Finance and Financial Technology
Program's Goal: Building Financial Technologists, Quant Developers, and Early-Stage CFOs or Fintech CTOs.
This program is not designed to produce employees; it is designed to produce finance practitioners with technical skills. The curriculum assumes that modern finance is built by people who understand both markets and systems end-to-end: mathematics \(\rightarrow\) computation \(\rightarrow\) markets & finance \(\rightarrow\) products \(\rightarrow\) users.
A bachelor's program is usually four years long. This duration exists to give you time to build foundations before you specialize, and to revisit ideas often enough that they actually stick. The program is usually divided into 8 terms, each designed to teach one important way of thinking and give you enough practice to see where it breaks.
TERM 1: Foundations
| Course Code | Course | Objective |
|---|---|---|
| MATH 101 | Linear Algebra | Portfolio theory, factor models, and risk decomposition all live in linear algebra. Also essential for ML in finance. Learn to think in vectors and matrices. |
| MATH 102 | Calculus | Returns change continuously. Derivatives price based on rates of change. This course builds intuition for how financial variables move and interact over time. |
| CS 101 | Introduction to Computational Thinking | Transition from "this is a market problem" to "this is a problem a computer can solve." Learn to automate financial analysis and decision-making. |
| FIN 101 | Introduction to Finance | Learn the time value of money, present value thinking, and how financial markets actually work. This is the foundation everything else builds on. |
| FIN 103 | Financial Accounting | Learn to read balance sheets, income statements, and cash flow statements. You'll be building systems that analyze these. |
| CS 103 | Fundamentals of Web Development | Build financial dashboards and web-based trading interfaces from day one. Learn to ship financial tools, not just models. |
TERM 2: Working with Financial Data
| Course Code | Course | Objective |
|---|---|---|
| MATH 103 | Multivariate Calculus | Modern portfolio theory, option pricing models, and risk management all require multivariable thinking. Learn to optimize in high dimensions. |
| MATH 104 | Discrete Mathematics | Financial contracts, computational finance, and algorithmic trading rely on discrete structures. This is the math that actually computes. |
| CS 105 | Data Structures & Algorithms | Efficient market data processing, order book management, and portfolio optimization require proper data structures. Learn to think algorithmically about finance. |
| CS 106 | Database Management Systems | Financial data outlives trading systems. Tick data, transactions, positions: all need proper storage and retrieval. Learn to build systems that remember. |
| FIN 104 | Managerial Accounting | Cost accounting, performance measurement. You'll be building financial reporting systems and understand what metrics matter. |
| PROJECT 101 | Build FinTech Tool - 1 | Build a financial calculator, portfolio tracker, or stock screener. Ship it as a web app. Practice the full cycle: idea, build, deploy, iterate with real market data. |
TERM 3: Quantitative Foundations
| Course Code | Course | Objective |
|---|---|---|
| MATH 202 | Mathematical Transforms | Fourier analysis for signal processing in market data, filtering techniques, spectral analysis. Essential for quantitative trading and technical analysis systems. |
| CS 201 | Algorithmic Problem Solving | Competitive coding sharpens your ability to solve optimization problems fast. Also, this is how you'll be interviewed at every quant firm. |
| CS 202 | Object-Oriented Design Patterns | Trading systems, risk platforms, and market data handlers benefit from proper architecture. Learn patterns that scale in production finance. |
| CS 203 | Computer Architecture & Operating Systems | Latency matters in trading. Memory hierarchies affect backtest speed. Learn what happens under the hood so you can optimize financial systems. |
