RRepoGEO

REPOGEO REPORT · LITE

multigroupco/DMG-Data-Science-Awesome

Default branch main · commit 85971f7a · scanned 6/1/2026, 5:33:11 PM

GitHub: 530 stars · 42 forks

AI VISIBILITY SCORE
28 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 1 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 multigroupco/DMG-Data-Science-Awesome, 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
  • highabout#1
    Update repository description for clarity and keywords

    Why:

    CURRENT
    Source for Who're Interested in Data!
    COPY-PASTE FIX
    A curated 'Awesome List' of essential resources, tools, and frameworks for data science, machine learning, and AI practitioners.
  • highreadme#2
    Add a concise English purpose statement to the README introduction

    Why:

    COPY-PASTE FIX
    This repository is a curated 'Awesome List' providing a comprehensive collection of resources, tools, and frameworks for data science, machine learning, and artificial intelligence enthusiasts and professionals.
  • mediumhomepage#3
    Add a homepage URL to the repository metadata

    Why:

    COPY-PASTE FIX
    https://multigroup.co/

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 multigroupco/DMG-Data-Science-Awesome
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 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Kaggle Learn · recommended 2×
  2. Awesome Data Science · recommended 1×
  3. Towards Data Science · recommended 1×
  4. freeCodeCamp · recommended 1×
  5. DataCamp · recommended 1×
  • CATEGORY QUERY
    Where can I find a comprehensive list of resources for learning data science?
    you: not recommended
    AI recommended (in order):
    1. Awesome Data Science
    2. Kaggle Learn
    3. Towards Data Science
    4. freeCodeCamp
    5. DataCamp
    6. Coursera
    7. edX

    AI recommended 7 alternatives but never named multigroupco/DMG-Data-Science-Awesome. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are the best curated collections of tools and frameworks for machine learning and AI?
    you: not recommended
    AI recommended (in order):
    1. Awesome Machine Learning
    2. Awesome Deep Learning
    3. Papers With Code
    4. Google's AI Platform
    5. Microsoft Azure AI
    6. Hugging Face Transformers Library
    7. Kaggle Learn

    AI recommended 7 alternatives but never named multigroupco/DMG-Data-Science-Awesome. This is the gap to close.

    Show full AI answer

Objective checks

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

  • Metadata completeness
    warn

    Suggestion:

  • 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 multigroupco/DMG-Data-Science-Awesome?
    pass
    AI named multigroupco/DMG-Data-Science-Awesome explicitly

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

  • If a team adopts multigroupco/DMG-Data-Science-Awesome in production, what risks or prerequisites should they evaluate first?
    pass
    AI named multigroupco/DMG-Data-Science-Awesome 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 multigroupco/DMG-Data-Science-Awesome solve, and who is the primary audience?
    pass
    AI did not name multigroupco/DMG-Data-Science-Awesome — 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

Drop this badge into the README of multigroupco/DMG-Data-Science-Awesome. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

RepoGEO badge previewLive preview
MARKDOWN (README)
[![RepoGEO](https://repogeo.com/badge/multigroupco/DMG-Data-Science-Awesome.svg)](https://repogeo.com/en/r/multigroupco/DMG-Data-Science-Awesome)
HTML
<a href="https://repogeo.com/en/r/multigroupco/DMG-Data-Science-Awesome"><img src="https://repogeo.com/badge/multigroupco/DMG-Data-Science-Awesome.svg" alt="RepoGEO" /></a>
Pro

Subscribe to Pro for deep diagnoses

multigroupco/DMG-Data-Science-Awesome — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

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