Data Mining & Analytics

Introduction to the process of knowledge discovery in databases, Basic concepts of data warehousing and data mining, Data preprocessing techniques: selection, extraction, transformation, loading, Data warehouse design and implementation: multidimensional data model, case study using Oracle technology, Overview of data mining process and knowledge discovery, Database Support to Data Mining , Data Mining Techniques and Functions, Cluster Analysis , Regression Algorithms in Data Mining, Neural Networks in Data Mining ,Decision Tree Algorithms.

Machine learning schemes in data mining: finding and describing structure patterns (models) in data, informing future decisions, Information theory and statistics in data mining: from entropy to regression, Data mining core algorithms: statistical modeling, , clustering, association analysis , classification and prediction; Credibility: evaluating what has been leaned from training data and predicting model performance on new data, evaluation methods, and evaluation metrics; Weka: a set of commonly used machine learning algorithms implemented in Java for data

Mining; C5 and Cubist: Decision tree and model tree based data mining tools; Link Analysis in Text Mining, Web Mining Taxonomy, Mining the Web User Behavior, Web Analytics and case studies of real data mining applications.