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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 Operational Research program at the University of Delhi focuses on developing quantitative skills to solve complex decision-making problems in business, industry, and government. It integrates advanced mathematical, statistical, and computational techniques to optimize processes and resource allocation. The curriculum is designed to meet the growing demand for analytics professionals in the Indian market, addressing real-world challenges through a blend of theory and practical applications.
Who Should Apply?
This program is ideal for fresh graduates with a strong foundation in Mathematics, Statistics, or Engineering who are seeking entry into analytical roles. It also caters to working professionals aiming to upskill in data-driven decision-making or career changers transitioning into the rapidly expanding fields of analytics, supply chain, and financial modeling in India. A quantitative aptitude and problem-solving mindset are key prerequisites for success.
Why Choose This Course?
Graduates of this program can expect diverse career paths in India, including roles as Business Analysts, Data Scientists, Operations Consultants, and Quantitative Analysts. Entry-level salaries typically range from INR 6-10 LPA, with experienced professionals earning significantly more. The program prepares students for growth trajectories in major Indian and multinational companies by equipping them with skills aligned with certifications in areas like business analytics, supply chain optimization, and financial modeling.
Student Success Practices
Foundation Stage
Master Core Mathematical Foundations- (Semester 1-2)
Dedicate significant time to understanding the mathematical and statistical underpinnings of OR, such as Linear Algebra, Calculus, Probability, and Statistical Inference. Utilize online platforms like NPTEL and Khan Academy for supplementary learning and practice problems from standard textbooks.
Tools & Resources
NPTEL courses, Khan Academy, Standard textbooks (e.g., Hadley K. Linear Programming)
Career Connection
A strong foundation in these areas is crucial for tackling advanced OR concepts and excelling in quantitative roles, making you a strong candidate for analytical positions.
Develop Programming Proficiency- (Semester 1-2)
Beyond classroom learning, practice coding regularly in C and Python. Focus on implementing OR algorithms and solving competitive programming problems on platforms like HackerRank or LeetCode. Engage in small personal projects to apply concepts.
Tools & Resources
HackerRank, LeetCode, Jupyter Notebook, VS Code
Career Connection
Proficiency in programming languages like Python is non-negotiable for data science, analytics, and optimization roles, directly impacting your employability and project readiness.
Engage in Peer Learning & Discussion- (Semester 1-2)
Form study groups with classmates to discuss complex topics, solve problems collaboratively, and clarify doubts. Explaining concepts to others solidifies your understanding and builds teamwork skills.
Tools & Resources
Departmental common rooms, Online collaboration tools
Career Connection
Enhances problem-solving skills, fosters a deeper understanding of concepts, and develops communication abilities vital for team-based projects in industry.
Intermediate Stage
Undertake Practical Projects and Case Studies- (Semester 3)
Actively seek out opportunities for mini-projects or case study analyses, either as part of coursework or independently. Apply learned OR techniques to real-world scenarios, using tools like R, Python libraries (SciPy, PuLP), or specialized OR software.
Tools & Resources
Kaggle datasets, R Studio, Python (SciPy, NumPy, Pandas, Gurobi/OR-Tools)
Career Connection
Demonstrates practical application of OR skills, crucial for building a portfolio that attracts internships and job offers in consulting, logistics, or analytics.
Network with Industry Professionals- (Semester 3)
Attend departmental seminars, workshops, and guest lectures by industry experts. Leverage platforms like LinkedIn to connect with alumni and professionals in OR-related fields. Seek informational interviews to understand industry trends and career paths.
Tools & Resources
LinkedIn, Industry conferences (e.g., ORSI conferences), University career fairs
Career Connection
Builds valuable professional connections, opens doors to internship opportunities, and provides insights into industry demands and potential career trajectories.
Participate in Competitions and Hackathons- (Semester 3)
Engage in analytics, data science, or operational research-focused competitions and hackathons. These provide exposure to diverse problems, develop quick problem-solving abilities, and offer a platform to showcase skills.
Tools & Resources
Analytics Vidhya, Kaggle Competitions, University-organized hackathons
Career Connection
Adds valuable experience to your resume, sharpens critical thinking, and provides a competitive edge during placement drives, particularly for roles in top-tier analytics firms.
