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DUAL-DEGREE-B-TECH-BS-M-TECH-MS in Financial Engineering at Indian Institute of Technology Kharagpur
Paschim Medinipur, West Bengal
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About the Specialization
What is Financial Engineering at Indian Institute of Technology Kharagpur Paschim Medinipur?
This Industrial Engineering with specialization in Financial Engineering program at IIT Kharagpur focuses on applying advanced quantitative methods, computational tools, and engineering principles to solve complex problems in financial markets. It combines a strong foundation in Industrial Engineering with specialized knowledge in finance, addressing the growing demand for professionals who can leverage data and analytics in India''''s dynamic financial sector. The program emphasizes a blend of theoretical rigor and practical application, preparing students for quantitative roles.
Who Should Apply?
This program is ideal for high-achieving engineering graduates or those with strong quantitative backgrounds seeking entry into India''''s evolving financial landscape. It caters to fresh graduates aspiring for roles in quantitative finance, risk management, or investment banking. Working professionals looking to upskill in financial modeling, data analytics, and algorithmic trading will also find immense value, as well as career changers from core engineering disciplines transitioning into the thriving FinTech industry.
Why Choose This Course?
Graduates of this program can expect to pursue India-specific career paths in investment banks, asset management firms, hedge funds, consulting firms, and FinTech startups. Roles include quantitative analyst, risk manager, portfolio strategist, financial modeler, and data scientist. Entry-level salaries typically range from INR 10-20 LPA, growing significantly with experience to INR 30-50+ LPA for seasoned professionals. The program also aligns with certifications like CFA, FRM, and financial modeling expertise, fostering robust growth trajectories in Indian financial companies.
Student Success Practices
Foundation Stage
Master Core Engineering Fundamentals- (Semester 1-2)
Dedicate significant effort to building a strong foundation in mathematics, probability, statistics, and programming. These subjects are critical for understanding advanced financial models. Utilize online platforms like NPTEL for supplemental learning and join departmental study groups for problem-solving sessions.
Tools & Resources
NPTEL courses for Maths/Probability, Coursera/edX for foundational programming (Python/C++), Departmental study groups
Career Connection
A robust foundation ensures you can grasp the quantitative aspects of financial engineering and excel in technical interviews for advanced roles.
Develop Strong Programming Skills- (Semester 1-3)
Beyond classroom learning, consistently practice coding using online competitive programming platforms. Focus on data structures and algorithms, which are fundamental for computational finance and algorithmic trading. Participate in hackathons to apply skills practically.
Tools & Resources
CodeChef, HackerRank, GeeksforGeeks, GitHub for personal projects
Career Connection
Proficiency in programming (especially Python, C++) is non-negotiable for quantitative finance roles, enabling you to build and implement financial models.
Engage in Peer Learning & Mentorship- (Semester 1-2)
Form study groups with peers to discuss complex concepts and prepare for exams. Seek guidance from senior students and faculty mentors on academic pathways and career opportunities in financial engineering. This fosters a collaborative learning environment.
Tools & Resources
Departmental student associations, Faculty office hours, LinkedIn for connecting with alumni
Career Connection
Networking and peer support can provide invaluable insights into career options, internship leads, and academic strategies specific to your specialization.
Intermediate Stage
Undertake Domain-Specific Internships- (Semester 4-6)
Actively seek summer internships in finance-related roles such as quantitative analysis, risk management, or investment research. Prioritize opportunities that offer exposure to financial modeling, data analytics, or market research within Indian financial institutions or MNCs operating in India.
Tools & Resources
IIT Kharagpur Career Development Centre, LinkedIn, Internshala, Company career pages
Career Connection
Practical experience is crucial for understanding real-world financial problems and significantly boosts your resume for final placements.
Specialize in Financial Modeling & Analytics- (Semester 5-7)
Beyond core curriculum, take additional online courses or workshops in advanced Excel, VBA, R, and financial modeling tools. Develop projects involving real financial datasets to apply concepts learned in Quantitative Finance and Derivatives courses. Aim for certifications if time permits.
Tools & Resources
Coursera/edX for Financial Modeling, Bloomberg Terminal (if accessible), Kaggle for financial datasets
Career Connection
Deep expertise in financial modeling and analytics is highly valued, opening doors to roles in investment banking, asset management, and FinTech.
