---
id: data-science-fundamentals
title: "Data Science: Methods, Tools, and Best Practices"
schema_type: Article
category: science
language: en
confidence: low
last_verified: "2026-05-28"
created_date: "2026-05-24"
generation_method: ai_structured
ai_models:
  - claude-opus
derived_from_human_seed: true
conflict_of_interest: none_declared
is_live_document: false
data_period: static
atomic_facts:
  - id: fact-data-science-1
    statement: >-
      Tidy data organizes each variable as a column, each observation as a row, and each
      observational unit as a table.
    source_title: Tidy Data
    source_url: https://doi.org/10.18637/jss.v059.i10
    confidence: low
  - id: fact-data-science-2
    statement: >-
      Scikit-learn provides machine-learning tools for supervised and unsupervised learning in
      Python.
    source_title: "Scikit-learn: Machine Learning in Python"
    source_url: https://www.jmlr.org/papers/v12/pedregosa11a.html
    confidence: low
  - id: fact-data-science-3
    statement: >-
      NIST frames big-data work around data lifecycle and architecture concerns such as collection,
      preparation, analytics, and access.
    source_title: "NIST Big Data Interoperability Framework: Volume 1, Definitions"
    source_url: https://doi.org/10.6028/NIST.SP.1500-1r2
    confidence: low
completeness: 0.9
known_gaps:
  - This compact repair keeps only source-mapped public claims from the sampled audit entry.
disputed_statements: []
primary_sources:
  - title: Tidy Data
    type: academic_paper
    year: 2014
    url: https://doi.org/10.18637/jss.v059.i10
    doi: 10.18637/jss.v059.i10
    institution: Journal of Statistical Software
  - title: "Scikit-learn: Machine Learning in Python"
    type: academic_paper
    year: 2011
    url: https://www.jmlr.org/papers/v12/pedregosa11a.html
    institution: Journal of Machine Learning Research
  - title: "NIST Big Data Interoperability Framework: Volume 1, Definitions"
    type: government_report
    year: 2019
    url: https://doi.org/10.6028/NIST.SP.1500-1r2
    doi: 10.6028/NIST.SP.1500-1r2
    institution: National Institute of Standards and Technology
secondary_sources: []
updated: "2026-05-28"
---

## TL;DR

Data science fundamentals combine data organization, statistical learning tools, and reproducible analysis. This repair narrows the article to source-mapped methods claims.

## Core Explanation

The previous entry had weak coverage and generic sources. The repaired version uses tidy data, scikit-learn, and NIST big-data material.

## Further Reading

- [Tidy Data](https://doi.org/10.18637/jss.v059.i10)
- [Scikit-learn: Machine Learning in Python](https://www.jmlr.org/papers/v12/pedregosa11a.html)
- [NIST Big Data Interoperability Framework: Volume 1, Definitions](https://doi.org/10.6028/NIST.SP.1500-1r2)