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M-SC-APPLIED-QUANTITATIVE-FINANCE-SSE in General at Symbiosis International University (SIU)
Pune, Maharashtra
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About the Specialization
What is General at Symbiosis International University (SIU) Pune?
This M.Sc. (Applied Quantitative Finance) program at Symbiosis International University (SSE) focuses on equipping students with advanced quantitative skills, computational techniques, and comprehensive financial domain knowledge. It addresses the growing demand in the Indian financial services sector for professionals who can leverage data and sophisticated models to drive decision-making in areas like risk management, investment banking, and algorithmic trading. The program''''s strength lies in its blend of rigorous theory and practical application using cutting-edge tools.
Who Should Apply?
This program is ideal for fresh graduates with a strong mathematical or statistical background, as well as working professionals in finance looking to enhance their analytical capabilities. It caters to individuals seeking careers as quantitative analysts, risk managers, or data scientists in financial institutions. Students from engineering, economics, commerce, or science disciplines with a keen interest in finance and data-driven problem-solving are particularly well-suited for this rigorous program.
Why Choose This Course?
Graduates of this program can expect to secure high-value roles in India''''s booming financial sector, including investment banks, asset management firms, hedge funds, and fintech companies. Entry-level salaries typically range from INR 6-12 LPA, with significant growth trajectories for experienced professionals reaching INR 20+ LPA. The curriculum also aligns with international certifications like CFA and FRM, providing a competitive edge in both domestic and global markets.
Student Success Practices
Foundation Stage
Master Core Quantitative and Financial Concepts- (Semester 1-2)
Dedicate significant effort to building a strong foundation in mathematics, statistics, programming (Python/R), and financial markets. Utilize online platforms for supplemental learning, practice problems from textbooks, and engage in peer study groups to clarify concepts thoroughly.
Tools & Resources
Khan Academy, Coursera/edX (for ''''Mathematics for Finance'''' or ''''Python for Finance'''' courses), Python/R IDEs, NISM certifications (Equity Derivatives, Mutual Funds)
Career Connection
A solid foundation is crucial for excelling in advanced subjects and forms the bedrock for interviews for quantitative roles and further specialized studies.
Develop Practical Software Proficiency- (Semester 1-2)
Beyond theoretical understanding, gain extensive hands-on experience with industry-standard software like Excel (Advanced), R, Python, and statistical tools. Actively participate in practical labs and seek out opportunities to apply these tools to real-world financial datasets.
Tools & Resources
Microsoft Excel, Anaconda (for Python), RStudio, Kaggle for financial datasets, DataCamp/Udemy courses
Career Connection
Proficiency in these tools is a non-negotiable skill for quantitative analysts, risk managers, and data scientists in finance, directly enhancing employability.
Engage with Industry through Guest Lectures and Workshops- (Semester 1-2)
Actively attend and participate in guest lectures, webinars, and workshops organized by the department or university. Network with speakers and professionals to gain insights into industry trends, career paths, and practical challenges faced by finance practitioners in India.
Tools & Resources
LinkedIn, SSE career services events, Industry association events (e.g., CFA Society India webinars)
Career Connection
Early industry exposure helps in clarifying career goals, understanding job requirements, and building a professional network vital for internships and placements.
Intermediate Stage
Undertake Mini-Projects and Case Study Competitions- (Semester 3)
Apply theoretical knowledge to practical problems by working on mini-projects, either individually or in teams. Participate in finance and quantitative competitions to test skills, gain recognition, and build a portfolio of applied work. Focus on problem-solving for Indian market scenarios.
Tools & Resources
Kaggle competitions, College-organized hackathons, Indian stock market data sources (e.g., NSE/BSE websites), GitHub for project showcasing
Career Connection
Showcasing practical application through projects and competitions significantly strengthens resumes and provides concrete examples for interview discussions, demonstrating problem-solving abilities.
Pursue Relevant Professional Certifications- (Semester 3)
Consider pursuing relevant certifications like NISM modules (e.g., Equity Derivatives, Research Analyst, Investment Adviser) or foundational levels of CFA/FRM. These add industry-recognized credentials and validate your specialized knowledge in specific financial domains.
Tools & Resources
NISM study material and exams, CFA Institute resources, GARP (FRM) study guides, Online prep courses
Career Connection
Certifications signal serious commitment to a finance career and often make candidates more attractive to employers, especially for roles in investment banking and risk management.
