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Data science is the study of the generalizable extraction of knowledge from data. The big data era is rapidly approaching. However, such massive amounts of data can be too much for standard data analytics to handle. The present study investigates how to create a high-performance platform for effective big data analysis and how to create a suitable mining algorithm to extract valuable information from large-scale data. This paper starts with a quick overview of data analytics before delving extensively into the topic of big data analytics. For the next phase of big data analytics, certain significant unresolved problems and future research avenues will also be discussed. Big data enables prediction models that can be used by both computers and humans, as well as automated, actionable knowledge generation. The terms “big data” and “data science” are being used more frequently. What does that mean, though? Is it special in any way? What abilities are necessary for “data scientists” to be productive in a data-rich world? What does this mean for scientific research? In this article, I tackle these issues from a predictive modeling standpoint. This review additionally proposes a unified infrastructure-to-deployment taxonomy and practical design playbook that bridges modern distributed systems, machine learning operations, responsible AI, and scalable deployment architectures for next-generation data science ecosystems.
Ambreen Ilyas (Thu,) studied this question.