Data Science: Big Data Analytics – Methods, Applications, and Future Trends

Big data analytics has become a cornerstone of modern data science practice, enabling the extraction of actionable knowledge from massive, heterogeneous data sets. This paper reviews the principal analytics frameworks—Hadoop, Spark, and NoSQL databases—focusing on their architectural principles, scalability characteristics, and suitability for diverse analytical workloads. We discuss major application domains (healthcare, finance, smart cities, and industrial IoT) and examine persistent challenges related to scalability, privacy, and algorithmic complexity. A systematic literature review methodology is described, highlighting selection criteria and analysis procedures. Finally, emerging trends such as the integration of advanced AI/ML models, edge computing paradigms, and federated analytics are explored, outlining a roadmap for future research. The findings provide students and early career researchers with a consolidated view of the current state of the art and a basis for investigating next generation big data solutions.