Build a Strong Professional Network- (Semester 3)
Actively network with alumni, faculty, and industry professionals through LinkedIn, conferences, and formal/informal university events. Seek mentors who can provide guidance on career trajectory and specific industry insights within the Indian financial landscape.
Tools & Resources
LinkedIn Professional Network, Alumni association events, Industry meetups and conferences, University career fairs
Career Connection
Networking opens doors to internship opportunities, job referrals, and invaluable career advice, which is highly beneficial in navigating the competitive Indian job market.
Advanced Stage
Focus on In-depth Research for Project Work- (Semester 4)
Leverage the Project I and Project II opportunities to conduct comprehensive research on a contemporary quantitative finance topic relevant to the Indian or global markets. Aim for a publishable-quality dissertation, showcasing advanced analytical and research skills.
Tools & Resources
JSTOR, Google Scholar, ResearchGate, Bloomberg Terminal/Refinitiv Eikon (if available at uni), Statistical software like EViews/STATA, Academic writing tools
Career Connection
A strong research project demonstrates advanced analytical skills, critical thinking, and the ability to work independently, highly valued by employers for research or quantitative analyst roles.
Engage in Intensive Placement Preparation- (Semester 4)
Start preparing for placements early by practicing quantitative aptitude, logical reasoning, and technical interview questions related to financial modeling, derivatives, and machine learning. Participate in mock interviews and group discussions to hone soft skills relevant for the Indian corporate sector.
Tools & Resources
Interviewbit, LeetCode (for coding questions), Quant interview prep books, Online financial news portals (Livemint, Economic Times), University placement cell resources
Career Connection
Thorough preparation for placement processes significantly increases the chances of securing desirable job offers from top-tier companies and provides confidence in facing rigorous evaluations.
Specialized Skill Development and Portfolio Building- (Semester 4)
Identify specific areas of interest (e.g., algorithmic trading, credit risk, FinTech) and develop advanced skills through online courses or self-study. Build a compelling GitHub portfolio showcasing advanced projects, code contributions, and analyses to differentiate yourself in the job market.
Tools & Resources
Specialized MOOCs (e.g., edX ''''Algorithmic Trading'''' series), Kaggle Grandmaster competitions, GitHub repository, Domain-specific financial libraries
Career Connection
A specialized skill set and a robust portfolio are crucial for targeting niche roles in quantitative finance, demonstrating expertise and readiness for challenging industry positions in India and globally.
Program Structure and Curriculum
Eligibility:
- Bachelor''''s Degree from any recognized University/Institution of National Importance with a minimum of 50% marks (45% for SC/ST category) or equivalent grade, and Mathematics as one of the subjects at HSC (10+2 level) or equivalent examination.
Duration: 4 semesters / 2 years
Credits: 92 Credits
Assessment: Internal: 50% per subject, External: 50% per subject
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| DAF | Data Analysis for Finance | Core | 4 | Introduction to Statistical Software, Data Visualization, Probability Distributions, Hypothesis Testing, Regression Analysis |
| MAF | Mathematics for Finance | Core | 4 | Linear Algebra, Calculus, Differential Equations, Optimization, Numerical Methods |
| FIMS | Financial Institutions, Markets and Services | Core | 4 | Financial System Overview, Money Market, Capital Market, Financial Intermediaries, Investment Banking |
| FAR | Financial Accounting and Reporting | Core | 4 | Accounting Principles, Financial Statements Analysis, Cash Flow Statement, Ratio Analysis, Valuation Basics |
| AEFM | Advanced Excel for Financial Modelling | Core (Practical) | 2 | Advanced Excel Functions, Data Analysis Tools, Financial Modelling Techniques, Scenario Analysis, Goal Seek and Solver |
| QCS | Quantitative Communication Skills | Core | 2 | Business Communication, Presentation Skills, Technical Report Writing, Data Storytelling, Professional Ethics |
