Advanced Analytics for Management

Course details

The programme is intended for enabling practitioners, managers and decision makers to use advanced analytics for better decision making and in-depth understanding of these concepts using hands-on technique(s) and relating to business cases. The programme may also be of interest to participants from various analytics organizations to better understand the underlying concepts of these advanced techniques.

Objectives

• Analytics means extensive use of data, statistical analysis, explanatory and predictive modeling, and fact-based management to derive decisions and actions.
• Why analytics? Currently, companies competing in the same industry offer similar kind of products and use comparable technology. High performance business processes are thus, the only places where companies can differentiate. Many of the previous bases of differentiations are no longer available. Advantage of unique geographical location no longer matters greatly in global competition, and protective regulations are no longer that strong a deterrent. Propriety technologies can be copied in no time, and break-through innovation in products and services are becoming more and more difficult with the passage of time. What’s left as the only basis of competition is to improve your business processes that work with maximum efficiency and effectiveness, and to make right business decisions in quickest time possible.
• What are the business processes where analytics can help? Analytics can support almost any business process. To name a few, customer-based processes like customer segmentation, customer acquisition, customer retention, dynamic pricing; supplier-facing processes like capacity planning, demand-supply matching; financial processes like selecting portfolio of products, credit card scoring, future value analysis; and finally, human resource processes like recruiting and nurturing talents, and selecting and managing vendors.
• The programme will provide participants with an overview of the concepts and advanced techniques in statistics and data mining that are in use in the above domains of business. It will focus more on the application of the techniques. Also, there will be enough hands-on sessions to make the participants get a feel of it.

Content

• Statistical and Data Mining Tools for Discovering Patterns in the Data
• Business Forecasting
• Regression Analysis with applications to credit scoring, database marketing, fraud detection.
• Modeling Financial Data with applications to financial risk assessment
• Modern Clustering and Classification Techniques with applications to market segmentation, responder selection.
• Conjoint Analysis with applications to new product design
• Techniques for handling missing data.