RRepoGEO

REPOGEO REPORT · LITE

travistangvh/ChatGPT-Data-Science-Prompts

Default branch main · commit 1039b98d · scanned 6/27/2026, 3:33:22 PM

GitHub: 1,616 stars · 280 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)

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

AI VISIBILITY SCORE
17 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 0 warn · 1 fail
Objective metadata checks
AI knows your name
1 / 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 travistangvh/ChatGPT-Data-Science-Prompts, 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 README introduction to clarify it's a prompt collection

    Why:

    CURRENT
    The ChatGPT model is a large language model trained by OpenAI that is capable of generating human-like text. By providing it with a prompt, it can generate responses that continue the conversation or expand on the given prompt. In this repository, you will find prompts that can be used with ChatGPT for data science purposes. It is curated by Travis Tang.
    COPY-PASTE FIX
    This repository is a curated collection of 60 useful data science prompts specifically designed for ChatGPT. It helps data scientists and learners effectively leverage large language models for tasks like code generation, explanation, optimization, and troubleshooting.
  • hightopics#2
    Add relevant GitHub topics

    Why:

    CURRENT
    (none)
    COPY-PASTE FIX
    chatgpt, prompts, data-science, machine-learning, ai, llm, code-generation, data-analysis, python, sql
  • mediumlicense#3
    Add a LICENSE file

    Why:

    CURRENT
    (no LICENSE file detected — the repo has no recognizable license)
    COPY-PASTE FIX
    Create a LICENSE file in the repository root with the MIT License text.

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 travistangvh/ChatGPT-Data-Science-Prompts
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
GitHub Copilot
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. GitHub Copilot · recommended 1×
  2. jupyterlab/jupyter-ai · recommended 1×
  3. OpenAI API · recommended 1×
  4. huggingface/transformers · recommended 1×
  5. Google Colaboratory · recommended 1×
  • CATEGORY QUERY
    How can I leverage large language models for common data science coding tasks?
    you: not recommended
    AI recommended (in order):
    1. GitHub Copilot
    2. Jupyter AI (jupyterlab/jupyter-ai)
    3. OpenAI API
    4. Hugging Face Transformers Library (huggingface/transformers)
    5. Google Colaboratory
    6. Tabnine

    AI recommended 6 alternatives but never named travistangvh/ChatGPT-Data-Science-Prompts. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Where can I find effective prompts for AI-assisted data science code generation and debugging?
    you: not recommended
    AI recommended (in order):
    1. Awesome-ChatGPT-Prompts
    2. Prompt Engineering Guide
    3. OpenAI Cookbook
    4. Kaggle
    5. Towards Data Science
    6. DataCamp
    7. Hugging Face

    AI recommended 7 alternatives but never named travistangvh/ChatGPT-Data-Science-Prompts. This is the gap to close.

    Show full AI answer

Objective checks

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

  • Metadata completeness
    fail

    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 travistangvh/ChatGPT-Data-Science-Prompts?
    pass
    AI did not name travistangvh/ChatGPT-Data-Science-Prompts — 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?

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