Participate in Finance Competitions & Quants Clubs- (Semester 3-7)
Join the institute''''s finance clubs or quantitative trading societies. Participate in inter-college finance competitions, stock market simulations, or case study challenges. This provides hands-on experience, networking opportunities, and a chance to test your skills in a competitive environment.
Tools & Resources
Institute Finance Club, National level finance competitions (e.g., conducted by IIMs, IITs), Simulated trading platforms
Career Connection
Such participation demonstrates initiative, practical skills, and a keen interest in finance, making you a more attractive candidate for recruiters.
Advanced Stage
Focus on M.Tech Dissertation/Project for Specialization- (Semester 8-10)
Choose a dissertation topic that directly aligns with your career aspirations in financial engineering, such as algorithmic trading, credit risk modeling, or FinTech innovation. Work closely with faculty advisors and aim to publish research papers or present at conferences, if possible.
Tools & Resources
Academic research databases, Faculty research groups, LaTeX for technical writing
Career Connection
A strong, relevant M.Tech project showcases your expertise, research capabilities, and problem-solving skills, which are highly regarded by top financial firms and for further academic pursuits.
Intensive Placement Preparation- (Semester 8-9)
Engage in rigorous preparation for placements, focusing on quantitative aptitude, logical reasoning, and domain-specific knowledge in finance, stochastic processes, and machine learning. Practice mock interviews, group discussions, and aptitude tests targeted by finance companies. Develop a strong portfolio of projects.
Tools & Resources
Placement cell resources, InterviewBit, Glassdoor for company-specific interview experiences
Career Connection
Systematic preparation is key to converting placement opportunities into job offers in highly competitive financial roles.
Build a Professional Network- (Semester 7-10)
Actively network with alumni working in financial engineering roles, industry professionals, and guest lecturers. Attend workshops, seminars, and industry events (online and offline) to expand your professional contacts and stay updated on industry trends, especially in India''''s FinTech sector.
Tools & Resources
LinkedIn, Alumni association events, Industry conferences (e.g., NASSCOM, FICCI events related to FinTech)
Career Connection
A strong professional network can lead to mentorship, job referrals, and insights into niche opportunities in the dynamic financial industry.
Program Structure and Curriculum
Eligibility:
- Admission to the B.Tech/B.S. program is based on JEE Advanced ranks. Students must meet specific academic performance criteria (CGPA) and departmental requirements to opt for and continue in the M.Tech/M.S. Financial Engineering specialization.
Duration: 10 semesters (5 years)
Credits: 221 Credits
Assessment: Assessment pattern not specified
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| MA11001 | Mathematics - I | Common Core | 4 | |
| PH11001 | Physics - I | Common Core | 4 | |
| CY11001 | Chemistry - I | Common Core | 4 | |
| EE11001 | Electrical Technology | Common Core | 4 | |
| CS10001 | Programming and Data Structures | Common Core | 3 | |
| LA11001 | English for Communication | Common Core | 3 |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| MA11002 | Mathematics - II | Common Core | 4 | |
| PH11002 | Physics - II | Common Core | 4 | |
| CY11002 | Chemistry - II | Common Core | 4 | |
| CE11001 | Engineering Drawing and Graphics | Common Core | 3 | |
| ME11001 | Engineering Mechanics | Common Core | 3 | |
| PH19001 | Physics Lab | Common Core Lab | 2 | |
| CS19001 | Programming & Data Structures Lab | Common Core Lab | 3 |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| MA20001 | Transform Calculus | Departmental Core | 3 | |
| IE21001 | Industrial Psychology & Human Relations | Departmental Core | 3 | |
| IE21003 | Engineering Economy | Departmental Core | 3 | |
| IE21005 | Manufacturing Processes | Departmental Core | 3 | |
| IE21007 | Probability and Stochastic Processes | Departmental Core | 4 | |
| ID20001 | Introduction to Industrial Design | Departmental Core | 3 | |
| IE29001 | Manufacturing Processes Lab | Departmental Lab | 2 | |
