Computational Methods for Data Analysis provides a systematic introduction to the computational, statistical and machine-learning techniques used for analysing complex datasets. The book begins with UNIX and Linux fundamentals before introducing knowledge discovery, data preprocessing, data mining and pattern recognition. It explains important computational approaches such as Hidden Markov Models, artificial neural networks, support vector machines and clustering methods. Statistical techniques, including principal component analysis, ANOVA and AMOVA, are presented alongside their practical applications. Dedicated coverage of gene prediction and phylogenetic algorithms connects computational methods with biological and genetic research. The concluding chapter introduces R programming for data manipulation, visualization and statistical analysis. Designed for students, teachers and researchers, the book develops the interdisciplinary skills required for computational biology, bioinformatics, data science and research-oriented data analysis.

Chapter 1: Introduction to Computational Data Analysis

Chapter 2: Basics of UNIX and Linux

Chapter 3: Working in UNIX/Linux Environment

Chapter 4: Knowledge Discovery in Databases (KDD)

Chapter 5: Fundamentals of Data Mining

Chapter 6: Introduction to Machine Learning

Chapter 7: Pattern Recognition Techniques

Chapter 8: Hidden Markov Models (HMM)

Chapter 9: Artificial Neural Networks (ANN)

Chapter 10: Support Vector Machines (SVM)

Chapter 11: Principal Component Analysis (PCA)

Chapter 12: Analysis of Variance (ANOVA)

Chapter 13: Analysis of Molecular Variance (AMOVA)

Chapter 14: Clustering Methods

Chapter 15: Gene Prediction and Phylogeny Algorithms

Chapter 16: Introduction to R