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krishnaik06/Perfect-Roadmap-To-Learn-Data-Science-In-2025

默认分支 main · commit c404cd2e · 扫描时间 2026/5/12 03:03:14

星标 4,050 · Fork 1,537

AI 可见性总分
17 /100
亟需修复
品类召回
0 / 2
在所有问题中均未被推荐
规则结果
通过 1 · 警告 0 · 失败 1
客观元数据检查
AI 认识你的名字
1 / 3
直接询问时，AI 是否点名你的仓库
如何阅读这份报告

行动计划告诉你下一步要做什么——按影响力排序、可直接复制粘贴的修改。品类可见性是真正的 GEO 测试：当用户向 AI 提一个不带品牌、本应让 krishnaik06/Perfect-Roadmap-To-Learn-Data-Science-In-2025 浮出水面的问题时，AI 是真的推荐了你，还是推荐了你的竞品？客观检查验证 AI 引擎最先权衡的那些元数据信号。自指检查判断 AI 是否还认识你的名字。

行动计划 — 可复制粘贴的修复

3 条由 gemini-2.5-flash 生成、按优先级排序的修改。修完后请把对应条目标记为完成。

整体方向
  • highabout#1
    Add a concise repository description

    原因：

    复制粘贴的修复
    A comprehensive, year-long roadmap for aspiring data scientists in 2025, featuring curated learning paths, resources, and interview preparation.
  • hightopics#2
    Add relevant topics to improve categorization

    原因：

    复制粘贴的修复
    data-science, roadmap, learning-path, data-scientist, career-path, machine-learning, python, statistics, eda, interview-prep
  • mediumreadme#3
    Add a clear introductory sentence to the README

    原因：

    当前
    # Perfect Roadmap To Learn Data Science In 2025
    复制粘贴的修复
    # Perfect Roadmap To Learn Data Science In 2025

This repository offers a comprehensive, step-by-step learning roadmap designed for individuals aspiring to become data scientists in 2025, guiding you through essential skills and resources.

本次扫描解析到的品类 GEO 通道：google/gemini-2.5-flash, deepseek/deepseek-v4-flash

品类可见性 — 真正的 GEO 测试

向 google/gemini-2.5-flash 提出的不带品牌问题。AI 推荐了你，还是推荐了别人？

各模型使用同一组问题 — 切换标签对比回答与排名。

召回
0 / 2
0% 的问题里出现了 krishnaik06/Perfect-Roadmap-To-Learn-Data-Science-In-2025
平均排名
—
越小越好。#1 表示首位推荐。
声量占比
0%
在所有被点名的工具中，你占了多少？
头号对手
Automate the Boring Stuff with Python
在 2 个问题中被推荐 1 次
竞品排行
  1. Automate the Boring Stuff with Python · 被推荐 1 次
  2. Python for Everybody · 被推荐 1 次
  3. LeetCode · 被推荐 1 次
  4. Khan Academy · 被推荐 1 次
  5. Practical Statistics for Data Scientists · 被推荐 1 次
  • 品类问题
    Seeking a complete learning roadmap for becoming a data scientist in the coming year.
    你：未被推荐
    AI 推荐顺序：
    1. Automate the Boring Stuff with Python
    2. Python for Everybody
    3. LeetCode
    4. Khan Academy
    5. Practical Statistics for Data Scientists
    6. Pandas (pandas-dev/pandas)
    7. NumPy (numpy/numpy)
    8. Python for Data Analysis
    9. An Introduction to Statistical Learning with Applications in R (ISLR)
    10. Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow
    11. Scikit-learn (scikit-learn/scikit-learn)
    12. Matplotlib (matplotlib/matplotlib)
    13. Seaborn (mwaskom/seaborn)
    14. Plotly/Dash
    15. SQLZoo
    16. Mode Analytics SQL Tutorial
    17. PostgreSQL (postgres/postgres)
    18. Deep Learning Specialization by Andrew Ng
    19. TensorFlow/Keras (tensorflow/tensorflow)
    20. PyTorch (pytorch/pytorch)
    21. Apache Spark (apache/spark)
    22. AWS S3
    23. EC2
    24. SageMaker
    25. MLflow (mlflow/mlflow)
    26. Docker (docker/docker-ce)
    27. Tableau Public
    28. PowerPoint
    29. Google Slides
    30. Kaggle
    31. GitHub
    32. Reddit's r/datascience
    33. Stack Overflow

