A Study on the Data Science Life Cycle and Its Applications in Modern Intelligent Systems

Abstract: Data Science has become one of the most important technologies in modern computing and intelligent decision-making systems. Organizations generate massive amounts of structured and unstructured data every day, creating the need for efficient techniques to collect, process, analyze, and extract meaningful insights from data. The Data Science Life Cycle provides a systematic framework for solving real-world problems using data-driven approaches. This paper presents a study on the phases of the Data Science Life Cycle, including data collection, data preprocessing, exploratory data analysis, feature engineering, model building, evaluation, deployment, and monitoring. The study explains how these stages work together to transform raw data into actionable insights and intelligent predictions. The paper also discusses applications, advantages, challenges, and future trends in Data Science.

Keywords: Data Science, Machine Learning, Data Analysis, Data Preprocessing, Predictive Modeling, Big Data, Artificial Intelligence, Data Visualization