| FIN 201 | Corporate Finance | Capital structure, M&A, capital budgeting. Learn how companies make financial decisions: you'll be building systems to analyze and automate these decisions. |
| PROJECT 201 | Build FinTech Tool - 2 | Build a backtesting engine or algorithmic trading simulator. Implement multiple strategies with realistic transaction costs. Learn the gap between theory and execution. |
TERM 4: Production Systems + Derivatives
| Course Code | Course | Objective |
|---|---|---|
| MATH 203 | Optimization Theory | Portfolio optimization, execution algorithms, risk budgeting: all are optimization problems. Learn to find optimal solutions under real-world constraints. |
| MATH 204 | Probability Theory | Markets are stochastic. Risk is probabilistic. Options pricing depends on probability. Learn rigorous probability so you model uncertainty correctly. |
| CS 204 | Modern Web Development | Build production-grade financial applications using modern frameworks. Real fintech products need proper engineering, not just working prototypes. |
| CS 205 | DevOps & Monitoring | Trading systems run 24/7. Market data pipelines can't go down. Learn to deploy, monitor, and maintain financial systems in production. |
| CS 206 | Computer Networks | Every trade goes over a network. Latency, packets, TCP/UDP: understand how market data flows and orders execute across the internet. |
| PROJECT 202 | Build FinTech Tool - 3 | Build a real-time market data dashboard with proper deployment infrastructure. WebSocket connections, live updates, monitoring. Keep it running for 30 days straight. |
TERM 5: Machine Learning
| Course Code | Course | Objective |
|---|---|---|
| MATH 301 | Statistical Theory | Hypothesis testing, regression, estimation. Learn to distinguish signal from noise in market data and validate trading strategies rigorously. |
| MATH 302 | Stochastic Processes | Asset prices are stochastic processes. Learn Brownian motion, random walks, mean reversion. This is how markets evolve mathematically. |
| CS 301 | System Design & Architecture | Design trading platforms, risk systems, market data infrastructure. Learn to architect systems that handle millions of transactions and survive market crashes. |
| CS 402 | Machine Learning & Pattern Recognition | Apply ML to finance: return prediction, risk modeling, portfolio construction. Learn what works and what's just overfitting. |
| FIN 202 | Financial Modeling & Forecasting | Three-statement models, valuation, scenario analysis. Build models that combine traditional finance with computational techniques. |
| PROJECT 301 | Build FinTech Tool - 4 | Build an ML-powered trading system or risk analytics platform. Train models on historical data, implement prediction pipeline, backtest with realistic assumptions. |
TERM 6: Blockchain + Cloud Infrastructure
| Course Code | Course | Objective |
|---|---|---|
| MATH 304 | Numerical Methods | Option pricing, Monte Carlo simulations, numerical optimization. The math that makes quantitative finance computationally feasible. |
| CS 304 | Introduction to Data Engineering | Build data pipelines for financial data: ingestion, transformation, storage. Modern finance is data engineering. |
| CS 305 | Cryptography & Coding Theory | Cryptography powers blockchain, secure transactions, and digital assets. Learn the math behind trust in decentralized finance. |
| CS 306 | Cloud Architecture & Scalability | Deploy trading systems on AWS/GCP/Azure. Auto-scaling for market volatility, disaster recovery, multi-region architectures. |
| FIN 301 | Risk Management & Insurance | VaR, stress testing, scenario analysis, hedging. Build systems that quantify and manage risk before it destroys portfolios. |
| PROJECT 302 | Build FinTech Tool - 5 | Build a cryptocurrency analytics platform or DeFi application. On-chain data analysis, smart contract interaction, cloud deployment. Experience blockchain finance firsthand. |
TERM 7: Advanced ML + Trading Systems
| Course Code | Course | Objective |
|---|---|---|
| MATH 303 | Stochastic Calculus | Continuous-time finance, Ito's lemma, Black-Scholes derivation. This is the rigorous math behind derivatives pricing. |
