data science
Let an agent explain data science and its components, relay methods, tools and workflows from technical and academic sources, describe subfields and critical perspectives, and distinguish data science from statistics, machine learning, data engineering and business intelligence.
Research draft, second pass
A second pass drafted this model: the structure a model of this thing needs, and what is known about it in the world. The line under this one says how the second half was obtained - researched against sources, or recalled without web access, in which case nothing here was read anywhere and every claim is a lead to verify. Unreviewed either way.
written by Claude from model knowledge without web access - no source was read, every claim is a lead to verify
Researched by: Claude
Purpose and description
Let an agent explain data science and its components, relay methods, tools and workflows from technical and academic sources, describe subfields and critical perspectives, and distinguish data science from statistics, machine learning, data engineering and business intelligence.
An interdisciplinary field that uses statistics, computing, machine learning and domain knowledge to extract insight from data, encompassing data engineering that builds pipelines and infrastructure, analysis and modelling, data visualisation and computational design, spatial and geospatial data science applied to location data, media intelligence applied to news and social media, and critical approaches such as data feminism that examine power and bias; data science underpins decision-making in business, science and government and raises questions of privacy, fairness and reproducibility.
What it is for: Extracting insight from data.
It can be explain components and workflow; relay methods and tools; describe subfields and critiques; distinguish related fields.
Distinguishing features
Interdisciplinary
Data-driven
Computational scale
Ethical questions
What it looks like
Not a visible object; code, models and visualisations.
Physical character
term popularised: 2000s period
common languages: Python, R, SQL list
workflow stages: collection, cleaning, analysis, modelling, communication list
How it is recognised
Insight from data through statistics and computing
Data engineering, visualisation and computational design, geospatial and spatial data science, media intelligence, data feminism
Statistics is a foundation; machine learning is a method family; business intelligence is reporting-focused
Related models
is a kind of - in registry terms
draws on - and computer science
uses - for modelling
is contrasted with - reporting-focused analysis
In practice
Families and kinds
data engineering and pipelines
exploratory analysis and statistics
machine learning and predictive modelling
data visualisation and computational design
spatial and geospatial data science
media intelligence and text analytics
critical data studies including data feminism
Standards and regulation
Data protection laws such as the GDPR
Research reproducibility and ethics guidelines
Emerging AI and algorithmic accountability regulations
Failure modes and hazards
Biased data and models
Privacy violations
Overclaiming from correlations
Confusing data science with its component fields
Also called
Where this came from
wikidata · CC0 1.0
Drafted structure
Bundle to layer to finding to question, as the second pass will find it: 4 bundles · 8 layers · 8 findings · 16 questions.
Understand What data science is.
Definition.
Definition
Definition.
Definition
Definition.
- What is data science, and how does it differ from statistics, machine learning, data engineering and business intelligence? definition
- Is the question about data science in general, a subfield, a tool or a specific project? boundary
Subfields
Subfields.
Subfields
Subfields.
- What are data engineering, visualisation and computational design, geospatial data science, media intelligence and data feminism? definition
- Which entry fits the specific subfield? action
Practice Practice.
Practice.
Workflow
Workflow.
Workflow
Workflow.
- How does a data science project run from data collection and cleaning to modelling and communication? action
- Which references are standard? provenance
Tools
Tools.
Tools
Tools.
- What languages, libraries and platforms are used, and how are they chosen? provenance
- Which sources are cited? provenance
Ethics Ethics and critique.
Attribution.
Ethics
Privacy and fairness.
Ethics
Ethics.
- What privacy, fairness and accountability issues arise, and how are they addressed, with positions attributed? provenance
- Is the presentation neutral? boundary
Critique
Critical perspectives.
Critique
Critique.
- What do data feminism and critical data studies argue about power in data, with positions attributed? provenance
- Which entry fits critical data studies? action
Context Careers and history.
Context.
Careers
Careers.
Careers
Careers.
- What roles exist in data science, and how do people train for them? provenance
- Which entry fits data scientist? action
History
History.
History
History.
- How did data science emerge from statistics, databases and machine learning? provenance
- Which entry fits the history of data science? action
What the second pass must settle
- Should data engineering and geospatial data science be separate primary entries?
- How should technical and academic sources be linked?
- The registry entry has merged aliases naming subfields and a critical movement; should they be split off?