RRepoGEO

REPOGEO REPORT · LITE

PavelGrigoryevDS/awesome-data-analysis

Default branch main · commit f2873c1c · scanned 5/17/2026, 4:07:35 AM

GitHub: 1,089 stars · 154 forks

Scan history for this repo

Score trend below includes all ready runs (older left, newer right; scroll horizontally if needed). The table is collapsed by default—expand for newest-first rows, 10 per page.

Score trend (left → right: older → newer)

2 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

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 PavelGrigoryevDS/awesome-data-analysis, 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
    Explicitly state 'awesome list' in the README's opening sentence

    Why:

    CURRENT
    500+ curated resources for data analysis and data science: tools, libraries, roadmaps, cheatsheets, interview guides and more.
    COPY-PASTE FIX
    This is an awesome list of 500+ curated resources for data analysis and data science: tools, libraries, roadmaps, cheatsheets, interview guides and more.
  • mediumreadme#2
    Add a 'Comparison' section to the README

    Why:

    COPY-PASTE FIX
    ## How is this different from learning platforms or tools?

This repository is a curated collection of external resources, unlike learning platforms (e.g., Kaggle Learn, Coursera) which provide their own content, or software tools (e.g., Pandas, NumPy) which are used for data analysis. We help you discover the best resources, not provide them directly.
  • lowabout#3
    Update the repository description to explicitly include 'awesome list'

    Why:

    CURRENT
    🚀 500+ curated resources for Data Analysis & Data Science: Python, SQL, Statistics, ML, AI, Visualization, Cheatsheets, Roadmaps, Interview Prep. For beginners and experts.
    COPY-PASTE FIX
    🚀 An awesome list of 500+ curated resources for Data Analysis & Data Science: Python, SQL, Statistics, ML, AI, Visualization, Cheatsheets, Roadmaps, Interview Prep. For beginners and experts.

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 PavelGrigoryevDS/awesome-data-analysis
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Kaggle Learn
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Kaggle Learn · recommended 1×
  2. freeCodeCamp · recommended 1×
  3. Coursera · recommended 1×
  4. edX · recommended 1×
  5. Towards Data Science · recommended 1×
  • CATEGORY QUERY
    Where can I find a comprehensive list of resources for learning data science and analytics?
    you: not recommended
    AI recommended (in order):
    1. Kaggle Learn
    2. freeCodeCamp
    3. Coursera
    4. edX
    5. Towards Data Science
    6. DataCamp
    7. Awesome Data Science

    AI recommended 7 alternatives but never named PavelGrigoryevDS/awesome-data-analysis. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are the best Python libraries and tools for data manipulation and exploratory data analysis?
    you: not recommended
    AI recommended (in order):
    1. pandas (pandas-dev/pandas)
    2. NumPy (numpy/numpy)
    3. Matplotlib (matplotlib/matplotlib)
    4. Seaborn (mwaskom/seaborn)
    5. Jupyter Notebook (jupyter/notebook)
    6. JupyterLab (jupyterlab/jupyterlab)
    7. Plotly (plotly/plotly.py)
    8. Dash (plotly/dash)
    9. SciPy (scipy/scipy)

    AI recommended 9 alternatives but never named PavelGrigoryevDS/awesome-data-analysis. 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 PavelGrigoryevDS/awesome-data-analysis?
    pass
    AI named PavelGrigoryevDS/awesome-data-analysis explicitly

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

  • If a team adopts PavelGrigoryevDS/awesome-data-analysis in production, what risks or prerequisites should they evaluate first?
    pass
    AI named PavelGrigoryevDS/awesome-data-analysis 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 PavelGrigoryevDS/awesome-data-analysis solve, and who is the primary audience?
    pass
    AI did not name PavelGrigoryevDS/awesome-data-analysis — 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?

Embed your GEO score

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  • Deep reports10 / month
  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite