This document provides an overview of data science tools, techniques, and applications. It begins by defining data science and explaining why it is an important and in-demand field. Examples of applications in healthcare, marketing, and logistics are given. Common computational tools for data science like RapidMiner, WEKA, R, Python, and Rattle are described. Techniques like regression, classification, clustering, recommendation, association rules, outlier detection, and prediction are explained along with examples of how they are used. The advantages of using computational tools to analyze data are highlighted.