Data mining and large-scale data analytics have become central to modern computer science and related disciplines, driven by the rapid growth of massive datasets generated from social media, business transactions, scientific experiments, healthcare systems, and distributed platforms. These datasets often contain valuable hidden patterns and knowledge that can support decision-making and predictive analysis. This course provides students with a comprehensive foundation in data mining concepts and scalable data processing techniques. It covers data understanding and preprocessing, association rule mining, classification and prediction methods, and the fundamentals of distributed data management and processing using Hadoop, HDFS, MapReduce, and Apache Spark. Through theoretical discussions, practical assignments, and oral presentations, students gain hands-on experience in applying data mining algorithms and big data frameworks to real-world problems, while developing the ability to analyze large datasets and communicate analytical results effectively.