EVOLUTION OF DATA SCIENCE METHODOLOGIES FROM STATISTICAL APPROACHES TO MACHINE LEARNING PARADIGMS

Abstract

This paper explores the evolution of data science methodologies, from traditional statistical approaches to modern machine learning paradigms. It begins by defining data science and outlining its scope, highlighting the increasing importance of data-driven decision-making in various sectors. The historical context of data science is reviewed, with a focus on the early use of statistical methods for data analysis. Key limitations of these traditional approaches, including their reliance on linear models and interpretability issues, are discussed. The paper then examines the transition to machine learning, emphasizing the impact of computational advancements and the rise of big data on the development of more sophisticated algorithms. Key differences between traditional statistics and machine learning models are outlined, with particular attention given to the strengths and weaknesses of each methodology. Finally, the paper presents the drivers of this methodological shift and considers applications in diverse domains, such as healthcare and finance. Challenges, including the interpretability of machine learning models and ethical concerns in data science, are also addressed. The paper concludes with a discussion of future directions for data science methodologies.