Advanced Stage
Focus on Dissertation/Project Excellence- (Semester 4)
Choose a dissertation topic that aligns with your career interests and offers scope for in-depth OR application. Work closely with your supervisor, aiming for novel solutions or significant practical impact. Present your work confidently.
Tools & Resources
Research papers (Google Scholar, Scopus), Advanced OR software (CPLEX, Gurobi)
Career Connection
A strong dissertation demonstrates research capabilities and specialized skills, making you highly valuable for R&D roles, advanced analytics positions, and further academic pursuits.
Intensive Placement Preparation- (Semester 4)
Begin placement preparation early by refining your resume, practicing aptitude tests, and mock interviews. Focus on case study discussions and technical interview questions related to OR, statistics, and machine learning. Utilize university placement cell resources.
Tools & Resources
Placement Cell workshops, Online aptitude tests, Glassdoor for interview experiences
Career Connection
Crucial for securing desirable placements. Effective preparation ensures you can articulate your skills and knowledge, maximizing your chances with top recruiters.
Develop Specialization and Advanced Tools- (Semester 4)
Based on your elective choices (e.g., Supply Chain, Financial OR, Data Analytics), delve deeper into specialized tools and advanced concepts. For example, master specific simulation software for supply chain or advanced econometric packages for financial analysis.
Tools & Resources
Specialized software (e.g., AnyLogic, Arena, MATLAB, R packages), Advanced online courses (Coursera, edX)
Career Connection
Cultivating niche expertise makes you a highly sought-after candidate for specialized roles, distinguishing you in a competitive job market and opening pathways to senior positions faster.
Program Structure and Curriculum
Eligibility:
- B.Sc. (Hons.)/B.A. (Hons.) in Operational Research/Mathematics/Statistics with at least 50% marks in aggregate OR Master''''s Degree in Mathematics/Statistics with at least 50% marks in aggregate OR Bachelor''''s Degree in Engineering/Computer Science with at least 50% marks in aggregate. Candidates must have passed XII standard with Mathematics as one of the subjects.
Duration: 4 semesters / 2 years
Credits: 80 Credits
Assessment: Internal: 25%, External: 75%
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| DSC-OR-101 | Linear Programming | Core | 4 | Introduction to Operational Research, Linear Programming Formulation, Simplex Method, Duality Theory, Sensitivity Analysis, Transportation and Assignment Problems |
| DSC-OR-102 | Inventory Management | Core | 4 | Introduction to Inventory Management, Deterministic Inventory Models, Probabilistic Inventory Models, Inventory Control Systems, Specialized Inventory Models |
| DSC-OR-103 | Probability and Statistics | Core | 4 | Probability Concepts, Random Variables and Distributions, Expectation and Moments, Sampling Distributions, Limit Theorems |
| DSC-OR-104 | Programming in C and Python | Core | 4 | C Programming Fundamentals, Control Structures in C, Functions and Pointers in C, Python Basics and Data Types, Control Flow and Functions in Python, Data Structures in Python |
| GE-OR-105 | Generic Elective I (Choose one from list) | Elective (Generic) | 4 | Econometrics (GE-OR-105(i)): Regression Analysis, Time Series, Panel Data, Computer Networks (GE-OR-105(ii)): Network Models, Protocols, Technologies, Combinatorial Optimization (GE-OR-105(iii)): Graphs, Algorithms, Polyhedra, Data Structure and File Processing (GE-OR-105(iv)): Arrays, Stacks, Trees, Hashing, Regression Analysis (GE-OR-105(v)): Simple, Multiple, Non-linear Regression, Marketing Research (GE-OR-105(vi)): Research Design, Data Collection, Analysis |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| DSC-OR-201 | Integer Programming and Network Flows | Core | 4 | Integer Linear Programming, Cutting Plane Algorithms, Branch and Bound Technique, Network Flow Problems, Shortest Path, Max Flow Min Cut |
| DSC-OR-202 | Queueing Systems | Core | 4 | Introduction to Queueing Theory, Poisson Process, Birth and Death Process, M/M/1, M/M/c Queueing Models, Non-Markovian Queues, Queueing Networks |
