Master of Social Work (MSW)
A professional degree program preparing students for a career in social work with hands-on, work-linked experience.
About the Program
The Master of Social Work (MSW) Work-Linked Program (WLP) offers a unique blend of academic learning and practical fieldwork. It is designed for compassionate individuals dedicated to making a difference in society. This program equips students with the knowledge and skills to address social problems, advocate for vulnerable populations, and implement community development projects.
Key Information
Program Objectives
To develop a deep understanding of social work principles, ethics, and practices.
To train students in working with individuals, groups, and communities to promote social well-being.
To equip students with research skills to analyze social issues and evaluate intervention effectiveness.
To prepare graduates for leadership roles in NGOs, government agencies, and corporate social responsibility (CSR) departments.
Career Prospects
Social Worker
Community Development Officer
Counselor
NGO Manager
CSR Manager
Policy Analyst
Project Coordinator
Program Structure
Semester I
Philosophy of Social Work
Social Work with Individuals and Groups-I
Dynamics of Human Behaviour
Abnormal Psychology
Field Work-I
Semester II
Social Work Research and Statistics-I
Social Work with Individuals and Groups-II
Community Work and Social Action
Social and Organizational Psychology
Field Work-II / Project Centric Learning I
Semester III
Social Work Research and Statistics-II
Social Welfare Administration
Criminology
Communication in Social Work / Field Instruction in Social Work
Rural and Tribal Communities in India / Family and Child Welfare
Open Elective
Dissertation Synopsis Orientation
Semester IV
Preventive and Correctional Law
Social Infrastructure Development in India
Labour Legislation and Labour Welfare
Psychiatric Social Work / Industrial Relations
Urban Community Development / Welfare and Development of Women
Dissertation and Viva- Voce Examination
Project Centric Learning II