{"adoption": {"forks": 1211, "observed_at": "2026-08-28T04:08:57.828125+00:00", "stars": 4743}, "canonical_url": "https://ross.abutalabs.com/products/cracking-the-data-science-interview", "card": {"archived": false, "artifact_type": "learning-resource", "description": "A Collection of Cheatsheets, Books, Questions, and Portfolio For DS/ML Interview Prep", "domain": ["data-science", "machine-learning", "tutorials", "education"], "enriched": true, "function": ["data-science", "machine-learning", "deep-learning"], "health_score": 20, "homepage": "https://medium.com/cracking-the-data-science-interview", "language": "Jupyter Notebook", "license": null, "license_family": "other", "maturity": "maintenance", "member_repos": ["khanhnamle1994/cracking-the-data-science-interview"], "name": "khanhnamle1994/cracking-the-data-science-interview", "platform": ["python"], "pushed_at": "2024-08-31T11:22:32+00:00", "repo": "khanhnamle1994/cracking-the-data-science-interview", "stars": 4743, "tags": ["interview-preparation", "cheatsheets", "ebooks", "question-bank", "portfolio", "statistics", "sql"], "topics": ["data-science", "machine-learning", "deep-learning", "data-portfolio", "downloadable-cheatsheets", "statistics", "python", "data-journalism", "concepts", "data-wrangling"], "urls": [], "use_cases": ["prepare for a data science interview", "find machine learning interview questions and answers", "review statistics and probability concepts before an interview", "get downloadable cheatsheets for SQL and ML topics", "build a data science portfolio with example projects", "study deep learning and NLP concepts for job interviews"], "what_it_is": "A curated collection of cheatsheets, ebooks, interview questions, case studies, and portfolio projects for data science and machine learning interview preparation. It is a learning resource repository rather than runnable software.", "when_to_avoid": ["you need production code, libraries, or tools to include in a project", "you want a structured interactive course with exercises and grading", "you need up-to-date material, as some content may be dated"], "when_to_choose": ["you are preparing for data science, ML, or analytics interviews", "you want condensed cheatsheets and curated reading material in one place", "you need example portfolio projects to showcase data skills"]}, "data_as_of": "2026-08-30T08:39:29.467469+00:00", "members": [{"path": "/products/cracking-the-data-science-interview", "repo": "khanhnamle1994/cracking-the-data-science-interview", "role": "main", "score": 32}], "provenance": {"archived": {"kind": "observed", "observed_at": "2026-08-28T04:08:57.828125+00:00", "source": "github"}, "artifact_type": {"confidence": null, "enriched_at": "2026-08-29T18:18:57.028981+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "567a671f3fc284fb45fcf55aa92e17ff930c5b131a8297944531ecc834d2f309", "fetched_at": "2026-08-28T04:08:57.828125+00:00", "kind": "readme", "missing": false, "url": "https://github.com/khanhnamle1994/cracking-the-data-science-interview"}], "taxonomy_version": 1}, "description": {"kind": "observed", "observed_at": "2026-08-28T04:08:57.828125+00:00", "source": "github"}, "domain": {"confidence": null, "enriched_at": "2026-08-29T18:18:57.028981+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "567a671f3fc284fb45fcf55aa92e17ff930c5b131a8297944531ecc834d2f309", "fetched_at": "2026-08-28T04:08:57.828125+00:00", "kind": "readme", "missing": false, "url": "https://github.com/khanhnamle1994/cracking-the-data-science-interview"}], "taxonomy_version": 1}, "enriched": {"inputs": [], "kind": "computed", "method": "enrichment_status"}, "function": {"confidence": null, "enriched_at": "2026-08-29T18:18:57.028981+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "567a671f3fc284fb45fcf55aa92e17ff930c5b131a8297944531ecc834d2f309", "fetched_at": "2026-08-28T04:08:57.828125+00:00", "kind": "readme", "missing": false, "url": "https://github.com/khanhnamle1994/cracking-the-data-science-interview"}], "taxonomy_version": 1}, "health_score": {"inputs": ["days_since_push", "days_since_release", "archived"], "kind": "computed", "method": "health_v1"}, "homepage": {"kind": "observed", "observed_at": "2026-08-28T04:08:57.828125+00:00", "source": "github"}, "language": {"kind": "observed", "observed_at": "2026-08-28T04:08:57.828125+00:00", "source": "github"}, "license": {"kind": "observed", "observed_at": "2026-08-28T04:08:57.828125+00:00", "source": "github"}, "license_family": {"inputs": ["license"], "kind": "computed", "method": "license_family"}, "maturity": {"confidence": null, "enriched_at": "2026-08-29T18:18:57.028981+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "567a671f3fc284fb45fcf55aa92e17ff930c5b131a8297944531ecc834d2f309", "fetched_at": "2026-08-28T04:08:57.828125+00:00", "kind": "readme", "missing": false, "url": "https://github.com/khanhnamle1994/cracking-the-data-science-interview"}], "taxonomy_version": 1}, "member_repos": {"kind": "observed", "observed_at": "2026-08-28T04:08:57.828125+00:00", "source": "github"}, "name": {"kind": "observed", "observed_at": "2026-08-28T04:08:57.828125+00:00", "source": "github"}, "platform": {"confidence": null, "enriched_at": "2026-08-29T18:18:57.028981+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "567a671f3fc284fb45fcf55aa92e17ff930c5b131a8297944531ecc834d2f309", "fetched_at": "2026-08-28T04:08:57.828125+00:00", "kind": "readme", "missing": false, "url": "https://github.com/khanhnamle1994/cracking-the-data-science-interview"}], "taxonomy_version": 1}, "pushed_at": {"kind": "observed", "observed_at": "2026-08-28T04:08:57.828125+00:00", "source": "github"}, "repo": {"kind": "observed", "observed_at": "2026-08-28T04:08:57.828125+00:00", "source": "github"}, "stars": {"kind": "observed", "observed_at": "2026-08-28T04:08:57.828125+00:00", "source": "github"}, "tags": {"confidence": null, "enriched_at": "2026-08-29T18:18:57.028981+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "567a671f3fc284fb45fcf55aa92e17ff930c5b131a8297944531ecc834d2f309", "fetched_at": "2026-08-28T04:08:57.828125+00:00", "kind": "readme", "missing": false, "url": "https://github.com/khanhnamle1994/cracking-the-data-science-interview"}], "taxonomy_version": 1}, "topics": {"kind": "observed", "observed_at": "2026-08-28T04:08:57.828125+00:00", "source": "github"}, "urls": {"kind": "observed", "observed_at": "2026-08-28T04:08:57.828125+00:00", "source": "github"}, "use_cases": {"confidence": null, "enriched_at": "2026-08-29T18:18:57.028981+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "567a671f3fc284fb45fcf55aa92e17ff930c5b131a8297944531ecc834d2f309", "fetched_at": "2026-08-28T04:08:57.828125+00:00", "kind": "readme", "missing": false, "url": "https://github.com/khanhnamle1994/cracking-the-data-science-interview"}], "taxonomy_version": 1}, "what_it_is": {"confidence": null, "enriched_at": "2026-08-29T18:18:57.028981+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "567a671f3fc284fb45fcf55aa92e17ff930c5b131a8297944531ecc834d2f309", "fetched_at": "2026-08-28T04:08:57.828125+00:00", "kind": "readme", "missing": false, "url": "https://github.com/khanhnamle1994/cracking-the-data-science-interview"}], "taxonomy_version": 1}, "when_to_avoid": {"confidence": null, "enriched_at": "2026-08-29T18:18:57.028981+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "567a671f3fc284fb45fcf55aa92e17ff930c5b131a8297944531ecc834d2f309", "fetched_at": "2026-08-28T04:08:57.828125+00:00", "kind": "readme", "missing": false, "url": "https://github.com/khanhnamle1994/cracking-the-data-science-interview"}], "taxonomy_version": 1}, "when_to_choose": {"confidence": null, "enriched_at": "2026-08-29T18:18:57.028981+00:00", "kind": "inferred", "prompt_version": 1, "sources": [{"content_hash": "567a671f3fc284fb45fcf55aa92e17ff930c5b131a8297944531ecc834d2f309", "fetched_at": "2026-08-28T04:08:57.828125+00:00", "kind": "readme", "missing": false, "url": "https://github.com/khanhnamle1994/cracking-the-data-science-interview"}], "taxonomy_version": 1}}, "score": {"components": {"activity": 0, "longevity": 100, "rhythm": 35}, "computed_at": "2026-09-02T17:46:02.011165+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": 2946, "days_push": 732, "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}}