RRepoGEO

REPOGEO REPORT · LITE

Moataz-Elmesmary/Data-Science-Roadmap

Default branch main · commit 04a86642 · scanned 5/27/2026, 2:53:42 PM

GitHub: 4,257 stars · 604 forks

AI VISIBILITY SCORE
33 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
2 pass · 0 warn · 0 fail
Objective metadata checks
AI knows your name
2 / 3
Direct prompts that named your repo
HOW TO READ THIS REPORT

Action plan is what to do next — copy-pasteable changes prioritized by impact. Category visibility is the real GEO test: when a user asks an AI a brand-free question that should surface Moataz-Elmesmary/Data-Science-Roadmap, does the AI actually recommend you — or your competitors? Objective checks verify the metadata signals AI engines weight first. Self-mention check detects whether AI even knows you exist by name.

Action plan — copy-paste fixes

3 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • highreadme#1
    Clarify README's opening statement to emphasize 'curated roadmap'

    Why:

    CURRENT
    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.
    COPY-PASTE FIX
    This repository offers a comprehensive, curated self-learning roadmap for aspiring data scientists, aggregating the best free external resources and learning paths.
  • mediumtopics#2
    Add specific topics to highlight the repo's function as a learning roadmap

    Why:

    CURRENT
    big-data, chatgpt, cheatsheet, cv-template, data-analysis, data-engineering, data-science, data-visualization, deep-learning, interview-questions, linear-algebra, llms, machine-learning, mathematics, neural-network, nlp, probability, python, sql, statistics
    COPY-PASTE FIX
    big-data, chatgpt, cheatsheet, cv-template, data-analysis, data-engineering, data-science, data-visualization, deep-learning, interview-questions, linear-algebra, llms, machine-learning, mathematics, neural-network, nlp, probability, python, sql, statistics, data-science-roadmap, learning-path, career-guide, curated-resources, self-study, data-science-resources, data-science-curriculum
  • lowabout#3
    Expand the repository description to highlight its curated nature

    Why:

    CURRENT
    Data Science Roadmap from A to Z
    COPY-PASTE FIX
    A comprehensive, curated self-learning roadmap for aspiring data scientists, providing a structured path and aggregating the best free external resources from A to Z.

Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash

Category visibility — the real GEO test

Brand-free queries asked to google/gemini-2.5-flash. Did AI recommend you, or someone else?

Same questions for every model — switch tabs to compare answers and rankings.

Recall
0 / 2
0% of queries surface Moataz-Elmesmary/Data-Science-Roadmap
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Coursera
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Coursera · recommended 2×
  2. Python · recommended 1×
  3. Khan Academy · recommended 1×
  4. 3Blue1Brown · recommended 1×
  5. Think Stats · recommended 1×
  • CATEGORY QUERY
    What's a good self-learning path to become a data scientist from scratch?
    you: not recommended
    AI recommended (in order):
    1. Python
    2. Khan Academy
    3. 3Blue1Brown
    4. Think Stats
    5. Coursera
    6. NumPy (numpy/numpy)
    7. Pandas (pandas-dev/pandas)
    8. SQL
    9. Matplotlib (matplotlib/matplotlib)
    10. Seaborn (mwaskom/seaborn)
    11. Plotly/Dash (plotly/plotly.py)
    12. Scikit-learn (scikit-learn/scikit-learn)
    13. Octave/MATLAB
    14. XGBoost (dmlc/xgboost)
    15. LightGBM (microsoft/LightGBM)
    16. TensorFlow/Keras (tensorflow/tensorflow)
    17. PyTorch (pytorch/pytorch)
    18. Kaggle
    19. GitHub
    20. Stack Overflow
    21. Reddit's r/datascience
    22. Towards Data Science on Medium

    AI recommended 22 alternatives but never named Moataz-Elmesmary/Data-Science-Roadmap. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Where can I find free resources to learn machine learning and data analysis skills?
    you: not recommended
    AI recommended (in order):
    1. Coursera
    2. Andrew Ng's Machine Learning course
    3. Python for Everybody Specialization
    4. Kaggle Learn
    5. Pandas
    6. freeCodeCamp.org
    7. Google's Machine Learning Crash Course
    8. TensorFlow
    9. edX
    10. Towards Data Science
    11. Krish Naik
    12. StatQuest with Josh Starmer

    AI recommended 12 alternatives but never named Moataz-Elmesmary/Data-Science-Roadmap. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    pass

  • README presence
    pass

Self-mention check

Does AI even know your repo exists when asked about it directly?

  • Compared to common alternatives in this category, what is the core differentiator of Moataz-Elmesmary/Data-Science-Roadmap?
    pass
    AI named Moataz-Elmesmary/Data-Science-Roadmap explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • If a team adopts Moataz-Elmesmary/Data-Science-Roadmap in production, what risks or prerequisites should they evaluate first?
    pass
    AI named Moataz-Elmesmary/Data-Science-Roadmap explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • In one sentence, what problem does the repo Moataz-Elmesmary/Data-Science-Roadmap solve, and who is the primary audience?
    pass
    AI did not name Moataz-Elmesmary/Data-Science-Roadmap — likely talking about a different project

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

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