{"adoption": {"forks": 676, "observed_at": "2026-08-28T04:03:21.626734+00:00", "stars": 1046}, "canonical_url": "https://ross.abutalabs.com/products/interview-prepartion-data-science", "card": {"archived": false, "artifact_type": "learning-resource", "description": null, "domain": ["data-science", "education", "machine-learning", "tutorials"], "enriched": true, "function": ["data-science", "developer-tools"], "health_score": 20, "homepage": null, "language": "Jupyter Notebook", "license": null, "license_family": "other", "maturity": "maintenance", "member_repos": ["krishnaik06/Interview-Prepartion-Data-Science"], "name": "krishnaik06/Interview-Prepartion-Data-Science", "platform": ["python", "cross-platform"], "pushed_at": "2024-01-12T20:35:35+00:00", "repo": "krishnaik06/Interview-Prepartion-Data-Science", "stars": 1046, "tags": ["interview-preparation", "jupyter-notebooks", "data-science-interview", "study-material", "questions-and-answers"], "topics": [], "urls": [], "use_cases": ["prepare for a data science interview", "review machine learning interview questions", "practice common data science concepts before an interview", "study statistics and ML theory for job interviews", "find example data science interview notebooks", "brush up on Python data science topics for interviews"], "what_it_is": "A collection of Jupyter Notebook study materials for preparing for data science interviews. It covers common interview questions and concepts across data science, machine learning, and related topics.", "when_to_avoid": ["you need a production library or tool rather than study material", "you need structured courses with assessments or certification", "you need up-to-date content guaranteed to reflect current interview trends, as updates are infrequent"], "when_to_choose": ["you are preparing for data science or ML interviews and want curated notebook-based study material", "you prefer learning through Jupyter Notebooks with worked examples", "you want a free community-maintained question bank for data science roles"]}, "data_as_of": "2026-08-30T08:39:29.467469+00:00", "members": [{"path": "/products/interview-prepartion-data-science", "repo": "krishnaik06/Interview-Prepartion-Data-Science", "role": "main", "score": 32}], "provenance": {"archived": {"kind": "observed", "observed_at": "2026-08-28T04:03:21.626734+00:00", "source": "github"}, "artifact_type": {"confidence": null, "enriched_at": "2026-08-30T07:01:57.523405+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "6eacedf2379e9c2e5bbf3cf37e6bf32a564fedbbaeafbc893e9185dd1418287f", "fetched_at": "2026-08-28T04:03:21.626734+00:00", "kind": "readme", "missing": false, "url": "https://github.com/krishnaik06/Interview-Prepartion-Data-Science"}], "taxonomy_version": 1}, "description": {"kind": "observed", "observed_at": "2026-08-28T04:03:21.626734+00:00", "source": "github"}, "domain": {"confidence": null, "enriched_at": "2026-08-30T07:01:57.523405+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "6eacedf2379e9c2e5bbf3cf37e6bf32a564fedbbaeafbc893e9185dd1418287f", "fetched_at": "2026-08-28T04:03:21.626734+00:00", "kind": "readme", "missing": false, "url": "https://github.com/krishnaik06/Interview-Prepartion-Data-Science"}], "taxonomy_version": 1}, "enriched": {"inputs": [], "kind": "computed", "method": "enrichment_status"}, "function": {"confidence": null, "enriched_at": "2026-08-30T07:01:57.523405+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "6eacedf2379e9c2e5bbf3cf37e6bf32a564fedbbaeafbc893e9185dd1418287f", "fetched_at": "2026-08-28T04:03:21.626734+00:00", "kind": "readme", "missing": false, "url": "https://github.com/krishnaik06/Interview-Prepartion-Data-Science"}], "taxonomy_version": 