| CS 307 | Introduction to Parallel Computing | Backtesting millions of strategy variations, real-time risk calculations, high-frequency data processing—all need parallelism. |
| CS 401 | Data Sourcing & Mining | Scrape alternative data: news, social media, satellite imagery. Modern alpha comes from unique datasets. Learn to acquire and process them. |
| CS 403 | Deep Learning | Neural networks for time series forecasting, NLP for news sentiment, reinforcement learning for trading. Understand deep learning's role in modern finance. |
| PROJECT 401 | Build FinTech Startup - 1 | Launch a fintech product. Could be a trading platform, robo-advisor, payment solution, or financial data API. Real users, real data, real technical challenges. |
TERM 8: Production ML + Fintech Scale
| Course Code | Course | Objective |
|---|---|---|
| MATH 401 | Modelling & Simulation | Monte Carlo for option pricing, market microstructure simulation, agent-based models of markets. Build computational models of complex financial systems. |
| MATH 402 | Game Theory & Strategy | Strategic trading, market making, auction theory, mechanism design. Finance is often a game between sophisticated players. |
| CS 308 | GPU Programming & CUDA | Accelerate financial computations: options pricing, backtesting, ML training. GPUs are essential for modern quantitative finance. |
| CS 404 | MLOps & Production Deployment | Deploy ML models in production trading systems. Monitoring, retraining, A/B testing, version control. The gap between notebook and production. |
| FIN 403 | Venture Finance & Cap Tables | Cap tables, burn rate, fundraising, venture economics. You're building a fintech company, you should understand the business side. |
| PROJECT 402 | Build FinTech Startup - 2 | Scale your fintech product. Add ML/AI capabilities. Handle real transaction volume. Navigate regulatory considerations. Pitch to investors. |
Program's Goal: Building Financial Technologists with specialized expertise in quantitative trading, blockchain infrastructure, or enterprise fintech systems.
Just like the Bachelor's program, this program is not designed to produce employees; it is designed to produce specialized financial technologists. Assuming that one knows and has covered the content from the Bachelor's program, one can opt for one of the three specialization tracks possible.
A master's program is usually two years long. In the first year, students learn the specialized skill and build two projects. In the second year, we expect them to build a specialized fintech business or trading system. The entire second year is the residency program focused just on building!
TERM 1: Mathematical Foundations of Markets
| Course Code | Course | Objective |
|---|---|---|
| MATH 501 | Bayesian Computational Models | Bayesian inference for trading signals, prior belief updating, probabilistic forecasting. Markets require continuous belief revision under uncertainty. |
| MATH 502 | Time Series Analysis | Markets remember the past in subtle ways. Learn how to model trends, regimes, volatility, and autocorrelation so you don’t mistake noise for signal. |
| MATH 503 | Quantitative Finance | This course translates market intuition into math: asset pricing, derivatives, portfolio construction, and arbitrage. Learn why some strategies work, why most don’t, and where models break. |
| CS 501 | Advanced Data Sourcing & Mining | Alternative data sourcing, web scraping at scale, data quality assessment. Alpha comes from unique data sources. |
| CS 502 | Parallel & Distributed Computing | Trading systems compete on speed and scale. Learn how computation spreads across cores and machines so your strategies don’t die waiting for results. |
| PROJECT 503 | Shipped Product - 1 | Build a backtesting engine from scratch. Implement multiple strategies with realistic transaction costs, slippage, and risk metrics. Learn how easy it is to fool yourself with historical data. |
Note: This program is same as the one provided to people with CS and AI backgrounds. However, since Finance & Financial Technology students have already covered stochastic courses in bachelor's program, they study MATH 501 and CS 501 as replacements.
