This course aims to enable students to Gain knowledge and perspective of underlying IT Infrastructure in Business Analytics industries and to equip students with open standards for enterprise applications in various industrial verticals like healthcare vertical open standard, retail vertical open standard and insurance vertical open standard.
The course aims to develop an understanding ofhow to design & develop reports and dashboards utilizing key performance indicators to improve day-to-day business decision making. It provides learning on how to plan and implement BI development projects and know the administrative and deployment scenarios & issues in BI space.
This course aims to develop an understanding on the use of business analytics in social, web, media & communication space. It explores the ways of using statistical data in developing metrics that can help track visitor activity that can guide marketing strategies. You will learn how to optimize different aspects of a site to increase visitors, maximize conversion rates, and reduce the costs of acquisition. The course also teaches how to synchronize a brand across all Social Media outlets.
This course aims to develop an understanding of how analytical models are used for Customer Value Management in various phases of Customer Life Cycle and helps to learn and understand Dimension Reduction techniques and build predictive models using advanced Machine Learning Techniques like Neural Networks, Random Forest, Support Vector Machine, Survival Analysis (Cox Regression).
This course aims to develop an understanding of the use of business analytics in industries like banking, insurance, retail& consumer products, media & communication. Students get in depth knowledge with case studies on the impact of Business analytics and intricate details about its use in the Major industrial domains.
The course imparts knowledge on how to develop models to predict categorical and continuous outcomes, using such techniques as neural networks, decision trees, logistic regression, support vector machines, and Bayesian network models. It describes the use of the binary classifier and numeric predictor nodes to automate model selection and advices on when and how to use each model or apply combination of two or more models to improve prediction.