| DSC-OR-203 | Statistical Inference | Core | 4 | Point Estimation, Interval Estimation, Hypothesis Testing, Non-parametric Tests, Analysis of Variance |
| DSC-OR-204 | Optimization in Machine Learning | Core | 4 | Basics of Machine Learning, Supervised Learning (Regression, Classification), Unsupervised Learning (Clustering), Optimization Algorithms (Gradient Descent), Neural Networks Fundamentals |
| GE-OR-205 | Generic Elective II (Choose one from list) | Elective (Generic) | 4 | Stochastic Processes (GE-OR-205(i)): Markov Chains, Renewal Theory, Martingales, Digital Image Processing (GE-OR-205(ii)): Image Enhancement, Restoration, Compression, Design and Analysis of Algorithms (GE-OR-205(iii)): Algorithmic Paradigms, Complexity, Financial Modeling (GE-OR-205(iv)): Financial Instruments, Risk Management, Valuation, Database Management Systems (GE-OR-205(v)): ER Model, SQL, Normalization, Transactions, Actuarial Modeling (GE-OR-205(vi)): Life Contingencies, Survival Models, Annuities |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| DSC-OR-301 | Non-linear Programming | Core | 4 | Introduction to Non-linear Programming, Classical Optimization Techniques, Karush-Kuhn-Tucker Conditions, Convex Programming, Quadratic and Separable Programming |
| DSC-OR-302 | Decision Theory and Markov Processes | Core | 4 | Decision Making Under Uncertainty, Utility Theory, Game Theory, Markov Chains, Markov Decision Processes |
| DSC-OR-303 | Reliability and Maintenance Theory | Core | 4 | Reliability Concepts, Life Distributions, System Reliability, Maintenance Policies, Replacement Models |
| DSE-OR-304 | Discipline Specific Elective I (Choose two from list) | Elective (Discipline Specific) | 4 | Financial Operational Research (DSE-OR-304(i)): Portfolio Theory, Option Pricing, Risk Management, Dynamic Programming (DSE-OR-304(ii)): Bellman''''s Principle, Optimal Substructure, Applications, Supply Chain Management (DSE-OR-304(iii)): Logistics, Network Design, Inventory Optimization, Data Analytics and Business Intelligence (DSE-OR-304(iv)): Data Mining, Visualization, Predictive Analytics |
| DSE-OR-305 | Discipline Specific Elective II (Choose another from DSE-OR-304 list) | Elective (Discipline Specific) | 4 | Financial Operational Research (DSE-OR-304(i)): Portfolio Theory, Option Pricing, Risk Management, Dynamic Programming (DSE-OR-304(ii)): Bellman''''s Principle, Optimal Substructure, Applications, Supply Chain Management (DSE-OR-304(iii)): Logistics, Network Design, Inventory Optimization, Data Analytics and Business Intelligence (DSE-OR-304(iv)): Data Mining, Visualization, Predictive Analytics |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| DSC-OR-401 | Metaheuristics | Core | 4 | Heuristics vs Metaheuristics, Genetic Algorithms, Simulated Annealing, Tabu Search, Ant Colony Optimization, Particle Swarm Optimization |
| DSC-OR-402 | Simulation Modelling and Analysis | Core | 4 | Introduction to Simulation, Random Number Generation, Input and Output Analysis, Verification and Validation, Discrete Event Simulation |
| DSC-OR-403 | Project (Dissertation) | Project | 4 | Problem Identification and Formulation, Literature Review, Methodology Development, Data Collection and Analysis, Report Writing and Presentation |
| DSE-OR-404 | Discipline Specific Elective III (Choose two from list) | Elective (Discipline Specific) | 4 | Advanced Linear Programming (DSE-OR-404(i)): Revised Simplex, Decomposition, Convex Sets, Advanced Inventory Management (DSE-OR-404(ii)): Multi-Echelon, Perishable, JIT, Forecasting Methods (DSE-OR-404(iii)): Time Series, Exponential Smoothing, ARIMA, Business Process Re-engineering (DSE-OR-404(iv)): Process Analysis, Redesign, Implementation |
| DSE-OR-405 | Discipline Specific Elective IV (Choose another from DSE-OR-404 list) | Elective (Discipline Specific) | 4 | Advanced Linear Programming (DSE-OR-404(i)): Revised Simplex, Decomposition, Convex Sets, Advanced Inventory Management (DSE-OR-404(ii)): Multi-Echelon, Perishable, JIT, Forecasting Methods (DSE-OR-404(iii)): Time Series, Exponential Smoothing, ARIMA, Business Process Re-engineering (DSE-OR-404(iv)): Process Analysis, Redesign, Implementation |
