Data Science: Challenges and Directions

TL;DR

This paper reviews the current state of data science, highlighting its challenges and future directions by analyzing its core complexities and the methodologies needed to address them.

Contribution

It offers a comprehensive exploration of the intrinsic challenges and research directions in data science, emphasizing its nature as a complex systems science.

Findings

Limited exploration of new data-driven challenges

Data science problems are complex systems requiring specialized methodologies

Highlights the need for innovative approaches in data science research

Abstract

While data science has emerged as a contentious new scientific field, enormous debates and discussions have been made on it why we need data science and what makes it as a science. In reviewing 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, and only a limited number of new data-driven challenges and directions have been explored. In this paper, we explore the intrinsic challenges and directions inspired by comprehensively exploring the complexities and intelligence embedded in data science problems. We focus on the research and innovation challenges inspired by the nature of data science problems as complex systems, and the methodologies for handling such systems.