AMPBA Applied Learning

Capstone Project

The Capstone Project is a student-led team project that offers a significant value proposition to organisations and the students. It provides a platform for students to apply their classroom learning to real business problems and leverage the power of data to provide valuable insights.

Salient features of the Capstone:

• Student teams address a business issue currently being faced or considered by your organization and come out with actionable solutions/recommendations
• The organization will provide the problem definition and a sample data set corresponding to the problem
• Teams of students will bid for projects stating their proposed approach and methodology
• The team is guided by our distinguished faculty, and monitored by the program team
• The commitment of time by student team (of 3 to 4 members) is about 450-500 man-hours across 3 months
• Student teams maintain regular client interaction/ reporting
• Two formal reviews are held by the client, faculty and the program management team all of which contribute to the grades allotted to the team
• Final deliverables from the students will be a report based on the data analysis of a suitably developed model
• There can be an intermediate deliverable where the initially developed model is taken and run by the company on the live data and feedback provided to student teams to enhance the model and write their final reports

Recent Projects

Banking

• Econometric data analysis for identifying locations to open new branches in India

Computer Science

• Facial Key Point Detection

Finance

• Prediction of Future Security Prices

Healthcare

• Identifying Hidden Drug-Drug Interactions in FDA AERS
• Anomaly Detection Engine for Medical Technology Equipment
• Healthcare Bundled Payments

Insurance

• A study of Insurance Fraud using Advanced Analytics

Marketing

• Customer Profiling & Market Basket Analysis

Retail/Social Media

• Auto-classification of Products into Categories

 

Social Sciences

• A View of India Elections Past and Present Social and Historical - 2004 - 2014

Supply Chain and Operations

• Freight Optimization through Analytical Model

Telecom

• Fraud Analytics using machine - learning and Engineering on " Big Data " (FAME) for Telecom