# business-science/awesome-generative-ai-data-scientist

A curated list of 100+ resources for building and deploying generative AI specifically focusing on helping you become a Generative AI Data Scientist with LLMs

Repository: https://github.com/business-science/awesome-generative-ai-data-scientist
Canonical: https://ross.abutalabs.com/products/awesome-generative-ai-data-scientist
License Family: other
Topics: data-science, generative-ai, ai, ai-engineer, awesome, copilot, data-scientist, gpt, machine-learning, ml-engineer, openai
Last push: 2025-04-10T14:19:55+00:00

## Health v2 (maintenance only)
Score: 20/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 15, release rhythm 8, longevity 53
- inputs: {"age_days": 750, "days_push": 510, "days_rel": 518, "gap_med": null, "n_releases_24m": 1}
- flags: no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1583, forks 259 (observed 2026-08-28T04:05:07.227507+00:00)

## What it is
A curated awesome-list of 100+ free resources for becoming a Generative AI Data Scientist, covering LLMs, AI agents, RAG, fine-tuning, LLMOps, and cloud deployment. It is a reference collection of links rather than software.

## Use cases
- find free resources to learn generative AI for data science
- discover LLM frameworks and agent tools
- learn how to build RAG applications
- find resources on fine-tuning and pretraining LLMs
- learn LLMOps and LLM monitoring and observability
- find vector databases for retrieval-augmented generation
- transition from data scientist to generative AI engineer

## When to choose
- you want a curated starting point for learning generative AI as a data scientist
- you need a directory of Python and R AI libraries, agents, and LLM tooling
- you want free learning materials on LLM deployment and LLMOps

## When to avoid
- you need runnable software or a library rather than a list of links
- you need a structured course or tutorial with guided lessons
- you need up-to-date documentation for a specific tool

## Facets
- artifact type: learning-resource
- maturity: active
- function: developer-tools
- domain: artificial-intelligence, large-language-models, data-science, machine-learning, awesome-lists, tutorials
- platform: cross-platform
- tags: awesome-list, curated-resources, generative-ai, llm, rag, ai-agents, llmops, free-resources

## Member repositories
- business-science/awesome-generative-ai-data-scientist (main) score 20

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:07.227507+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 2026-08-30T03:56:00.159737+00:00, confidence not recorded.
  - readme: https://github.com/business-science/awesome-generative-ai-data-scientist (fetched 2026-08-28T04:05:07.227507+00:00, sha 0c68ee1f769a)
- Data as of 2026-08-30T08:39:29.467469+00:00.