TERM 2: Production Trading Infrastructure
| Course Code | Course | Objective |
|---|---|---|
| MATH 504 | Quantitative Risk Management | Profit without risk control is just delayed failure. Learn how to model downside, tail risk, drawdowns, and capital constraints so systems survive bad days, not just good ones. |
| CS 505 | Data Integration & Warehousing | Market data is messy, delayed, and inconsistent. This course teaches how to build reliable data pipelines so trading decisions are based on reality, not corrupted feeds. |
| CS 506 | Advanced Database Engineering | Low latency and correctness rarely coexist by accident. Learn how to design storage systems that serve real-time decisions without sacrificing consistency or safety. |
| CS 510 | Algorithmic Trading | Turn mathematical strategies into executable systems. This course focuses on execution logic, order management, market impact, and the practical constraints that separate theory from profits. |
| CS 511 | Network Modelling & Optimization | Markets are networks of participants, venues, and constraints. Learn how to model and optimize flows: orders, capital, and risk, across complex, interconnected systems. |
| PROJECT 504 | Shipped Product - 2 | Build a real-time trading system with sub-millisecond latency requirements, proper order management, and risk controls. Paper trade for 30 days and experience how systems behave when the markets fight back. |
TERM 3 & 4: Build Trading Firm
Try to build algorithmic trading firms, robo-advisors, risk analytics platforms, or crypto trading infrastructure.
TERM 1: Blockchain Fundamentals & Smart Contracts
| Course Code | Course | Objective |
|---|---|---|
| MATH 501 | Bayesian Computational Models | Probabilistic reasoning for blockchain consensus, Byzantine fault tolerance, uncertainty in decentralized systems. |
| CS 501 | Advanced Data Sourcing & Mining | On-chain data analysis, blockchain indexing, mempool monitoring, MEV detection. Extract insights from decentralized ledgers. |
| CS 502 | Parallel & Distributed Computing | Blockchain nodes are distributed systems. Learn the computational architecture of decentralized networks. |
| CS 503 | Data Intensive Computing | Process blockchain data at scale: full node sync, transaction indexing, state reconstruction. Blockchains generate massive data. |
| CS 521 | Smart Contract Development & Security | Solidity, Rust for Solana, contract architecture, common vulnerabilities, formal verification. Build secure decentralized applications. |
| PROJECT 603 | DeFi Protocol - 1 | Build a DeFi protocol: DEX, lending platform, or derivatives protocol. Deploy to testnet. Audit for security vulnerabilities. Learn smart contract economics. |
TERM 2: DeFi Infrastructure & Scaling
| Course Code | Course | Objective |
|---|---|---|
| MATH 504 | Quantitative Risk Management | Protocol risk modeling, liquidation mechanisms, oracle failures, systemic DeFi risk. Build resilient decentralized systems. |
| CS 505 | Data Integration & Warehousing | Integrate on-chain and off-chain data, build blockchain analytics platforms, cross-chain data aggregation. |
| CS 506 | Advanced Database Engineering | Blockchain indexers, graph databases for wallet relationships, optimized queries for on-chain analytics. |
| CS 522 | Layer 2 Scaling & Cross-Chain Infrastructure | Rollups, state channels, sidechains, bridges, interoperability. Learn how blockchain systems scale beyond Layer 1 constraints. |
| CS 523 | Tokenomics & Mechanism Design | Token distribution, governance mechanisms, incentive alignment, AMM design. Economics of decentralized protocols. |
| PROJECT 604 | DeFi Protocol - 2 | Deploy production DeFi protocol to mainnet. Handle real user funds, implement governance, monitor protocol health. Experience the full responsibility of decentralized finance. |
TERM 3 & 4: Build Blockchain Business
Launch a DeFi protocol, blockchain infrastructure company, or crypto trading platform. Raise funding. Acquire users. Navigate regulatory landscape.
