The Nexus of Data Science and Big Data: Accelerating Data-Driven Decision Making in the Digital Age

Abstract

In the contemporary digital landscape, integrating data science techniques with big data is essential for organizations to leverage their data effectively. This research paper examines core data science methodologies, including data pre-processing, exploratory data analysis, predictive modeling, and optimization. Through an in-depth exploration of these methods, this article illustrates how they collectively enable tasks such as data cleaning, integration, insightful exploration, accurate predictions through machine learning, and streamlined analysis processes. Emphasizing the complementary relationship between data science and big data, the study underscores how this integration empowers organizations to make informed decisions, enhance operational efficiency, improve customer experiences, and foster innovation across diverse industries. These findings highlight the critical role of data-driven approaches in navigating the challenges of the digital age and shaping the future of global industries.