REPOGEO REPORT · LITE

r0f1/datascience

Default branch master · commit ff643940 · scanned 6/19/2026, 11:12:59 AM

GitHub: 4,634 stars · 711 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

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 r0f1/datascience, 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 opening to clarify its community-oriented nature

Why:

CURRENT

> A curated list of awesome resources for practicing data science using Python, including not only libraries, but also links to tutorials, code snippets, blog posts and talks.

COPY-PASTE FIX

> A community-curated list of awesome resources for practicing data science using Python, including not only libraries, but also links to tutorials, code snippets, blog posts and talks. This list is regularly updated and maintained to serve the broader data science community.

mediumhomepage#2
Add a homepage URL to the repository metadata
Why:
COPY-PASTE FIX
```
https://github.com/r0f1/datascience
```

lowreadme#3

Add a 'Why this list?' or 'Comparison' section to the README

Why:

COPY-PASTE FIX

## Why this list?
This list focuses specifically on Python resources for data science, offering a curated selection of libraries, tutorials, and best practices. Unlike broader 'awesome' lists, we prioritize depth and relevance within the Python data science ecosystem. We aim to be a comprehensive, community-driven resource, distinct from personal project collections or single-library repositories.

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 r0f1/datascience

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

pytorch/pytorch

Recommended in 2 of 2 queries

COMPETITOR LEADERBOARD

pytorch/pytorch · recommended 2×
tensorflow/tensorflow · recommended 2×
vinta/awesome-python · recommended 1×
josephmisiti/awesome-machine-learning · recommended 1×
Kaggle Learn · recommended 1×

CATEGORY QUERY
Where can I find a curated list of Python resources for deep learning and statistical analysis?
you: not recommended
AI recommended (in order):
1. Awesome Python (vinta/awesome-python)
2. Awesome Machine Learning (josephmisiti/awesome-machine-learning)
3. Kaggle Learn
4. Towards Data Science
5. PyTorch (pytorch/pytorch)
6. TensorFlow (tensorflow/tensorflow)
AI recommended 6 alternatives but never named r0f1/datascience. This is the gap to close.
Show full AI answer
CATEGORY QUERY
Seeking recommended Python libraries for data preprocessing, visualization, and machine learning workflows.
you: not recommended
AI recommended (in order):
1. Pandas (pandas-dev/pandas)
2. NumPy (numpy/numpy)
3. Scikit-learn (scikit-learn/scikit-learn)
4. Matplotlib (matplotlib/matplotlib)
5. Seaborn (mwaskom/seaborn)
6. Plotly (plotly/plotly.py)
7. TensorFlow (tensorflow/tensorflow)
8. PyTorch (pytorch/pytorch)
AI recommended 8 alternatives but never named r0f1/datascience. 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 r0f1/datascience?
pass
AI did not name r0f1/datascience — 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 r0f1/datascience in production, what risks or prerequisites should they evaluate first?
pass
AI named r0f1/datascience 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 r0f1/datascience solve, and who is the primary audience?
pass
AI named r0f1/datascience explicitly
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 r0f1/datascience. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

Live preview

MARKDOWN (README)

[![RepoGEO](https://repogeo.com/badge/r0f1/datascience.svg)](https://repogeo.com/en/r/r0f1/datascience)

HTML

<a href="https://repogeo.com/en/r/r0f1/datascience"><img src="https://repogeo.com/badge/r0f1/datascience.svg" alt="RepoGEO" /></a>

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