbQFN0Lw8o6v4KlF64LsCyaooMccT45T\u002Fview?usp=sharing) &\n        📕 [设计数据密集型应用](https:\u002F\u002Fdrive.google.com\u002Ffile\u002Fd\u002F1CrzA--WWNcxxQwLqzg1yPfiI3FaEo49z\u002Fview?usp=sharing))\u003Cbr>\n\u003Cimg align=\"right\" width=\"150\" height=\"150\" src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FMoataz-Elmesmary_Data-Science-Roadmap_readme_0284bdf0e62c.png\">\n         阿拉伯语播客，[如何开启数据工程职业生涯。](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=OtaBhXjrbX4)\u003Cbr>\n         对于阿拉伯地区的朋友们，我推荐两个YouTube频道：([车库教育](https:\u002F\u002Fwww.youtube.com\u002F@GarageEducation) 和 [大数据用阿拉伯语](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLrooD4hY1QqAK5pbBpcthLuMa-cXnXJLE))\u003Cbr>\n         [路线图1](https:\u002F\u002Fgithub.com\u002FOmarEhab007\u002FData_Engineering_Mentorship) - *(推荐)*\u003Cbr> \n         [路线图2](https:\u002F\u002Fwww.educba.com\u002Fdata-engineer-roadmap\u002F) \u003Cbr>\n         [路线图3](https:\u002F\u002Fgithub.com\u002Fdatastacktv\u002Fdata-engineer-roadmap)\u003Cbr>\n         [IBM数据工程专业证书](https:\u002F\u002Fwww.coursera.org\u002Fprofessional-certificates\u002Fibm-data-engineer)\u003Cbr>\n        *注：只要精通SQL、Python、Apache [Spark](https:\u002F\u002Fwww.udacity.com\u002Fcourse\u002Flearn-spark-at-udacity--ud2002)\u002FHadoop、数据建模以及[[数据仓库](https:\u002F\u002Fwww.coursera.org\u002Flearn\u002Fdwdesign) - {阿拉伯语-[从第7集开始](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLxNoJq6k39G_m6DYjpz-V92DkaQEiXxkF)}]，就能为你带来许多机会。先从这些工具和概念入手，再逐步学习其他工具、理念和云平台。*\u003Cbr>\n***\n\u003Cdetails>\u003Csummary>:file_folder: \u003Cb>简历 \u002F 履历 :memo: \u003C\u002Fb>  \n \u003Cimg src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FMoataz-Elmesmary_Data-Science-Roadmap_readme_665e7aaaf5cf.gif\" width=\"75\">\n  \u003Ca href=\"https:\u002F\u002Fgit.io\u002Ftyping-svg\">\n    \u003Cimg src=\"https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FMoataz-Elmesmary_Data-Science-Roadmap_readme_b29bbe702d4d.png\">\n\u003C\u002Fsummary>\n  \n- [叶海亚·阿拉法·穆斯塔法指出的常见错误](https:\u002F\u002Fwww.facebook.com\u002Fyehia.arafa.mostafa\u002Fposts\u002F110086229517000)\u003Cbr>\n- [奥马尔·亚塞尔的简历技巧](https:\u002F\u002Fmedium.com\u002F@oyaraouf\u002Fcv-tips-5faaec55ec07)\u003Cbr>\n- [Careercup展示的良好简历应具备的特征](https:\u002F\u002Fwww.careercup.com\u002Fresume)\u003Cbr>\n- 在完成你的简历初稿后，可以请穆斯塔法·纳吉布进行评审（https:\u002F\u002Fwww.facebook.com\u002Fstory.php?story_fbid=2928705840553931&id=445112032246670)\u003Cbr>\n- [亚塞尔·阿拉在毕业后发布的简历](https:\u002F\u002Fwww.linkedin.com\u002Ffeed\u002Fupdate\u002Furn:li:activity:6964595411839799296\u002F)\u003Cbr>\n- [如何制作数据科学家简历](https:\u002F\u002Fenhancv.com\u002Fresume-examples\u002Fdata-scientist\u002F)\u003Cbr>\n- [数据科学简历指南](https:\u002F\u002Fwww.beamjobs.com\u002Fresumes\u002Fdata-science-resume-example-guide)\u003Cbr>\n- 针对数据岗位的简历\u002FCV撰写（阿拉伯语）\u003Cbr>\n  📹[视频1](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=R0hsJiNxdDE)\u003Cbr>\n  📹[视频2](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=CrTO0hrC-zQ)\n\u003C\u002Fdetails>\n\n***\n:pushpin: [\u003Cb>\u003Ci>埃及的数据与人工智能公司\u003C\u002Fi>\u003C\u002Fb>](https:\u002F\u002Ftrello.com\u002Fb\u002Fu4HH9Anu\u002Fdata-ai-jobs-in-egypt)   -   [\u003Ci>埃及的AI\u002FML驱动型企业\u003C\u002Fi>](https:\u002F\u002Fgithub.com\u002Fharryadel\u002FAI-ML-Driven-Companies-In-Egypt)\n***\n\n\u003Ch2>联系我 :iphone:\u003C\u002Fh2> \t \u003Cbr>\n  \n\u003Ca href=\"https:\u002F\u002Fwww.facebook.com\u002FMoatazElmesmary\u002F\" title=\"Facebook\">\u003Cimg src=\"https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FFacebook-%234267B2?style=flat&logo=Facebook&logoColor=white\"\u002F>\u003C\u002Fa>\n\u003Ca href=\"https:\u002F\u002Ftwitter.com\u002FMoatazElmesmary\" title=\"twitter\">\u003Cimg src=\"https:\u002F\u002Fimg.shields.io\u002Ftwitter\u002Furl?label=twitter&style=social&url=https%3A%2F%2Fimg.shields.io%2Ftwitter%2F%3Flabel%3Dtwitter%26style%3Dsocial\"\u002F>\u003C\u002Fa>\n\u003Ca href=\"https:\u002F\u002Fwww.linkedin.com\u002Fin\u002FMoatazElmesmary\u002F\" title=\"LinkedIn\">\u003Cimg src=\"https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FLinkedIn-%230177B5?style=flat&logo=linkedin&logoColor=white\"\u002F>\u003C\u002Fa>\u003Ch1 align=\"center\">\n[![打字SVG](https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FMoataz-Elmesmary_Data-Science-Roadmap_readme_70ddd29c2533.png)](https:\u002F\u002Fgit.io\u002Ftyping-svg)\u003C\u002Fh1>","# Data-Science-Roadmap 快速上手指南\n\n本指南基于 **Data-Science-Roadmap** 项目整理，旨在帮助开发者快速搭建数据科学学习环境并开启自学之路。该项目提供了一条从入门到进阶的免费学习路径，涵盖统计学、编程语言（Python\u002FR）、数据处理及机器学习等核心内容。\n\n## 环境准备\n\n在开始之前，请确保你的开发环境满足以下要求：\n\n*   **操作系统**：Windows, macOS 或 Linux 均可。\n*   **前置知识**：建议具备基础的计算机操作能力。若无数学或编程背景，请先阅读项目推荐的《Grokking Algorithms》或观看基础统计视频。\n*   **核心依赖**：\n    *   **Python** (推荐版本 3.8+) 或 **R** 语言。\n    *   **包管理工具**：Conda (推荐) 或 pip。\n    *   **开发工具 (IDE)**：Jupyter Notebook, PyCharm, VS Code 或 Google Colab (无需本地安装)。\n\n> **💡 提示**：对于初学者，强烈推荐使用 **Anaconda** 发行版，它预装了数据科学所需的大部分库（如 NumPy, Pandas, Matplotlib, Scikit-learn），可避免繁琐的环境配置。国内用户可通过 [清华大学开源软件镜像站](https:\u002F\u002Fmirrors.tuna.tsinghua.edu.cn\u002Fhelp\u002Fanaconda\u002F) 下载 Anaconda 安装包以加速。\n\n## 安装步骤\n\n### 方案 A：使用 Anaconda（推荐，一站式解决）\n\n1.  **下载安装包**：\n    访问 [清华镜像站](https:\u002F\u002Fmirrors.tuna.tsinghua.edu.cn\u002Fanaconda\u002Farchive\u002F) 下载适合你系统的最新 Anaconda 安装包。\n\n2.  **安装**：\n    运行下载的安装程序，按照默认选项完成安装。勾选 \"Add Anaconda to my PATH environment variable\" (Linux\u002FMac) 或在安装后手动配置环境变量。\n\n3.  **验证安装**：\n    打开终端 (Terminal) 或命令提示符 (CMD\u002FPowerShell)，输入以下命令：\n    ```bash\n    conda --version\n    python --version\n    ```\n    若显示版本号，则安装成功。\n\n4.  **创建专属学习环境** (可选但推荐)：\n    ```bash\n    conda create -n ds-roadmap python=3.9\n    conda activate ds-roadmap\n    ```\n\n5.  **安装核心数据科学库**：\n    ```bash\n    conda install numpy pandas scipy matplotlib scikit-learn jupyter\n    ```\n\n### 方案 B：使用 Google Colab（零配置，云端运行）\n\n如果你不想在本地安装任何软件，可以直接使用浏览器访问：\n*   访问地址：[https:\u002F\u002Fcolab.research.google.com](https:\u002F\u002Fcolab.research.google.com)\n*   点击 \"New Notebook\" 即可开始编写代码。所有常用库（NumPy, Pandas 等）已预装。\n\n### 方案 C：使用 pip 手动安装 (适用于已有 Python 环境)\n\n```bash\npip install numpy pandas scipy matplotlib scikit-learn jupyterlab\n```\n*国内用户建议使用清华源加速安装：*\n```bash\npip install -i https:\u002F\u002Fpypi.tuna.tsinghua.edu.cn\u002Fsimple numpy pandas scipy matplotlib scikit-learn jupyterlab\n```\n\n## 基本使用\n\n安装完成后，你可以通过以下步骤开始第一个数据科学练习：\n\n### 1. 