    AI 推荐了 33 个替代方案，却始终没点名 krishnaik06/Perfect-Roadmap-To-Learn-Data-Science-In-2025。这就是要补上的差距。

    查看 AI 完整回答
  • 品类问题
    What are the best structured learning paths for mastering data science concepts and tools?
    你：未被推荐
    AI 推荐顺序：
    1. Coursera Specializations and Professional Certificates
    2. IBM Data Science Professional Certificate
    3. Google Advanced Data Analytics Professional Certificate
    4. DeepLearning.AI TensorFlow Developer Professional Certificate
    5. University of Michigan's Applied Data Science with Python Specialization
    6. DataCamp Career Tracks
    7. Data Scientist with Python Career Track
    8. Data Scientist with R Career Track
    9. Udacity Nanodegree Programs
    10. Data Scientist Nanodegree
    11. Data Analyst Nanodegree
    12. edX MicroMasters Programs
    13. MITx MicroMasters Program in Statistics and Data Science
    14. ColumbiaX MicroMasters Program in Data Science
    15. Kaggle Learn
    16. Fast.ai Practical Deep Learning for Coders
    17. Google's Machine Learning Crash Course

    AI 推荐了 17 个替代方案，却始终没点名 krishnaik06/Perfect-Roadmap-To-Learn-Data-Science-In-2025。这就是要补上的差距。

    查看 AI 完整回答

客观检查

针对 AI 引擎最看重的元数据信号的规则审计。

  • Metadata completeness
    fail

    建议：

  • README presence
    pass

自指检查

当被直接问到你时，AI 是否还知道你的仓库存在？

  • Compared to common alternatives in this category, what is the core differentiator of krishnaik06/Perfect-Roadmap-To-Learn-Data-Science-In-2025?
    pass
    AI 未点名 krishnaik06/Perfect-Roadmap-To-Learn-Data-Science-In-2025 —— 很可能在说另一个项目

    AI 的回答可能信誓旦旦却是错的。请按事实核对：技术栈、目标人群、差异化点是不是和你实际的对得上？

  • If a team adopts krishnaik06/Perfect-Roadmap-To-Learn-Data-Science-In-2025 in production, what risks or prerequisites should they evaluate first?
    pass
    AI 明确点名了 krishnaik06/Perfect-Roadmap-To-Learn-Data-Science-In-2025

    AI 的回答可能信誓旦旦却是错的。请按事实核对：技术栈、目标人群、差异化点是不是和你实际的对得上？

  • In one sentence, what problem does the repo krishnaik06/Perfect-Roadmap-To-Learn-Data-Science-In-2025 solve, and who is the primary audience?
    pass
    AI 未点名 krishnaik06/Perfect-Roadmap-To-Learn-Data-Science-In-2025 —— 很可能在说另一个项目

    AI 的回答可能信誓旦旦却是错的。请按事实核对：技术栈、目标人群、差异化点是不是和你实际的对得上？

嵌入你的 GEO 徽章

把这个徽章贴进 krishnaik06/Perfect-Roadmap-To-Learn-Data-Science-In-2025 的 README。每次重新扫描都会自动更新，并跳到最新报告——是「我在乎 AI 可发现性」最简单的公开证明。

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krishnaik06/Perfect-Roadmap-To-Learn-Data-Science-In-2025 — 轻量扫描仍免费；本卡列出 Pro 相对轻量的深度额度。

  • 深度报告每月 10 次
  • 无品牌品类查询5，轻量 2
  • 优先行动项8，轻量 3
krishnaik06/Perfect-Roadmap-To-Learn-Data-Science-In-2025 — RepoGEO 报告