# data science

> field of study to extract insights from data

**Wikidata**: [Q2374463](https://www.wikidata.org/wiki/Q2374463)  
**Wikipedia**: [English](https://en.wikipedia.org/wiki/Data_science)  
**Source**: https://4ort.xyz/entity/data-science

## Summary  
Data science is a field of study focused on extracting insights from data using techniques from statistics, applied mathematics, and computer science. It encompasses disciplines like analytics, data engineering, and data visualization to interpret meaningful patterns. Practitioners, known as data scientists, apply these methods across industries to inform decision-making.

## Key Facts  
- **Parent Fields**: Part of analytics, informatics (inception: 1957), data engineering, and data ethics.  
- **Practiced By**: Data scientists and data architects.  
- **Tools Used**: Statistics, computer science, data visualization, and statistical thinking.  
- **Related Software**: PolyAnalyst (inception: 1994), SaQC, DataSHIELD.  
- **Subreddit**: r/datascience (created: 2011-08-06).  
- **Wikidata Sitelinks**: 57 linked pages.  
- **YSO ID**: 29172 (aliases: "datatiede," "data science").  
- **GitHub Topic**: "data-science."  
- **Instance Of**: Branch of science, academic discipline.  

## FAQs  
### Q: What is the difference between data science and analytics?  
A: Data science is broader, combining statistics, programming, and domain expertise to extract insights, while analytics focuses specifically on discovering and interpreting data patterns.  

### Q: What tools do data scientists use?  
A: Data scientists use statistics, programming languages (e.g., Python, R), and tools like PolyAnalyst for predictive analytics and SaQC for data quality control.  

### Q: What industries employ data science?  
A: Data science is applied in tech (e.g., AI, cybersecurity), healthcare, finance, and social sciences, with companies like Aristek Systems and Whitecyber specializing in it.  

## Why It Matters  
Data science transforms raw data into actionable insights, driving innovation in fields like artificial intelligence, healthcare, and business intelligence. By combining statistical analysis, machine learning, and domain expertise, it enables organizations to make data-driven decisions, optimize processes, and predict trends. Its ethical dimensions, such as responsible data science and data feminism, address societal impacts, ensuring fairness and transparency. The field’s interdisciplinary nature bridges gaps between technical and non-technical domains, making it indispensable in the digital age.  

## Notable For  
- **Interdisciplinary Approach**: Combines statistics, computer science, and domain knowledge.  
- **Ethical Focus**: Includes subfields like data ethics and responsible data science.  
- **Wide Applications**: Used in AI, geospatial analysis, and social data science.  
- **Tool Diversity**: Supports tools like PolyAnalyst and DataSHIELD for varied use cases.  
- **Global Reach**: Practiced by companies worldwide, including Aristek Systems (Lithuania) and Whitecyber (Indonesia).  

## Body  
### Parent Fields  
Data science is part of several broader disciplines:  
- **Analytics**: Focuses on discovering patterns in data (24 sitelinks).  
- **Informatics**: Studies computational systems for data storage (inception: 1957; 35 sitelinks).  
- **Data Engineering**: Builds systems to collect and process data (6 sitelinks).  

### Related Entities  
- **Organizations**: Aristek Systems (founded: 2001, Lithuania) and Whitecyber (founded: 2006, Indonesia) specialize in data science and AI.  
- **Software**: PolyAnalyst (1994) for predictive analytics; SaQC for data quality control.  
- **People**: Notable practitioners include Federico Neri (data scientist) and Lior Rokach (computer scientist).  

### Technical Properties  
- **Uses**: Statistics, applied mathematics, data visualization.  
- **Classifications**:  
  - YSO ID: 29172 ("datatiede").  
  - Mesh Descriptor: D000077488 ("Data Science").  
  - ANSZRC 2020 Code: 490508 ("Statistical data science").  

### Online Presence  
- **Subreddit**: r/datascience (since 2011).  
- **Stack Exchange**: datascience.stackexchange.com.  
- **GitHub Topic**: "data-science."  

## Schema Markup  
```json
{
  "@context": "https://schema.org",
  "@type": "Thing",
  "name": "Data science",
  "description": "Field of study focused on extracting insights from data using statistics, computer science, and domain expertise.",
  "sameAs": [
    "https://www.wikidata.org/wiki/Q2374463",
    "https://en.wikipedia.org/wiki/Data_science"
  ],
  "additionalType": "Branch of science"
}

## References

1. Freebase Data Dumps. 2013
2. YSO-Wikidata mapping project
3. [Source](https://kanger.dev/career/data-scientist)
4. [Source](https://kanger.dev/career/data-architect)
5. Quora
6. [Source](https://vocabs.ardc.edu.au/viewById/316)
7. [Data science / Twitter](https://twitter.com/i/topics/930484568305541120)
8. [OpenAlex](https://docs.openalex.org/download-snapshot/snapshot-data-format)
9. [Best Data Science Posts - Reddit](https://www.reddit.com/t/data_science/)