| EV20001 | Environmental Science | Institute Core | 2 |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| MA20004 | Numerical Methods | Departmental Core | 3 | |
| IE21002 | Operations Research | Departmental Core | 4 | |
| IE21004 | Ergonomics | Departmental Core | 3 | |
| IE21006 | Statistical Quality Control | Departmental Core | 3 | |
| IE21008 | Production Planning & Control | Departmental Core | 3 | |
| IE29002 | Operations Research Lab | Departmental Lab | 2 | |
| IE29004 | Statistical Quality Control Lab | Departmental Lab | 2 | |
| IE29006 | Industrial Engineering Lab - I | Departmental Lab | 2 |
Semester 5
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| IE31001 | Maintenance Engineering | Departmental Core | 3 | |
| IE31003 | Applied Probability and Statistics | Departmental Core | 4 | |
| IE31005 | Simulation Modeling & Analysis | Departmental Core | 4 | |
| IE31007 | Facilities Planning and Design | Departmental Core | 3 | |
| IE39001 | Industrial Engineering Lab - II | Departmental Lab | 2 | |
| HSXXXXX | Humanities Elective - I | Institute Elective | 3 | |
| IE39003 | Simulation Lab | Departmental Lab | 4 |
Semester 6
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| IE31002 | Supply Chain Management | Departmental Core | 3 | |
| IE31004 | Work System Design | Departmental Core | 3 | |
| IE31006 | Financial Engineering | Departmental Core (Specialization) | 3 | |
| IE39002 | Industrial Engineering Lab - III | Departmental Lab | 2 | |
| OEXXXXX | Open Elective - I | Open Elective | 3 | |
| IE31008 | Decision Modelling and Optimization | Departmental Core | 4 | |
| IE39004 | Work System Design Lab | Departmental Lab | 4 | |
| IE39006 | Term Project (Design & Practice) | Departmental Project | 1 |
Semester 7
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| IE41001 | Analytics for Financial Decision Making | Professional Elective (Specialization) | 3 | |
| IE41003 | Management Information Systems | Professional Elective (Specialization) | 3 | |
| IE41005 | Derivatives & Risk Management | Professional Elective (Specialization) | 3 | |
| IE41007 | Stochastic Processes in Finance | Professional Elective (Specialization) | 3 | |
| IE41009 | Computational Finance | Professional Elective (Specialization) | 3 | |
| IE49001 | Analytics for Financial Decision Making Lab | Professional Elective Lab (Specialization) | 2 | |
| IE49003 | Derivatives & Risk Management Lab | Professional Elective Lab (Specialization) | 2 | |
| IE49005 | Computational Finance Lab | Professional Elective Lab (Specialization) | 2 |
Semester 8
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| IE40002 | Financial Econometrics | Professional Elective (Specialization) | 3 | |
| IE40004 | Portfolio Management & Investment Analysis | Professional Elective (Specialization) | 3 | |
| IE40006 | Machine Learning in Finance | Professional Elective (Specialization) | 3 | |
| IE40008 | Financial Regulation & Ethics | Professional Elective (Specialization) | 3 | |
| IE40010 | Fixed Income Securities | Professional Elective (Specialization) | 3 | |
| IE49002 | Financial Econometrics Lab | Professional Elective Lab (Specialization) | 2 | |
| IE49004 | Machine Learning in Finance Lab | Professional Elective Lab (Specialization) | 2 | |
| IE47002 | B.Tech Project Part-1 | Departmental Project | 1 |
Semester 9
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| IE50001 | Advanced Optimization in Finance | Professional Core (Specialization) | 3 | |
| IE50003 | Quantitative Trading Strategies | Professional Core (Specialization) | 3 | |
| IE50005 | Financial Technology (FinTech) | Professional Core (Specialization) | 3 | |
| IE50007 | Risk Analytics and Management | Professional Core (Specialization) | 3 | |
| IE59001 | Advanced Optimization in Finance Lab | Professional Core Lab (Specialization) | 2 | |
| IE59003 | Quantitative Trading Strategies Lab | Professional Core Lab (Specialization) | 2 | |
| IE57001 | M.Tech Dissertation Part-1 | Departmental Project | 4 |
Semester 10
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| IE50002 | Behavioral Finance | Professional Elective (Specialization) | 3 | |
| IE50004 | Financial Systems & Markets | Professional Elective (Specialization) | 3 | |
| IE50006 | Credit Risk Modeling | Professional Elective (Specialization) | 3 | |
| IE50008 | AI & Blockchain in Finance | Professional Elective (Specialization) | 3 | |
| IE57002 | M.Tech Dissertation Part-2 | Departmental Project | 8 |
