# Data Science: A Comprehensive Overview

**Authors:** Longbing Cao  
**arXiv:** [2007.03606](https://arxiv.org/abs/2007.03606)  
**Date:** 2020-07-01  
**Summary page:** https://www.summarizepaper.com/en/arxiv-id/2007.03606v1/

## Key points

- The era of big data and data economy has emerged in the twenty-first century
- Data DNA is crucial for all data-based organisms, carrying essential knowledge and insights
- Data science and analytics are essential for understanding and harnessing the power of data
- Ongoing debate about whether big data is hype and if data science is still in early stages
- Challenges and opportunities arising from advancements in research, innovation, business practices, professional development, and educational initiatives within data science realm
- Evolution from traditional data analysis to advanced field of data science
- Major challenges faced in driving innovation through data utilization
- Nature of data analytics as a critical component of ecosystem within the dynamic landscape of the data economy
- Evolving profession and competency requirements in field of data education to meet growing demands for skilled professionals adept at navigating complex datasets
- Potential future trajectories for data science as it continues to evolve and shape various industries

## Summary

In the twenty-first century, the era of big data and data economy has emerged. Data DNA plays a crucial role in all data-based organisms by carrying essential knowledge, insights, and potential. This data DNA is at the core of the field of data science along with analytics. These are essential for understanding and harnessing its power. There is ongoing debate about whether big data is merely hype and if data science is still in its early stages. However, there are undeniable challenges and opportunities arising from advancements in research, innovation, business practices, professional development, and educational initiatives within the realm of data science. This comprehensive overview delves into various fundamental aspects of data science. It explores the evolution from traditional data analysis to the more advanced field of data science. It introduces key concepts within this domain and paints a broad picture of the current era dominated by data science. The article also discusses major challenges faced in driving innovation through data utilization. It examines the nature of data analytics as a critical component of this ecosystem and highlights new industrialization trends and service opportunities emerging within the dynamic landscape of the data economy. Furthermore, this overview sheds light on the evolving profession and competency requirements in the field of data education to meet growing demands for skilled professionals adept at navigating complex datasets. It also offers insights into potential future trajectories for data science as it continues to evolve and shape various industries. Authored by Longbing Cao and published in ACM Computing Surveys (2017), this article stands out as one of the pioneering works that not only presents a comprehensive big picture view but also provides valuable observations, lessons learned, and forward-thinking perspectives on the ever-evolving field of and .