Data Analytics aims to reveal the hidden information within the data by means of various methods, which would improve the decisions and the subsequent actions in order to create value from the data. In this process, there are various sub-processes such as business understanding, data understanding, data preparation, modeling, evaluation and deployment of the model. Within the scope of this course, metrics and methods that would be used to validate the models, supervised learning techniques (i.e., regression and classification), unsupervised learning techniques (e.g. clustering, association rule mining, principal component analysis) and feature engineering and feature subset selection methods will be discussed and various use cases in real life applications will be presented.