| IAPM | Investment Analysis and Portfolio Management | Core | 4 | Investment Environment, Risk and Return, Portfolio Theory, Asset Allocation, Equity and Bond Valuation |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| PFF | Programming for Finance | Core | 4 | Python/R Basics, Data Structures and Algorithms, Object-Oriented Programming, Financial Libraries (Numpy, Pandas), Data Manipulation |
| DM | Derivative Markets | Core | 4 | Futures and Forwards, Options and Swaps, Pricing Models Introduction, Hedging Strategies, Market Mechanics |
| FIS | Fixed Income Securities | Core | 4 | Bond Characteristics, Yield Measures, Interest Rate Risk, Term Structure of Interest Rates, Securitization |
| EFF | Econometrics for Finance | Core | 4 | OLS Regression, Time Series Analysis, Panel Data Econometrics, Volatility Models (ARCH/GARCH), Forecasting Techniques |
| DVF | Data Visualization for Finance | Core (Practical) | 2 | Data Visualization Principles, Tableau/Power BI Introduction, Interactive Dashboards, Storytelling with Data, Financial Reporting Visuals |
| RM | Research Methodology | Core | 2 | Research Design, Data Collection Methods, Sampling Techniques, Statistical Inference, Academic Report Writing |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| FRM | Financial Risk Management | Core | 4 | Market Risk, Credit Risk, Operational Risk, Risk Measurement Models (VaR, ES), Regulatory Frameworks (Basel Accords) |
| FE | Financial Econometrics | Core | 4 | Advanced Time Series Models, Panel Data Applications, Cointegration and Causality, Non-Linear Econometrics, Asset Pricing Models |
| MLF | Machine Learning for Finance | Core | 4 | Supervised and Unsupervised Learning, Ensemble Methods, Neural Networks and Deep Learning, Text Mining for Finance, Algorithmic Trading Applications |
| PI | Project I | Project | 4 | Research Problem Identification, Literature Review, Methodology Design, Data Collection Plan, Initial Data Analysis and Reporting |
| AI | Alternative Investments | Elective | 4 | Hedge Funds, Private Equity and Venture Capital, Real Estate Investments, Commodities and Infrastructure, Performance Evaluation |
| CRM | Credit Risk Modelling | Elective | 4 | Default Probability Models, Loss Given Default (LGD), Exposure at Default (EAD), Credit Rating Systems, Credit Derivatives |
| BF | Behavioural Finance | Elective | 4 | Cognitive Biases and Heuristics, Prospect Theory, Market Anomalies, Investor Psychology, Decision Making under Uncertainty |
| ATS | Algorithmic Trading Strategies | Elective | 4 | High-Frequency Trading, Market Microstructure, Algorithmic Execution Strategies, Strategy Backtesting, Quantitative Trading Systems |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| DPH | Derivatives Pricing and Hedging | Core | 4 | Option Pricing Models (Black-Scholes), Volatility Smiles and Skews, Exotic Options, Hedging Strategies with Derivatives, Interest Rate Derivatives |
| CF | Computational Finance | Core | 4 | Monte Carlo Simulation, Numerical Optimization, Finite Difference Methods, Stochastic Calculus Applications, Implementation in Python/R |
| BDAF | Big Data Analytics for Finance | Core | 4 | Hadoop and Spark Ecosystems, NoSQL Databases, Big Data Tools and Technologies, Cloud Computing for Finance, Real-time Financial Analytics |
| PII | Project II | Project | 4 | Project Execution and Data Analysis, Results Interpretation and Discussion, Dissertation Writing and Formatting, Oral Presentation and Defense, Ethical Research Practices |
| BCC | Blockchain and Cryptocurrency | Elective | 4 | Blockchain Technology Fundamentals, Cryptocurrencies and Digital Assets, Decentralized Finance (DeFi), Smart Contracts and DApps, Regulatory Landscape in India |
| QPM | Quantitative Portfolio Management | Elective | 4 | Portfolio Optimization Techniques, Factor Models in Asset Pricing, Performance Attribution Analysis, Risk Budgeting and Control, Quantitative Trading Strategies |
| CMEF | Commodity Markets and Energy Finance | Elective | 4 | Commodity Derivatives, Energy Markets Structure, Carbon Trading and Green Finance, Hedging in Commodity Markets, Supply Chain Finance |
| FTDB | FinTech and Digital Banking | Elective | 4 | Digital Payments Ecosystem, Neobanks and Challenger Banks, Robo-Advisors and WealthTech, Regulatory Technology (RegTech), Artificial Intelligence in Banking |
