# Moataz-Elmesmary/Data-Science-Roadmap

Data Science Roadmap from A to Z

Repository: https://github.com/Moataz-Elmesmary/Data-Science-Roadmap
Canonical: https://ross.abutalabs.com/products/data-science-roadmap
Homepage: https://www.linkedin.com/posts/moatazelmesmary_github-moataz-elmesmarydata-science-roadmap-activity-6974293994960769024-yCdi?utm_source=share&utm_medium=member_desktop
License: MIT
License Family: permissive
Topics: data-analysis, data-engineering, data-science, data-visualization, deep-learning, machine-learning, mathematics, probability, python, sql, statistics, cheatsheet, cv-template, interview-questions, linear-algebra, neural-network, big-data, chatgpt, llms, nlp
Last push: 2025-12-06T20:01:50+00:00

## Health v2 (maintenance only)
Score: 57/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 55, release rhythm 35, longevity 100
- inputs: {"age_days": 1599, "days_push": 270, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 4352, forks 616 (observed 2026-08-28T04:08:46.325261+00:00)

## What it is
A free self-learning roadmap repository guiding learners into data science from A to Z, curating the best free resources, videos, and cheatsheets. It covers the distinctions between data science, analytics, and engineering, plus topics like Python, SQL, statistics, machine learning, deep learning, NLP, and LLMs.

## Use cases
- break into a data science career with a structured learning path
- find free resources to learn machine learning and deep learning
- prepare for data science interviews with question collections
- learn statistics, linear algebra, and probability for data science
- compare data science vs data analytics vs data engineering roles
- get a data science CV template and cheatsheets
- learn Python and SQL for data analysis

## When to choose
- you want a curated, free, step-by-step self-study path into data science
- you need interview prep materials and CV templates alongside learning resources
- you are a beginner unsure which data career track fits you

## When to avoid
- you need runnable software, libraries, or tools rather than learning material
- you want an accredited course with mentorship and certification
- you need deep, exhaustive coverage of a single advanced topic

## Facets
- artifact type: learning-resource
- maturity: active
- function: data-science, machine-learning, deep-learning, data-visualization, nlp
- domain: data-science, machine-learning, tutorials, education
- platform: cross-platform
- tags: roadmap, self-learning, cheatsheet, interview-questions, cv-template, statistics, sql, python, free-resources

## Member repositories
- Moataz-Elmesmary/Data-Science-Roadmap (main) score 57

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:08:46.325261+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T18:21:31.375888+00:00, confidence not recorded.
  - readme: https://github.com/Moataz-Elmesmary/Data-Science-Roadmap (fetched 2026-08-28T04:08:46.325261+00:00, sha f3edc3f842ba)
- Data as of 2026-08-30T08:39:29.467469+00:00.