Data Science and Big Data Analytics: Transforming Data into Meaningful Insights A Review Paper

Abstract In today's digital world, data is being generated at an extremely high rate through smartphones, social media, websites, sensors, online transactions, educational platforms, healthcare systems, and many other sources. Data by itself does not provide much value unless it is properly collected, processed, analysed, and converted into useful information. Data Science and Big Data Analytics have therefore become important areas of computer science and information technology. Data Science combines statistics, mathematics, programming, machine learning, and domain knowledge to discover useful patterns from data. Big Data Analytics focuses on analysing very large, complex, and rapidly generated datasets that traditional data-processing systems may not handle efficiently. NIST describes Big Data in terms such as volume, variety, velocity, and variability, and identifies data science as a process for obtaining useful knowledge directly from data. This paper explains the basic concepts of Data Science and Big Data Analytics, their relationship, major technologies, data-processing lifecycle, applications, advantages, challenges, and future scope. It also discusses technologies such as Hadoop, Apache Spark, cloud computing, databases, machine learning, and data visualization. The paper highlights how these technologies are helping organisations make better decisions in healthcare, education, banking, agriculture, transportation, business, and other fields. The objective is to provide B.Tech students with a simple understanding of how large amounts of data can be transformed into meaningful knowledge and practical solutions.