RRepoGEO

REPOGEO REPORT · LITE

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

Default branch main · commit c404cd2e · scanned 5/12/2026, 3:03:14 AM

GitHub: 4,050 stars · 1,537 forks

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

3 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, year-long roadmap for aspiring data scientists in 2025, featuring curated learning paths, resources, and interview preparation.
  • hightopics#2
    Add relevant topics to improve categorization

    Why:

    COPY-PASTE FIX
    data-science, roadmap, learning-path, data-scientist, career-path, machine-learning, python, statistics, eda, interview-prep
  • mediumreadme#3
    Add a clear introductory sentence to the README

    Why:

    CURRENT
    # Perfect Roadmap To Learn Data Science In 2025
    COPY-PASTE FIX
    # Perfect Roadmap To Learn Data Science In 2025

This repository offers a comprehensive, step-by-step learning roadmap designed for individuals aspiring to become data scientists in 2025, guiding you through essential skills and resources.

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 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Automate the Boring Stuff with Python · recommended 1×
  2. Python for Everybody · recommended 1×
  3. LeetCode · recommended 1×
  4. Khan Academy · recommended 1×
  5. Practical Statistics for Data Scientists · recommended 1×
  • CATEGORY QUERY
    Seeking a complete learning roadmap for becoming a data scientist in the coming year.
    you: not recommended
    AI recommended (in order):
    1. Automate the Boring Stuff with Python
    2. Python for Everybody
    3. LeetCode
    4. Khan Academy
    5. Practical Statistics for Data Scientists
    6. Pandas (pandas-dev/pandas)
    7. NumPy (numpy/numpy)
    8. Python for Data Analysis
    9. An Introduction to Statistical Learning with Applications in R (ISLR)
    10. Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow
    11. Scikit-learn (scikit-learn/scikit-learn)
    12. Matplotlib (matplotlib/matplotlib)
    13. Seaborn (mwaskom/seaborn)
    14. Plotly/Dash
    15. SQLZoo
    16. Mode Analytics SQL Tutorial
    17. PostgreSQL (postgres/postgres)
    18. Deep Learning Specialization by Andrew Ng
    19. TensorFlow/Keras (tensorflow/tensorflow)
    20. PyTorch (pytorch/pytorch)
    21. Apache Spark (apache/spark)
    22. AWS S3
    23. EC2
    24. SageMaker
    25. MLflow (mlflow/mlflow)
    26. Docker (docker/docker-ce)
    27. Tableau Public
    28. PowerPoint
    29. Google Slides
    30. Kaggle
    31. GitHub
    32. Reddit's r/datascience
    33. Stack Overflow

    AI recommended 33 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
    What are the best structured learning paths for mastering data science concepts and tools?
    you: not recommended
    AI recommended (in order):
    1. Coursera Specializations and Professional Certificates
    2. IBM Data Science Professional Certificate
    3. Google Advanced Data Analytics Professional Certificate
    4. DeepLearning.AI TensorFlow Developer Professional Certificate
    5. University of Michigan's Applied Data Science with Python Specialization
    6. DataCamp Career Tracks
    7. Data Scientist with Python Career Track
    8. Data Scientist with R Career Track
    9. Udacity Nanodegree Programs
    10. Data Scientist Nanodegree
    11. Data Analyst Nanodegree
    12. edX MicroMasters Programs
    13. MITx MicroMasters Program in Statistics and Data Science
    14. ColumbiaX MicroMasters Program in Data Science
    15. Kaggle Learn
    16. Fast.ai Practical Deep Learning for Coders
    17. Google's Machine Learning Crash Course

    AI recommended 17 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?

Embed your GEO score

Drop this badge into the README of krishnaik06/Perfect-Roadmap-To-Learn-Data-Science-In-2025. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

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