TERM 1: Financial Data Infrastructure
| Course Code | Course | Objective |
|---|---|---|
| MATH 502 | Time Series Analysis | Financial event streams, transaction pattern analysis, fraud detection time series. Enterprise fintech is real-time data processing. |
| CS 501 | Advanced Data Sourcing & Mining | Aggregate financial data from banks, payment processors, third-party APIs. Modern fintech is data integration. |
| CS 502 | Parallel & Distributed Computing | Process millions of transactions concurrently, distributed ledger systems, parallel fraud detection. Scale matters in payments. |
| CS 503 | Data Intensive Computing | Transaction data pipelines, real-time analytics on payment flows, data lake architectures for financial services. |
| CS 524 | Payment Systems & Processing | Payment rails, settlement, clearing, card networks, ACH, wire transfers. Learn how money actually moves. |
| PROJECT 605 | Payment Platform - 1 | Build a payment processing system or digital wallet. Handle transactions, implement fraud detection, ensure PCI compliance. Deploy to sandbox environment. |
TERM 2: Regulatory Technology & Scale
| Course Code | Course | Objective |
|---|---|---|
| CS 505 | Data Integration & Warehousing | Integrate banking systems, payment processors, KYC providers, regulatory reporting. Enterprise fintech is integration complexity. |
| CS 506 | Advanced Database Engineering | ACID transactions for financial systems, consistency guarantees, transaction isolation. Financial data demands correctness. |
| CS 509 | Software Testing | Test financial transactions, property-based testing for money movements, chaos engineering for payment systems. |
| CS 525 | RegTech & Compliance Automation | KYC/AML automation, transaction monitoring, regulatory reporting, GDPR compliance. Learn to build systems that satisfy regulators. |
| CS 526 | Open Banking & API Design | PSD2, account aggregation, OAuth for financial data, API security. Modern banking is API-driven. |
| PROJECT 606 | Payment Platform - 2 | Scale payment platform to handle 10K+ transactions/day. Implement full KYC/AML, regulatory reporting, multi-currency support. Prepare for regulatory audit. |
TERM 3 & 4: Build Enterprise Fintech
Launch a B2B payment platform, banking-as-a-service, embedded finance solution, or regtech company. Sign enterprise clients. Navigate bank partnerships and licensing.
Program's Goal: Train people to make them eligible for fintech developer, blockchain developer, and quantitative analyst roles.
This program is designed to produce employees. The philosophy is to cover only the essential skills over a period of 9 months. The last 3 months are dedicated to placements and job hunting. Placements happen on our own job platform.
TERM 1: Programming + Finance Foundations
| Course Code | Course | Objective |
|---|---|---|
| CS 101 | Introduction to Computational Thinking | Transition from "this is a market problem" to "this is a problem a computer can solve." Learn to automate financial analysis. |
| CS 102 | Programming Paradigms | Master multiple programming paradigms. Functional programming is common in quant finance. OOP structures trading systems. |
| CS 103 | Fundamentals of Web Development | Build financial dashboards and web-based tools. Learn to ship working fintech applications, not just scripts. |
| CS 105 | Data Structures & Algorithms | Efficient market data processing, order book management require proper algorithms. Think computationally about finance. |
| FIN 101 | Introduction to Finance | Time value of money, financial markets, instruments. Understand what you're building systems for. |
| PROJECT 101 | Build FinTech Tool - 1 | Build a portfolio tracker, stock screener, or financial calculator. Deploy as web app. Work with real market data APIs. |
Note: Ideally, students should be able to secure junior fintech developer or financial data analyst roles with this foundation alone. Any further study is only required when looking for specialized roles.
TERM 2: Production Systems + Markets
| Course Code | Course | Objective |
|---|---|---|
| CS 106 | Database Management Systems | Store financial data properly: transactions, positions, market data. Learn database design for finance. |
| CS 204 | Modern Web Development | Build production-grade fintech applications using modern frameworks. Real products need proper engineering. |
| CS 205 | DevOps & Monitoring | Deploy and monitor financial systems. Trading tools and market data pipelines run 24/7. |
| FIN 103 | Financial Accounting | Read financial statements. You'll build systems that analyze these and understand what they mean. |
| FIN 201 | Corporate Finance | Capital structure, valuation, M&A. Build systems that analyze and automate financial decisions. |
| PROJECT 202 | Build FinTech Tool - 2 | Build a backtesting engine, real-time market dashboard, or crypto tracker. Proper deployment, monitoring, real market data. Keep it running for 60 days. |
Note: Ideally, students should be able to crack Fintech Developer or Junior Quant Analyst roles after both terms, depending on their experience.