Data Mining & Predictive Analytics

Data Mining & Predictive Analytics is the name given to a group of disciplines, technologies, applications and practices for analyzing data (usually based on past business performance) and building models to help enterprise users make better, faster business decisions. This course introduces basic concepts, tasks, methods, and techniques in data mining. On completion of this course students should recognize the need for data mining applications, identify appropriate tools and techniques to solve data mining tasks and learn how to use data mining software in a number of business related scenarios. The emphasis is on various data mining problems and their solutions.

Course Content:

• Overview of Business Analytics: Introduction & Methodology.

• Data Preprocessing: extraction, transformation (e.g. discretization) and cleaning;

• Data Exploration (descriptive statistics, summarization) and dimensionality reduction

• Assessing classification and predictive performance

• Multivariate Regression and Numerical Prediction

• Decision Trees and Rules based Classification

• Statistical Modeling and Bayesian Learning

• Backpropagation Neural Networks

• Discriminative Models and Support Vector Machines

• Association and Market Basket Analysis

• Cluster Analysis (EM, hierarchical methods, partitioning methods)

• Mining Data Streams and times-series data

• Applications in Marketing, finance, health care and (e.g. consumer behavior, fraud detection, risk analysis, market segmentation)

Prerequisites:

• Business Intelligence Course