启动 Jupyter Notebook\n在终端中运行以下命令启动交互式编程环境：\n```bash\njupyter notebook\n```\n浏览器会自动打开一个本地页面，点击 \"New\" -> \"Python 3\" 创建新文件。\n\n### 2. 运行第一个数据分析示例\n在新建的单元格中复制并运行以下代码，体验数据加载与基础分析：\n\n```python\nimport pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n# 1. 创建模拟数据集\ndata = {\n    'Product': ['A', 'B', 'C', 'D', 'E'],\n    'Sales': [150, 200, 120, 300, 250],\n    'Profit': [30, 45, 20, 60, 55]\n}\ndf = pd.DataFrame(data)\n\n# 2. 查看数据前几行\nprint(\"数据预览:\")\nprint(df.head())\n\n# 3. 基础统计描述\nprint(\"\\n统计描述:\")\nprint(df.describe())\n\n# 4. 简单可视化\nplt.figure(figsize=(8, 5))\nplt.bar(df['Product'], df['Sales'], color='skyblue')\nplt.title('Product Sales Overview')\nplt.xlabel('Product')\nplt.ylabel('Sales')\nplt.show()\n```\n\n### 3. 跟随路线图学习\n代码运行成功后，你可以参照原项目的 **Beginner (入门)** 阶段开始系统学习：\n*   **统计学基础**：观看推荐的 Descriptive Statistics 视频。\n*   **Python 进阶**：学习面向对象编程 (OOP) 和 Pandas 高级用法。\n*   **实战练习**：利用 Kaggle 提供的数据集进行数据清洗和分析练习。\n\n> **注意**：本路线图强调“不要急于进入机器学习 (ML)\"，请务必先打好编程和数学基础。","刚毕业的市场分析专业学生李明想转行数据科学，面对海量学习资源感到无从下手，急需一条清晰的入门路径。\n\n### 没有 Data-Science-Roadmap 时\n- **概念混淆严重**：分不清数据科学、数据分析和数据工程的区别，导致学习方向频繁摇摆，今天学 SQL 明天啃 Hadoop，精力分散。\n- **技能树缺失**：不清楚企业招聘所需的硬技能（如机器学习算法）与软技能组合，盲目刷题却无法满足岗位实际要求。\n- **工具选型困难**：在 Anaconda、PyCharm 或云端环境之间犹豫不决，花费数周配置本地环境却仍未写出第一行有效代码。\n- **项目流程断层**：只关注模型训练，完全忽略从“业务理解”到“模型部署”的完整生命周期，面试时无法阐述项目闭环。\n\n### 使用 Data-Science-Roadmap 后\n- **定位清晰明确**：通过对比图表迅速厘清三大领域差异，根据自身数学与编程背景，果断锁定“数据科学”为主攻方向。\n- **学习路径结构化**：依托路线图提供的 A 到 Z 自学计划，按部就班地掌握 Python、统计学及机器学习核心技能，不再遗漏关键知识点。\n- **环境搭建高效**：直接采纳推荐的 Anaconda 或 Google Colab 方案，半天内完成开发环境配置，立即开始实战练习。\n- **全流程视野开阔**：借助推荐视频深入理解项目全生命周期，在个人作品中补充了业务洞察与部署环节，显著提升简历竞争力。\n\nData-Science-Roadmap 将碎片化的知识整合为系统化的行动指南，帮助初学者以最低试错成本快速构建符合行业标准的能力体系。","https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002FMoataz-Elmesmary_Data-Science-Roadmap_7776671d.png","Moataz-Elmesmary","Moataz Elmesmary ","https:\u002F\u002Foss.gittoolsai.com\u002Favatars\u002FMoataz-Elmesmary_396e58d0.jpg",null,"Egyptian Food Bank","Cairo, Egypt","moataz.mesmary@gmail.com","MoatazElmesmary","https:\u002F\u002Fmoatazelmesmary.vercel.app\u002F","https:\u002F\u002Fgithub.com\u002FMoataz-Elmesmary",4227,596,"2026-04-18T17:56:47","MIT",1,"Windows, macOS, Linux","未说明",{"notes":90,"python":91,"dependencies":92},"该项目是一个学习路线图而非单一软件工具，因此没有严格的系统运行需求。官方推荐使用 Anaconda 发行版（包含 Jupyter Notebook、R Studio 等）或 PyCharm 作为本地开发环境；也可使用无需本地安装的 Google Colab。初学者建议先掌握编程和数学基础再进入机器学习阶段。","未说明 (推荐安装 Anaconda 或使用 Google Colab)",[93,94,95,96,97],"NumPy","Pandas","Matplotlib","Scikit-Learn","SciPy",[99,44,14,15,16],"其他",[101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120],"data-analysis","data-engineering","data-science","data-visualization","deep-learning","machine-learning","mathematics","probability","python","sql","statistics","cheatsheet","cv-template","interview-questions","linear-algebra","neural-network","big-data","chatgpt","llms","nlp","2026-03-27T02:49:30.150509","2026-04-20T10:35:04.252497",[],[]]