🎓 Undergraduate Finance Study Roadmap This roadmap is structured to provide the core quantitative and analytical foundation necessary for a career in finance, especially complementing your strong background in Data Engineering.
I. The Essential Pillars (Prerequisites) These subjects are the fundamental language of business and quantitative analysis. Financial Accounting is non-negotiable.
Subject
Core Concepts
Recommended Book
Top Video Resource
Financial Accounting
The three financial statements (Income Statement, Balance Sheet, Cash Flow), accrual accounting, reading annual reports.
Intermediate Accounting by Kieso, Weygandt, & Warfield (Industry Standard)
Edspira (YouTube): Clear, concise lessons on core accounting concepts.
Business Statistics
Probability, distributions, hypothesis testing, simple and multiple Linear Regression.
Statistics for Business and Economics by Paul Newbold
Khan Academy (Statistics): Excellent for foundational math and probability.
Microeconomics
Supply/Demand, Pricing, Market Efficiency, Elasticity, Consumer/Producer Theory.
Principles of Economics by N. Gregory Mankiw
MIT OpenCourseWare (14.01SC Principles of Microeconomics): Comprehensive and rigorous.
Python for Finance
Using Python for data manipulation, financial analysis, and basic modeling.
Python for Finance by Yves Hilpisch
Quantopian Lectures (Archive): Focuses on quantitative finance and libraries like pandas and numpy.
II. Core Finance Disciplines These subjects form the heart of the finance degree, covering valuation and management.
- Corporate Finance (Financial Management) Focuses on company-level decision-making (raising and spending capital).
Core Concept
Recommended Book
Top Video Resource
Time Value of Money (TVM)
Corporate Finance by Ross, Westerfield, and Jordan (RWJ) — A highly practical, intuitive approach.
Aswath Damodaran (NYU Stern) - Corporate Finance Playlist: The best conceptual lecturer in the field.
Valuation & Budgeting
Principles of Corporate Finance by Brealey, Myers, and Allen (BMA) — More theoretical/academic.
Professor's Channel (e.g., CFI/Investopedia Videos): For step-by-step calculations of NPV, IRR, and WACC.
Cost of Capital (WACC)
- Investments and Portfolio Management Focuses on the investor's perspective (analyzing securities and managing risk).
Core Concept
Recommended Book
Top Video Resource
Security Valuation
Investments by Bodie, Kane, and Marcus (BKM) — The industry standard for portfolio theory.
Yale OpenCourseWare - Financial Markets (Robert Shiller): Excellent for understanding market dynamics and behavioral finance.
Risk & Return
A Random Walk Down Wall Street by Burton Malkiel (For market philosophy/behavioral finance).
CFA Institute Curriculum (Free Resources): Detailed, professional-level lectures on Portfolio Management.
Portfolio Theory (CAPM)
III. Advanced & Specialized Subjects These are often upper-division electives that require a strong math background.
| Subject | Core Concepts | Recommended Book | Top Video Resource | | :--- | :--- | :--- | | Fixed Income Analysis | Bond pricing, duration, convexity, yield curve analysis. | The Handbook of Fixed Income Securities by Frank J. Fabozzi | Bloomberg/CFA Videos: Specific technical explanations of bond metrics (e.g., key rate duration). | | Derivatives & Options | Valuation of futures, forwards, options (Black-Scholes Model), and swaps; hedging strategies. | Options, Futures, and Other Derivatives by John C. Hull — The definitive textbook. | Dr. Gordon's Lectures (YouTube): Clear, quantitative breakdowns of the Black-Scholes formula and option Greeks. | | Financial Modeling | Building integrated 3-statement models, Discounted Cash Flow (DCF) models, and Sensitivity Analysis in Excel/Python. | Financial Modeling by Simon Benninga | Wall Street Prep / BIWS: Premium courses are standard, but seek out free tutorials on building 3-statement models in Excel. | | Quantitative Finance | Time series analysis, Stochastic Calculus basics, applying machine learning models to trading. | Machine Learning for Trading by Stefan Jansen | QuantStack/PyData Conferences: Look for talks focusing on using scikit-learn and PyTorch for financial forecasting. |