The path

Learn.

A curated, opinionated path into AI and quantitative finance. Not a syllabus, not a bootcamp. Just the resources worth your time, in an order that makes sense, updated as good ones surface.

Curated, not generic. These are the specific free resources I would actually point someone to, in an order that makes sense. Mostly creators who teach the real work, in plain English: CampusX, Krish Naik, AnalystPrep, and a few others. Updated as better ones surface.
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Foundation

Everyone starts here
Foundation

Math and statistics

Probability, linear algebra, calculus, and statistics are the grammar of every model in finance and AI. Skip these and you are guessing.

Resources
Foundation

Python for data

Python is the working language of AI, quant, and data science. NumPy and pandas are load-bearing; learn them before libraries abstract them away.

Resources

Corporate finance and valuation

FP&A, valuation, and the language of corporate finance sit under most finance roles. Get the intuition before the spreadsheets.

Resources
  • Valuation, the full course Free YouTube · Aswath Damodaran (NYU Stern), the definitive free lectures on valuation and corporate finance
  • Damodaran channel Free YouTube · DCF, cost of capital, corporate finance, and FP&A intuition, all free
Three tracks branch from the foundation. Pick one, all three, or follow your curiosity.

Track A: Quant / financial engineering

Stochastic everything
Quant

Stochastic calculus

Brownian motion and Ito's lemma are the foundation of every derivative pricing model. Dense, but unavoidable if you are serious about this track.

Resources
Quant

Derivatives pricing

Black-Scholes is not the end; it is the beginning. Understanding why it works and where it breaks is what separates users from practitioners.

Resources
Quant

Fixed income and rates

Duration, convexity, and term-structure models underlie a large share of real risk capital. Bonds are less exciting and more consequential than options.

Resources
Quant

Market, credit, and model risk

VaR, expected shortfall, credit spreads, and model validation are the professional language of risk. Know the distinction between types before mixing them up in a pitch.

Resources
Quant

Time series analysis

ARIMA, cointegration, and volatility models are the workhorses of financial modeling. Most "AI predictions" in finance are really time series econometrics with better PR.

Resources

Track B: AI / machine learning

Models that learn
AI / ML

ML fundamentals

Gradient descent, overfitting, bias-variance tradeoff, cross-validation. The ideas that every other concept in this track depends on.

Resources
AI / ML

Deep learning

Neural networks, backpropagation, CNNs, and the architecture decisions that separate toy from production. Understanding this layer lets you reason about what LLMs actually do.

Resources
AI / ML

NLP and LLMs

Transformers, attention, and how language models work from the inside. The difference between using an API and understanding the architecture is the difference between a user and a builder.

Resources
AI / ML

AI agents and tool use

Agents are models that act in the world: they call tools, maintain state, and loop until a task completes. This is where the capability ceiling sits right now.

Resources

Track C: Data science in finance

Applied, production-minded
Data Science

SQL and data wrangling

Every finance data science job starts with SQL and messy datasets. Master joins, window functions, and pandas before touching a model.

Resources
Data Science

ML applied to finance

Credit scoring, fraud detection, return prediction, and portfolio construction with ML. Finance requires calibrated uncertainty and explainability that generic ML courses skip.

Resources
Data Science

Forecasting in practice

Revenue forecasting, demand planning, and cash flow projection with modern tooling. The gap between a good model and a trusted model is calibration and honest uncertainty bounds.

Resources
Data Science

MLOps basics

Model deployment, monitoring, versioning, and retraining loops. In finance, a model that cannot be audited or redeployed quickly is a liability. This is the production gap most DS courses ignore.

Resources
Data Science

Visualization and communication

A finance audience does not read model cards. Communicating uncertainty, trends, and model outputs clearly is a professional skill as real as the modeling itself.

Resources

Where the tracks meet

AI in finance

This is where the quant foundation, the ML toolset, and the data science workflow converge. Sarthak's focus: end-to-end AI in finance, with human-in-the-loop validation and governance that can survive regulatory scrutiny.

Convergence

Agentic AI in finance

Agents that read filings, run models, reconcile data, and escalate exceptions are real and running in production. The architecture decisions that make them trustworthy with money are not obvious.

Resources
Convergence

Model risk for AI systems

SR 11-7 was written for statistical models. Applying its validation logic to neural networks and agents requires new thinking. This is the most underserved skill gap in financial AI right now.

Resources
Convergence

Human-in-the-loop design

Automation that cannot hand off cleanly to a human is a liability, not an asset. HITL is not a concession; it is an architecture choice that keeps the system auditable and recoverable.

Resources
Convergence

AI governance and compliance

The regulatory environment for AI in financial services is moving fast. Model inventories, audit trails, explainability requirements, and the EU AI Act's high-risk classification for credit systems are shaping what you can build and how.

Resources

Resources listed are free or free-to-audit unless noted otherwise. None are sponsored. All views are my own and not those of my employer. To suggest a resource, reach out via email.