Authors: Chong Ho Yu (Hawaii Pacific University, USA) and Miodrag Lovric (Radford University, USA)
This article presents a unified perspective on the rapidly evolving and highly interconnected fields of data science, machine learning, data mining, big data, and statistics. It begins by outlining the scope and synergy among these domains, explaining how each contributes to the broader goal of transforming raw data into actionable insight. Data science is positioned as the umbrella discipline, drawing on statistical theory, machine learning algorithms, and domain knowledge to drive decision-making in a data-rich world.
Machine learning, a subfield of artificial intelligence, is described as a key mechanism for automating pattern recognition and prediction through experience and data exposure. The article explains common ML paradigms—supervised, unsupervised, and reinforcement learning—and emphasizes the increasing role of deep learning and neural networks in processing complex data like images, speech, and text.
Data mining is treated as a distinct but related concept, referring to the extraction of non-obvious patterns and associations from large datasets, often using classification, clustering, and association rules. It is differentiated from machine learning in terms of goals and methodology, but closely linked in practical application.
The article further explores distinctions between data science and big data. Data science focuses on extracting meaning and solving problems with data, while big data emphasizes the infrastructure and technologies required to store and manage massive volumes of information. Nevertheless, the two are intertwined, with big data tools enabling data science work at scale.
Finally, the article contrasts statistics and data science. While statistics is foundational to data science, the latter incorporates programming, machine learning, and domain integration to tackle modern challenges. Statistics focuses on inference and population-level conclusions, while data science emphasizes pattern discovery, adaptability, and predictive modeling.
The article includes historical context, conceptual diagrams, and a thorough review of recent literature. It advocates for an integrative view of these disciplines to foster innovation, collaboration, and informed application in fields ranging from healthcare to finance and AI.
For a full theoretical exploration, technical taxonomy, and references, see the complete article in the International Encyclopedia of Statistical Science.