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M-SC in Operational Research at University of Delhi
Delhi, Delhi
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About the Specialization
What is Operational Research at University of Delhi Delhi?
This M.Sc. Operational Research program at the University of Delhi focuses on equipping students with advanced analytical and quantitative skills to solve complex decision-making problems across various sectors. With India''''s rapidly growing industries, there is a significant demand for professionals who can optimize processes, resources, and strategies, making this program highly relevant for the Indian market and its evolving needs in logistics, finance, and manufacturing.
Who Should Apply?
This program is ideal for mathematics, statistics, or operational research graduates seeking entry into the analytical domain. It also caters to working professionals from engineering, commerce, or science backgrounds looking to upskill in data-driven decision sciences. Individuals aiming for roles in business analytics, supply chain management, financial modeling, or industrial optimization will find this curriculum particularly beneficial, provided they possess a strong quantitative aptitude.
Why Choose This Course?
Graduates of this program can expect diverse career paths in India, including Data Scientist, Business Analyst, Management Consultant, Supply Chain Manager, and Quantitative Analyst. Entry-level salaries typically range from INR 6-10 LPA, with experienced professionals earning upwards of INR 15-25 LPA in top-tier companies. The program prepares students for roles in both Indian conglomerates and multinational corporations, aligning with certifications in areas like business analytics and project management.
Student Success Practices
Foundation Stage
Master Core Mathematical Foundations- (Semester 1-2)
Dedicate significant time to understanding Linear Algebra, Probability, Statistics, and Calculus. These subjects form the bedrock of Operational Research. Utilize resources like NPTEL courses, Khan Academy, and standard textbooks to solidify conceptual understanding before attempting problem-solving.
Tools & Resources
NPTEL, MIT OpenCourseWare, Schaum''''s Outlines
Career Connection
A strong foundation ensures easier grasp of advanced OR concepts, crucial for developing sophisticated models in future roles as an analyst or data scientist.
Develop Programming Proficiency in Python- (Semester 1-2)
Beyond classroom learning, actively practice Python programming for data manipulation, algorithm implementation, and statistical analysis. Participate in coding challenges and build small projects to apply theoretical concepts like linear programming or queuing theory in code.
Tools & Resources
HackerRank, LeetCode, Kaggle (beginner datasets), Anaconda Distribution
Career Connection
Proficient coding skills are non-negotiable for modern OR professionals, enabling efficient data processing, model building, and tool development, directly impacting placement opportunities in tech and analytics firms.
Engage in Peer Learning and Discussion Groups- (Semester 1-2)
Form study groups with peers to discuss complex topics, solve problems collaboratively, and share different perspectives on case studies. Teaching others reinforces your own understanding and exposes you to diverse problem-solving approaches.
Tools & Resources
Google Meet/Zoom for online collaboration, Whiteboards, University library study rooms
Career Connection
Enhances communication and teamwork skills, vital for collaborating in corporate environments and effectively presenting analytical findings to non-technical stakeholders.
Intermediate Stage
Seek Applied Projects and Internships- (Semester 3 (Summer after Semester 2))
Actively look for short-term projects or summer internships that allow you to apply OR techniques to real-world business problems. Prioritize opportunities that involve data collection, model building, and impact assessment in an organizational setting.
Tools & Resources
University career services, LinkedIn, Internshala, Company websites
Career Connection
Practical experience is highly valued by employers, providing tangible examples of problem-solving abilities and a deeper understanding of industry challenges, significantly boosting placement prospects.
Specialize through Electives and Certifications- (Semester 3-4)
Strategically choose elective courses that align with your career interests (e.g., Supply Chain, Finance, Data Mining). Complement this with relevant online certifications in tools like R, SQL, Tableau, or specialized OR software.
Tools & Resources
Coursera, edX, Udemy, Datacamp, IBM/Google professional certificates
Career Connection
Demonstrates focused expertise and a commitment to continuous learning, making you a more attractive candidate for specialized roles in analytics, consulting, or specific industry domains.
Participate in National-level Case Study Competitions- (Semester 3-4)
Engage in OR or analytics-focused case study competitions organized by institutions or industry bodies. This provides a platform to test your skills under pressure, work in teams, and gain exposure to industry-relevant scenarios.
Tools & Resources
Dare2Compete, Industry body websites (e.g., ORSI), Student clubs
Career Connection
Showcases problem-solving acumen, analytical thinking, and presentation skills to potential employers, often leading to pre-placement interview opportunities or direct hires.
Advanced Stage
Undertake a Comprehensive Dissertation/Project- (Semester 4)
Choose a challenging dissertation topic that integrates multiple OR techniques and has practical implications. Work closely with your supervisor, focusing on rigorous methodology, robust data analysis, and clear articulation of findings, treating it as a portfolio piece.
Tools & Resources
Academic journals (INFORMS, OR Forum), Research databases, Statistical software (R, Python, SAS)
Career Connection
A strong dissertation demonstrates independent research capabilities, analytical depth, and mastery of the subject, essential for advanced roles or higher studies.
Network Actively with Alumni and Industry Professionals- (Semester 3-4)
Attend webinars, seminars, and alumni events hosted by the university or department. Leverage LinkedIn to connect with alumni and professionals in your target industries for insights, mentorship, and potential job leads.
Tools & Resources
LinkedIn, University alumni portal, Industry conferences/meetups
Career Connection
Building a professional network is crucial for career opportunities, mentorship, and staying updated on industry trends, often leading to referrals and hidden job market access.
