RRepoGEO

REPOGEO REPORT · LITE

PavelGrigoryevDS/awesome-data-analysis

Default branch main · commit 4f180aca · scanned 6/28/2026, 5:27:47 AM

GitHub: 1,491 stars · 223 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
    Reposition the README's opening sentence to explicitly state it's an 'awesome list'

    Why:

    CURRENT
    500+ curated resources for data analysis and data science: tools, libraries, roadmaps, cheatsheets, interview guides and more.
    COPY-PASTE FIX
    This **awesome list** provides a comprehensive, curated collection of 500+ resources for data analysis and data science: tools, libraries, roadmaps, cheatsheets, interview guides, and more.
  • mediumreadme#2
    Add a 'Why This Awesome List?' section to clarify its unique value proposition

    Why:

    COPY-PASTE FIX
    ## ✨ Why This Awesome List?
Unlike interactive learning platforms, this repository offers a hand-picked, organized directory of the best external resources. It saves you time by curating high-quality tools, tutorials, and guides across various data analysis and data science domains, helping you quickly find what you need without sifting through countless options.
  • lowtopics#3
    Add more specific topics related to 'resource lists' or 'directories'

    Why:

    CURRENT
    ai, analytics, awesome-list, big-data, business-intelligence, dashboard, data-analysis, data-science, data-visualization, datasets, eda, jupyter-notebook, ml, numpy, pandas, python, resources, sql, statistics, tutorials
    COPY-PASTE FIX
    ai, analytics, awesome-list, big-data, business-intelligence, dashboard, data-analysis, data-science, data-visualization, datasets, eda, jupyter-notebook, ml, numpy, pandas, python, resources, sql, statistics, tutorials, learning-resources, data-science-resources, data-analysis-resources, curated-list

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
Coursera
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Coursera · recommended 1×
  2. edX · recommended 1×
  3. Kaggle · recommended 1×
  4. DataCamp · recommended 1×
  5. Udemy · recommended 1×
  • CATEGORY QUERY
    Where can I find a comprehensive list of resources for learning data analysis and data science?
    you: not recommended
    AI recommended (in order):
    1. Coursera
    2. edX
    3. Kaggle
    4. DataCamp
    5. Udemy
    6. freeCodeCamp.org
    7. Towards 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. Plotly (plotly/plotly.py)
    6. SciPy (scipy/scipy)
    7. Jupyter Notebook / JupyterLab

    AI recommended 7 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?

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  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite