Curriculum

The MBA in Business Analytics program at Loyola Institute of Business Administration Chennai is meticulously structured to provide students with a comprehensive understanding of analytics, business strategy, and data science. The curriculum spans two years, divided into four semesters, with each semester designed to build upon the previous one.

Course Structure Overview

The program consists of core courses, departmental electives, science electives, and laboratory sessions. Students are required to complete a minimum of 100 credits over the duration of the program, with specific credit allocations for each category.

Semester I

Semester II

Semester III

Semester IV

Advanced Departmental Electives

The department offers several advanced electives that allow students to specialize in specific areas of interest. These courses are taught by faculty members who are experts in their respective fields and often involve collaboration with industry partners.

Deep Learning and Neural Networks

This course delves into the mathematical foundations of neural networks, including backpropagation, convolutional neural networks (CNNs), and recurrent neural networks (RNNs). Students learn to implement these models using frameworks like TensorFlow and PyTorch. The course also covers recent advancements in deep learning, such as transformers and attention mechanisms.

Natural Language Processing for Business

This elective explores how NLP techniques can be applied to extract insights from textual data. Topics include sentiment analysis, named entity recognition, text classification, and machine translation. Students work on projects involving social media monitoring, customer feedback analysis, and document summarization.

Financial Analytics and Risk Modeling

This course focuses on applying quantitative methods to financial markets and risk management. Students learn about portfolio optimization, value-at-risk (VaR) models, credit risk assessment, and derivatives pricing. The course includes hands-on sessions with real financial datasets and industry-standard software like MATLAB and R.

Marketing Analytics and Customer Segmentation

This elective teaches students how to use data analytics to understand customer behavior and optimize marketing strategies. Topics include customer lifetime value (CLV), A/B testing, cohort analysis, and behavioral segmentation. Students engage in projects with actual marketing datasets provided by industry partners.

Supply Chain Optimization and Analytics

This course addresses the challenges of optimizing supply chains using analytical tools and techniques. Students learn about demand forecasting, inventory optimization, transportation planning, and logistics coordination. The course includes case studies from global supply chain leaders and practical applications using simulation software.

Cybersecurity for Data Analytics

With increasing threats in the digital landscape, this course focuses on securing data analytics environments. Topics include encryption, access control, threat detection, and incident response. Students explore how to protect sensitive data while enabling effective analytics, with a focus on compliance with regulations like GDPR and HIPAA.

Healthcare Analytics

This elective introduces students to the application of analytics in healthcare settings. Topics include patient outcome prediction, medical imaging analysis, drug discovery, and public health surveillance. Students work on projects involving real medical datasets and collaborate with healthcare institutions.

Public Sector Analytics

This course prepares students to apply analytics in government and non-profit organizations. Students learn about policy evaluation, resource allocation, urban planning, and social impact measurement. The course includes guest lectures from public sector officials and case studies from successful initiatives.

Project-Based Learning Philosophy

The department's philosophy on project-based learning is rooted in the belief that students learn best when they engage with real-world problems. Projects are designed to reflect industry challenges and require students to integrate knowledge from multiple disciplines.

Mini-Projects

Mini-projects are undertaken during the first two semesters. These projects are typically small-scale, lasting about 4–6 weeks, and focus on developing specific analytical skills or tools. Students work in teams and receive guidance from faculty mentors. The evaluation criteria include project documentation, presentation quality, and peer feedback.

Capstone Projects

The capstone project is the culmination of the program, undertaken during the final semester. Students select a topic relevant to their specialization or industry interest and work closely with a faculty mentor and an industry partner. The project involves extensive research, analysis, and presentation of findings to stakeholders.

Evaluation Criteria

Projects are evaluated based on multiple factors including technical competency, business relevance, innovation, and impact. Students must demonstrate their ability to communicate complex analytical concepts clearly and effectively. The final deliverables include a comprehensive report, a presentation, and a working prototype or model.