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course plan

The 24-week study plan

Parallel tracks: finance and strategy in the morning, statistics and data in the afternoon. Each week assumes 20–25 hours of study — adjust the pace to your situation.

  1. Wk 1–2

    Foundations

    Finance: accounting, three statements, key ratios, DuPont. Stats: probability, distributions, expected value, variance, CLT. Tools: set up Python (Jupyter, pandas, numpy).

  2. Wk 3–4

    Time value + inference

    Finance: PV, FV, NPV, IRR, annuities, perpetuities. Stats: hypothesis testing, confidence intervals, t-tests, chi-square. Tools: Excel financial functions, scipy.stats.

  3. Wk 5–7

    Corporate finance + regression

    Finance: capital budgeting, WACC, CAPM, capital structure (M&M, trade-off, pecking order). Stats: simple and multiple regression, OLS, diagnostics, R². Strategy: Five Forces, PESTEL. Tools: statsmodels OLS, Excel modelling.

  4. Wk 8–9

    Markets + econometrics

    Finance: fixed income, equity valuation, market efficiency. Stats: logistic regression, endogeneity, instrumental variables. Strategy: RBV, VRIO, dynamic capabilities, value chain. Tools: SQL fundamentals and window functions.

  5. Wk 10–12

    Portfolio theory + time series

    Finance: Markowitz, efficient frontier, CAPM regression, Fama–French. Stats: stationarity, ARIMA, GARCH, volatility. Strategy: generic strategies, Ansoff, BCG. Tools: portfolio optimisation in Python, arch package.

  6. Wk 13–14

    Derivatives + ML foundations

    Finance: options, binomial model, Black–Scholes, Greeks, hedging. Stats: decision trees, random forests, cross-validation, ridge and lasso. Tools: scikit-learn pipelines, feature engineering.

  7. Wk 15–17

    Valuation + advanced ML

    Finance: build a full DCF from scratch, relative valuation, precedent transactions. Stats: gradient boosting, PCA, clustering, model evaluation. Strategy: Blue Ocean, game theory, M&A strategy. Tools: full DCF in Excel, XGBoost.

  8. Wk 18–19

    Risk + Bayesian / deep learning

    Finance: VaR, ES/CVaR, stress testing, credit risk, Basel. Stats: Bayesian methods, deep learning basics, NLP on financial text. Strategy: Balanced Scorecard, EVA, value driver trees. Tools: Monte Carlo in Python.

  9. Wk 20–21

    Integration + visualisation

    Finance: capital allocation as strategy, financial strategy and competitive position. Stats: visualisation principles, dashboards, communicating results. Tools: matplotlib, seaborn, chart discipline.

  10. Wk 22–24

    Portfolio projects + interview prep

    Ship the projects below, rehearse case interviews end to end, and drill the mental arithmetic until the numbers stop being the bottleneck.

Portfolio projects

Four artefacts that prove the curriculum rather than describe it.

Full DCF valuation

Pick a listed company, build a three-statement model and DCF from filings, run sensitivity on WACC and terminal growth, and write the strategic thesis behind the assumptions.

Credit risk model

Use a public dataset (Lending Club or corporate defaults). Build a logistic regression and a gradient boosting classifier, compare AUC and precision-recall, and analyse feature importance in a Python notebook.

Portfolio optimiser

Download returns for 20+ OMX Stockholm stocks. Implement Markowitz optimisation, the efficient frontier and the minimum-variance portfolio, then add a Fama–French decomposition and interactive charts.

Strategic + financial analysis

Pick an industry such as Swedish fintech. Complete a Five Forces analysis, a value-chain comparison of three companies and a ROIC decomposition showing which has the strongest competitive position.

Source curricula

Lund University (LUSEM)

Finance Master's (Foundations of Finance, Financial Econometrics & ML, Theory of Corporate Finance, Empirical Finance NEKN82, Financial Valuation & Risk Management), Data Analytics & Business Economics (DABN13/14/19/20/22), Statistics (STAG33, STAH12, STAH14, STAE02/03, STAN47–53), International Strategic Management.

KTH Royal Institute of Technology

Banking & Finance (TBAFM): Corporate Finance & Markets I (ME2721), Financial Mathematics (SF2701), Portfolio Theory & Risk Management (SF2942), Financial Derivatives (SF2975), Risk Management (SF2980), Time Series Analysis (SF2943). Industrial Engineering & Management: Management & Strategy (ME2718).

CFA Institute

CFA Level I curriculum: ethics, quantitative methods, economics, financial statement analysis, corporate issuers, equity, fixed income, derivatives, alternatives and portfolio management.

Copenhagen Business School (CBS)

Corporate Strategy and Strategic Management (Robert M. Grant, Contemporary Strategy Analysis, plus journal articles and cases): corporate scope, firm boundaries, diversification, global strategy, innovation, governance and proposition-style strategic reasoning. Time Series for Economics, Business and Finance: stationarity, ARIMA, ADL, forecasting, VAR, cointegration and ECM, ARCH/GARCH, worked in R.

Berk & DeMarzo (2023)

Corporate Finance, 6th Edition (Global), Pearson. The standard reference for investment decision rules, capital budgeting, cost of capital, capital structure, payout policy, options, real options, risk management, M&A and business valuation.

Graham (2022)

John R. Graham, 'Presidential Address: Corporate Finance and Reality', The Journal of Finance. A reality check on textbook models, emphasising taxes, behavioural biases, financial frictions and institutional variation across firms and countries.