Data Science: A Comprehensive Overview
- URL: http://arxiv.org/abs/2007.03606v1
- Date: Wed, 1 Jul 2020 02:33:58 GMT
- Title: Data Science: A Comprehensive Overview
- Authors: Longbing Cao
- Abstract summary: The twenty-first century has ushered in the age of big data and data economy, in which data DNA 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.
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.
- Score: 42.98602883069444
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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.
Related papers
- The Future of Data Science Education [0.11566458078238004]
The School of Data Science at the University of Virginia has developed a novel model for the definition of Data Science.
This paper will present the core features of the model and explain how it unifies various concepts going far beyond the analytics component of AI.
arXiv Detail & Related papers (2024-07-16T15:11:54Z) - Data Augmentation in Human-Centric Vision [54.97327269866757]
This survey presents a comprehensive analysis of data augmentation techniques in human-centric vision tasks.
It delves into a wide range of research areas including person ReID, human parsing, human pose estimation, and pedestrian detection.
Our work categorizes data augmentation methods into two main types: data generation and data perturbation.
arXiv Detail & Related papers (2024-03-13T16:05:18Z) - Beyond Privacy: Navigating the Opportunities and Challenges of Synthetic
Data [91.52783572568214]
Synthetic data may become a dominant force in the machine learning world, promising a future where datasets can be tailored to individual needs.
We discuss which fundamental challenges the community needs to overcome for wider relevance and application of synthetic data.
arXiv Detail & Related papers (2023-04-07T16:38:40Z) - A Survey on Data Pricing: from Economics to Data Science [61.72030615854597]
We examine various motivations behind data pricing and understand the economics of data pricing.
We discuss both digital products and data products.
We consider a series of challenges and directions for future work.
arXiv Detail & Related papers (2020-09-09T19:31:38Z) - From Data to Knowledge to Action: A Global Enabler for the 21st Century [26.32590947516587]
A confluence of advances in the computer and mathematical sciences has unleashed unprecedented capabilities for enabling true evidence-based decision making.
These capabilities are making possible the large-scale capture of data and the transformation of that data into insights and recommendations.
The shift of commerce, science, education, art, and entertainment to the web makes available unprecedented quantities of structured and unstructured databases about human activities.
arXiv Detail & Related papers (2020-07-31T19:19:42Z) - Data Science: Nature and Pitfalls [42.98602883069444]
A critical matter for the healthy development of data science in its early stages is to deeply understand the nature of data and data science.
These important issues motivate the discussions in this article.
arXiv Detail & Related papers (2020-06-28T02:06:54Z) - Data Science: Challenges and Directions [42.98602883069444]
We review hundreds of pieces of literature which include data science in their titles.
We find that the majority of the discussions essentially concern statistics, data mining, machine learning, big data, or broadly data analytics.
We focus on the research and innovation challenges inspired by the nature of data science problems as complex systems.
arXiv Detail & Related papers (2020-06-28T01:49:00Z) - Ten Research Challenge Areas in Data Science [4.670305538969914]
Data science builds on knowledge from computer science, mathematics, statistics, and other disciplines.
This article starts with meta-questions about data science as a discipline and then elaborates on ten ideas for the basis of a research agenda for data science.
arXiv Detail & Related papers (2020-01-27T21:39:57Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.