RRepoGEO

REPOGEO REPORT · LITE

krishnaik06/Perfect-Roadmap-To-Learn-Data-Science-In-2025

Default branch main · commit c404cd2e · scanned 6/22/2026, 9:18:38 AM

GitHub: 4,077 stars · 1,538 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
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 krishnaik06/Perfect-Roadmap-To-Learn-Data-Science-In-2025, 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

2 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • highabout#1
    Add a concise repository description

    Why:

    COPY-PASTE FIX
    A comprehensive, structured roadmap and curated resource list to learn Data Science in 2025, covering Python, Statistics, Machine Learning, and more for aspiring data scientists.
  • mediumreadme#2
    Add a brief introductory paragraph to the README

    Why:

    CURRENT
    # Perfect Roadmap To Learn Data Science In 2025 [](https://youtu.be/N7RU6W4hAMI)

## Work Of Data Scientist?
    COPY-PASTE FIX
    # Perfect Roadmap To Learn Data Science In 2025 [](https://youtu.be/N7RU6W4hAMI)

This repository provides a comprehensive and structured roadmap for aspiring data scientists to learn the essential skills and concepts in 2025. It curates a step-by-step learning path, including resources for Python programming, statistics, machine learning, and more, designed to guide beginners through their data science journey.

## Work Of Data Scientist?

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 krishnaik06/Perfect-Roadmap-To-Learn-Data-Science-In-2025
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Automate the Boring Stuff with Python
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Automate the Boring Stuff with Python · recommended 2×
  2. Khan Academy · recommended 2×
  3. Coursera · recommended 2×
  4. apache/spark · recommended 2×
  5. Python for Data Analysis · recommended 1×
  • CATEGORY QUERY
    What is a comprehensive learning path to become a data scientist in 2025?
    you: not recommended
    AI recommended (in order):
    1. Automate the Boring Stuff with Python
    2. Python for Data Analysis
    3. Pandas (pandas-dev/pandas)
    4. SQL for Data Analysis by Mode Analytics
    5. LeetCode SQL
    6. HackerRank SQL
    7. Khan Academy
    8. Coursera
    9. NumPy (numpy/numpy)
    10. Matplotlib (matplotlib/matplotlib)
    11. Seaborn (mwaskom/seaborn)
    12. Plotly (plotly/plotly.py)
    13. Jupyter Notebooks (jupyter/notebook)
    14. JupyterLab (jupyterlab/jupyterlab)
    15. Practical Statistics for Data Scientists
    16. An Introduction to Statistical Learning (ISLR)
    17. Machine Learning Yearning
    18. Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow
    19. Scikit-learn (scikit-learn/scikit-learn)
    20. TensorFlow (tensorflow/tensorflow)
    21. Keras (keras-team/keras)
    22. Deep Learning with Python
    23. PyTorch (pytorch/pytorch)
    24. AWS Sagemaker
    25. Google Cloud AI Platform
    26. Azure Machine Learning
    27. Apache Spark (apache/spark)
    28. PySpark (apache/spark)
    29. Databricks
    30. MLflow (mlflow/mlflow)
    31. Kubeflow (kubeflow/kubeflow)
    32. Docker (docker/docker)
    33. FastAPI (tiangolo/fastapi)
    34. Hugging Face Transformers (huggingface/transformers)
    35. OpenCV (opencv/opencv)
    36. Kaggle
    37. GitHub
    38. Medium
    39. Streamlit (streamlit/streamlit)
    40. Dash (plotly/dash)

    AI recommended 40 alternatives but never named krishnaik06/Perfect-Roadmap-To-Learn-Data-Science-In-2025. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Where can I find resources to learn Python and statistics for data science beginners?
    you: not recommended
    AI recommended (in order):
    1. DataCamp
    2. Coursera
    3. Python for Everybody Specialization
    4. Statistics with Python Specialization
    5. Kaggle Learn
    6. freeCodeCamp
    7. Automate the Boring Stuff with Python
    8. Khan Academy
    9. Think Stats: Exploratory Data Analysis in Python

    AI recommended 9 alternatives but never named krishnaik06/Perfect-Roadmap-To-Learn-Data-Science-In-2025. 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 krishnaik06/Perfect-Roadmap-To-Learn-Data-Science-In-2025?
    pass
    AI did not name krishnaik06/Perfect-Roadmap-To-Learn-Data-Science-In-2025 — 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 krishnaik06/Perfect-Roadmap-To-Learn-Data-Science-In-2025 in production, what risks or prerequisites should they evaluate first?
    pass
    AI named krishnaik06/Perfect-Roadmap-To-Learn-Data-Science-In-2025 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 krishnaik06/Perfect-Roadmap-To-Learn-Data-Science-In-2025 solve, and who is the primary audience?
    pass
    AI did not name krishnaik06/Perfect-Roadmap-To-Learn-Data-Science-In-2025 — 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