1}, "health_score": {"inputs": ["days_since_push", "days_since_release", "archived"], "kind": "computed", "method": "health_v1"}, "homepage": {"kind": "observed", "observed_at": "2026-08-28T04:03:21.626734+00:00", "source": "github"}, "language": {"kind": "observed", "observed_at": "2026-08-28T04:03:21.626734+00:00", "source": "github"}, "license": {"kind": "observed", "observed_at": "2026-08-28T04:03:21.626734+00:00", "source": "github"}, "license_family": {"inputs": ["license"], "kind": "computed", "method": "license_family"}, "maturity": {"confidence": null, "enriched_at": "2026-08-30T07:01:57.523405+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "6eacedf2379e9c2e5bbf3cf37e6bf32a564fedbbaeafbc893e9185dd1418287f", "fetched_at": "2026-08-28T04:03:21.626734+00:00", "kind": "readme", "missing": false, "url": "https://github.com/krishnaik06/Interview-Prepartion-Data-Science"}], "taxonomy_version": 1}, "member_repos": {"kind": "observed", "observed_at": "2026-08-28T04:03:21.626734+00:00", "source": "github"}, "name": {"kind": "observed", "observed_at": "2026-08-28T04:03:21.626734+00:00", "source": "github"}, "platform": {"confidence": null, "enriched_at": "2026-08-30T07:01:57.523405+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "6eacedf2379e9c2e5bbf3cf37e6bf32a564fedbbaeafbc893e9185dd1418287f", "fetched_at": "2026-08-28T04:03:21.626734+00:00", "kind": "readme", "missing": false, "url": "https://github.com/krishnaik06/Interview-Prepartion-Data-Science"}], "taxonomy_version": 1}, "pushed_at": {"kind": "observed", "observed_at": "2026-08-28T04:03:21.626734+00:00", "source": "github"}, "repo": {"kind": "observed", "observed_at": "2026-08-28T04:03:21.626734+00:00", "source": "github"}, "stars": {"kind": "observed", "observed_at": "2026-08-28T04:03:21.626734+00:00", "source": "github"}, "tags": {"confidence": null, "enriched_at": "2026-08-30T07:01:57.523405+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "6eacedf2379e9c2e5bbf3cf37e6bf32a564fedbbaeafbc893e9185dd1418287f", "fetched_at": "2026-08-28T04:03:21.626734+00:00", "kind": "readme", "missing": false, "url": "https://github.com/krishnaik06/Interview-Prepartion-Data-Science"}], "taxonomy_version": 1}, "topics": {"kind": "observed", "observed_at": "2026-08-28T04:03:21.626734+00:00", "source": "github"}, "urls": {"kind": "observed", "observed_at": "2026-08-28T04:03:21.626734+00:00", "source": "github"}, "use_cases": {"confidence": null, "enriched_at": "2026-08-30T07:01:57.523405+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "6eacedf2379e9c2e5bbf3cf37e6bf32a564fedbbaeafbc893e9185dd1418287f", "fetched_at": "2026-08-28T04:03:21.626734+00:00", "kind": "readme", "missing": false, "url": "https://github.com/krishnaik06/Interview-Prepartion-Data-Science"}], "taxonomy_version": 1}, "what_it_is": {"confidence": null, "enriched_at": "2026-08-30T07:01:57.523405+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "6eacedf2379e9c2e5bbf3cf37e6bf32a564fedbbaeafbc893e9185dd1418287f", "fetched_at": "2026-08-28T04:03:21.626734+00:00", "kind": "readme", "missing": false, "url": "https://github.com/krishnaik06/Interview-Prepartion-Data-Science"}], "taxonomy_version": 1}, "when_to_avoid": {"confidence": null, "enriched_at": "2026-08-30T07:01:57.523405+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "6eacedf2379e9c2e5bbf3cf37e6bf32a564fedbbaeafbc893e9185dd1418287f", "fetched_at": "2026-08-28T04:03:21.626734+00:00", "kind": "readme", "missing": false, "url": "https://github.com/krishnaik06/Interview-Prepartion-Data-Science"}], "taxonomy_version": 1}, "when_to_choose": {"confidence": null, "enriched_at": "2026-08-30T07:01:57.523405+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "6eacedf2379e9c2e5bbf3cf37e6bf32a564fedbbaeafbc893e9185dd1418287f", "fetched_at": "2026-08-28T04:03:21.626734+00:00", "kind": "readme", "missing": false, "url": "https://github.com/krishnaik06/Interview-Prepartion-Data-Science"}], "taxonomy_version": 1}}, "score": {"components": {"activity": 0, "longevity": 100, "rhythm": 35}, "computed_at": "2026-09-03T02:20:16.233290+00:00", "flags": ["no_releases", "no_license"], "formula": "round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)", "inputs": {"age_days": 2172, "days_push": 964, "days_rel": null, "gap_med": null, "n_releases_24m": 0}, "score": 32, "version": 2}, "staleness": {"enrichment_outdated": false, "low_confidence": false, "scrape_days": 9, "stale_scrape": false}}