This chapter presents a comprehensive, hands-on exploration of clustering techniques using Python, R, and MATLAB, with a focus on applications in environmental and engineering data analysis. The discussion begins with an overview of the Anaconda distribution as a standardized ecosystem for managing dependencies and executing code across platforms. The strengths and weaknesses of each programming language are assessed, highlighting Python’s versatility, R’s statistical depth, and MATLAB’s numerical robustness. A real-world dataset from a municipal water treatment plant is employed to demonstrate the clustering process from data preprocessing to algorithm application and evaluation. Four algorithms, K-means, DBSCAN, Agglomerative Clustering, and Spectral Clustering, are applied, with their performance assessed through internal validation metrics (Silhouette Score and Davies–Bouldin Index) and PCA-based visualizations. The chapter provides step-by-step code, methodological explanations, and comparative insights, enabling readers to reproduce the analyses and make informed decisions about clustering tools in their own research.