[Paper Review] Data Science: A Comprehensive Overview

Longbing Cao|arXiv (Cornell University)|Jul 1, 2020

Big Data and Business IntelligenceBusiness, Management and Accounting82 references213 citations

TL;DR

This paper provides a comprehensive survey of data science, tracing its evolution from data analysis to data science, outlining the era’s features, challenges, opportunities, and implications for education, economy, and profession.

ABSTRACT

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.

Motivation & Objective

• Motivate the need to understand data science in the era of big data and data economy.
• Define data science and distinguish it from related terms such as data analysis, data analytics, and big data.
• Present a big-picture view of data science, including its research, economy, profession, and education.
• Identify major challenges, directions, and opportunities in data-driven innovation and data education.

Proposed method

• Conduct a comprehensive survey of the journey from statistics/data analysis to data science.
• Clarify and compare key terminology in data science (data science, data analytics, advanced analytics, etc.).
• Describe the era of data science, including government initiatives, industry trends, and educational implications.
• Synthesize the concept of data products and the data-to-knowledge-to-wisdom pathway.
• Outline the scientific agenda and institutional developments that support data science research and education.

Experimental results

Research questions

• RQ1 What is data science from both high-level and disciplinary perspectives?
• RQ2 How has the field evolved from data analysis to data science, and what are its core components and outputs (data products)?
• RQ3 What are the major societal, governmental, and educational initiatives shaping the data science era?
• RQ4 What are the key challenges, directions, and opportunities in data-driven innovation and education?
• RQ5 How should data science be organized as a profession, including competencies and training?

Key findings

• Data science is characterized as the science of data and as an interdisciplinary field combining statistics, informatics, computing, communication, management, sociology, and domain context to transform data into insights and decisions.
• The data science era is driven by datafication, open data initiatives, and government and institutional programs that foster data science research, innovation, and education.
• Data products emerge from data science and represent knowledge, intelligence, wisdom, and decisions delivered through various forms such as predictions, services, and tools.
• There is a substantial shift from traditional data analysis to data-driven discovery and analytics, with an emphasis on deep analytics and data-driven decision making.
• The paper documents global and regional initiatives (e.g., US NSF, EU Horizon 2020, China NSF, UN Global Pulse, Australia’s Data61) that shape the scientific agenda and infrastructure for data science.
• The study argues that data science is not simply “big data” but a broader discipline integrating multiple fields and driving new data-centric economies and professions.