Data Science: A Comprehensive Overview
Not on the map. Why?
Abstract (arXiv)
The twenty-first century has ushered in the age of big data and data economy, in which data DNA, which carries important knowledge, insights and potential, has become an intrinsic constituent of all data-based organisms. An appropriate understanding of data DNA and its organisms relies on the new field of data science and its keystone, analytics. Although it is widely debated whether big data is only hype and buzz, and data science is still in a very early phase, significant challenges and opportunities are emerging or have been inspired by the research, innovation, business, profession, and education of data science. This paper provides a comprehensive survey and tutorial of the fundamental aspects of data science: the evolution from data analysis to data science, the data science concepts, a big picture of the era of data science, the major challenges and directions in data innovation, the nature of data analytics, new industrialization and service opportunities in the data economy, the profession and competency of data education, and the future of data science. This article is the first in the field to draw a comprehensive big picture, in addition to offering rich observations, lessons and thinking about data science and analytics.
Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). First posted on arXiv 1 Jul 2020.
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Citing papers per year: 2021: 2; 2022: 1; 2023: 2; 2024: 0; 2025: 1.
6 papers.
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- 2021Data science and Machine learning in the Clouds: A Perspective for the Futurecited by 2
- 2022Evolution and use of data science vocabulary. How much have we changed in 13 years?cited by 0
- 2023Why Data Science Projects Failcited by 0
- 2025VDSAgents: A PCS-Guided Multi-Agent System for Veridical Data Science Automationcited by 0
- 2025VDSAgents: A PCS-Guided Multi-Agent System for Veridical Data Science Automation28 Oct 2025
- 2023Why Data Science Projects Fail8 Aug 2023
- 2023What Should Data Science Education Do with Large Language Models?6 Jul 2023
- 2022Evolution and use of data science vocabulary. How much have we changed in 13 years?28 Mar 2022
- 2021Data science and Machine learning in the Clouds: A Perspective for the Future2 Sep 2021
- 2021Fits and Starts: Enterprise Use of AutoML and the Role of Humans in the Loop12 Jan 2021