# jayinai/data-science-question-answer

A repo for data science related questions and answers

Repository: https://github.com/jayinai/data-science-question-answer
Canonical: https://ross.abutalabs.com/products/data-science-question-answer
Language: Jupyter Notebook
License: MIT
License Family: permissive
Topics: data-science, machine-learning, deep-learning, sql, statistics, system, reinforcement-learning
Last push: 2022-10-06T14:35:19+00:00

## Health v2 (maintenance only)
Score: 32/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 35, longevity 100
- inputs: {"age_days": 3153, "days_push": 1427, "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 2409, forks 648 (observed 2026-08-28T04:06:44.289501+00:00)

## What it is
A collection of Jupyter Notebook-based questions and answers covering data science topics such as SQL, statistics, supervised/unsupervised learning, reinforcement learning, NLP, and system design. It is intended as a quick-reference for interview preparation and for beginners learning basic data science concepts.

## Use cases
- prepare for data science interview questions
- quickly review machine learning concepts before an interview
- learn basic data science and statistics concepts as a beginner
- brush up on SQL join types and Spark basics
- review reinforcement learning and NLP fundamentals
- get advice on writing a quantified data science resume

## When to choose
- you need a broad, quick-reference review of data science topics before an interview
- you are new to data science and want concise explanations of core concepts
- you want community-contributed Q&A style notes across ML, SQL, and system design

## When to avoid
- you want in-depth study material on a specific topic
- you need actively maintained content - the repo is deprecated in favor of 'nail-ml-concept'
- you need runnable production code rather than conceptual notes

## Facets
- artifact type: learning-resource
- maturity: abandoned
- function: data-science, machine-learning, deep-learning, nlp, reinforcement-learning
- domain: data-science, machine-learning, tutorials, education
- platform: cross-platform
- tags: interview-preparation, cheat-sheet, jupyter-notebook, sql, statistics, deprecated, quick-reference

## Member repositories
- jayinai/data-science-question-answer (main) score 32

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:06:44.289501+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 2026-08-30T02:34:01.043933+00:00, confidence not recorded.
  - readme: https://github.com/jayinai/data-science-question-answer (fetched 2026-08-28T04:06:44.289501+00:00, sha 7e2e9adf5412)
- Data as of 2026-08-30T08:39:29.467469+00:00.