Prepare Rigorously for Placements and Interviews- (Semester 4 (pre-placement season))
Practice aptitude tests, quantitative puzzles, and technical interview questions regularly. Refine your resume and cover letter, highlighting OR projects and skills. Conduct mock interviews focusing on both technical depth and behavioral aspects.
Tools & Resources
Placement cell workshops, Glassdoor for company-specific interview questions, Aptitude books/online platforms
Career Connection
Systematic preparation directly increases the chances of securing desirable placements by ensuring you can articulate your knowledge and skills effectively under pressure.
Program Structure and Curriculum
Eligibility:
- B.A./B.Sc. (Hons.) Examination in Operational Research/Mathematics/Statistics with at least 50% marks in aggregate or Bachelor’s degree with at least 60% marks in aggregate with Mathematics/Statistics as one of the subjects.
Duration: 4 semesters / 2 years
Credits: 80 Credits
Assessment: Internal: 30%, External: 70%
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| OR-C101 | Mathematical Programming | Core | 4 | Linear Programming, Simplex Method, Duality Theory, Sensitivity Analysis, Transportation Problem, Assignment Problem |
| OR-C102 | Inventory Management | Core | 4 | Deterministic Inventory Models, Probabilistic Inventory Models, Inventory Control Systems, Lead Time Management, EOQ, EPQ models |
| OR-C103 | Probability and Statistics | Core | 4 | Random Variables, Probability Distributions, Sampling Distributions, Estimation Theory, Hypothesis Testing |
| OR-C104 | Fundamentals of Computer Science and Python Programming | Core | 4 | Programming Concepts, Python Syntax, Data Structures in Python, Control Flow, Functions, Object-Oriented Programming |
| OR-C105 | Practical-1 (Mathematical Programming & Inventory Management) | Practical/Lab | 2 | LP problem solving using software, Inventory model simulations, Sensitivity analysis applications |
| OR-C106 | Practical-2 (Probability and Statistics & Python Programming) | Practical/Lab | 2 | Statistical analysis using R/Python, Probability simulations, Data manipulation in Python |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| OR-C201 | Linear Algebra and Matrices | Core | 4 | Vector Spaces, Linear Transformations, Eigenvalues and Eigenvectors, Matrix Algebra, Quadratic Forms |
| OR-C202 | Queuing Theory | Core | 4 | Queuing Systems Basics, Markovian Queues, M/M/1, M/M/C models, Network of Queues, Applications of Queuing Theory |
| OR-C203 | Statistical Inference | Core | 4 | Point Estimation, Interval Estimation, Tests of Hypotheses, Non-parametric Tests, ANOVA |
| OR-C204 | Data Base Management Systems | Core | 4 | DBMS Architecture, Relational Model, SQL Queries, Data Normalization, Database Design |
| OR-C205 | Practical-3 (Linear Algebra and Matrices & Queuing Theory) | Practical/Lab | 2 | Matrix operations using software, Solving linear systems, Queuing model simulations |
| OR-C206 | Practical-4 (Statistical Inference & Data Base Management Systems) | Practical/Lab | 2 | Hypothesis testing with software, Regression analysis, SQL practice and database creation |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| OR-C301 | Non-Linear Programming | Core | 4 | Convex Sets and Functions, KKT Conditions, Quadratic Programming, Unconstrained Optimization, Numerical Optimization Methods |
| OR-C302 | Statistical Quality Control | Core | 4 | Control Charts (X-bar, R, P, C), Acceptance Sampling, Process Capability, Six Sigma Concepts, Quality Improvement Tools |
| OR-E30X | Discipline Specific Elective - 1 (Choose 1 from list) | Elective | 4 | Advanced Optimization Techniques, Stochastic Processes, Simulation and Modeling, Financial OR, Supply Chain Management |
| OR-E30X | Discipline Specific Elective - 2 (Choose 1 from list) | Elective | 4 | Multi-Criteria Decision Making, Game Theory, Data Mining for OR, Reliability and Maintenance, Big Data Analytics |
| OR-C303 | Practical-5 (Non-Linear Programming & Statistical Quality Control) | Practical/Lab | 2 | Non-linear optimization solvers, Control chart implementation, Acceptance sampling plans |
| OR-C304 | Practical-6 (DSE-1 & DSE-2 based practicals) | Practical/Lab | 2 | Elective specific software tools, Case studies based on DSE, Data analysis for chosen elective |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| OR-C401 | Heuristics and Meta-heuristics | Core | 4 | Local Search Algorithms, Simulated Annealing, Genetic Algorithms, Tabu Search, Ant Colony Optimization |
| OR-C402 | Integer Programming | Core | 4 | Branch and Bound, Cutting Plane Algorithms, Mixed Integer Programming, Formulations, Applications of Integer Programming |
| OR-E40X | Discipline Specific Elective - 3 (Choose 1 from list) | Elective | 4 | Decision Theory, Big Data Analytics for OR, Network Optimization, Project Management, Data Envelopment Analysis |
| OR-D401 | Dissertation | Project | 6 | Research Methodology, Problem Formulation, Data Collection & Analysis, Model Development, Report Writing |
| OR-C403 | Practical-7 (Heuristics and Meta-heuristics & Integer Programming) | Practical/Lab | 2 | Heuristic algorithm implementation, Integer programming solvers, Combinatorial optimization problems |
