Computational Statistics and Data Science in the Twenty-first Century

TL;DR

This paper discusses the evolving role of computational statistics in data science, highlighting core challenges, recent advances, and the impact of emerging computational paradigms like multi-core and quantum computing.

Contribution

It provides a high-level overview of key challenges and recent developments in computational statistics within the context of modern data science.

Findings

Identification of five core challenges in computational statistics

Recent advances in model-specific computational methods

Emphasis on the importance of non-sequential computing paradigms

Abstract

Data science has arrived, and computational statistics is its engine. As the scale and complexity of scientific and industrial data grow, the discipline of computational statistics assumes an increasingly central role among the statistical sciences. An explosion in the range of real-world applications means the development of more and more specialized computational methods, but five Core Challenges remain. We provide a high-level introduction to computational statistics by focusing on its central challenges, present recent model-specific advances and preach the ever-increasing role of non-sequential computational paradigms such as multi-core, many-core and quantum computing. Data science is bringing major changes to computational statistics, and these changes will shape the trajectory of the discipline in the 21st century.