This book has been developed strictly in accordance with the CST 322 – Data Analytics syllabus for the sixth semester B.Tech programme in Computer Science and Engineering. The primary objective of this text is to bridge the gap between theoretical foundations and practical implementation, enabling students to analyze data systematically and derive meaningful insights using appropriate analytical techniques and tools.

The content is organized into five well-defined modules. The book begins with Mathematics for Data Analytics, introducing descriptive statistics, probability distributions, and hypothesis testing, which form the analytical backbone for data-driven reasoning. It then progresses to the fundamentals of data analytics, covering the analytics process model, data life cycle, sampling, preprocessing, and dimensionality reduction techniques. The core analytical methods, including predictive and descriptive analytics, supervised and unsupervised learning algorithms, and association rule mining, are explained with clarity and relevance to real-world scenarios

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