12 phases
Statistics
Probability through machine learning and visualisation.
From Lund LUSEM's Statistics department (STAA/STAH/STAN), Data Analytics & Business Economics (DABN), KTH Time Series Analysis (SF2943) and Financial Econometrics (NEKN82).
Phase 1
Describing Data
Populations and samples, averages, spread, shape and what a number can honestly claim.
Phase 2
Probability Foundations
Random variables, Bayes, key distributions, CLT and LLN.
Phase 3
Statistics of Returns & Risk
Return definitions, log returns, volatility scaling, fat tails and the risk numbers built on them.
Phase 4
Correlation, Diversification & Beta
Covariance, correlation, portfolio variance and the regression that produces beta.
Phase 5
Statistical Inference
Hypothesis testing, p-values, errors, power and confidence intervals.
Phase 6
Regression & Econometrics
OLS, R², diagnostics, endogeneity and logistic regression.
Phase 7
Time Series
Stationarity, ARIMA, GARCH and out-of-sample forecasting.
Phase 8
Applied Time Series for Business & Finance
ARIMA, ADL, forecasting, VAR, cointegration and GARCH — plus how to run the whole project in R.
Phase 9
Machine Learning
Trees, ensembles, regularisation, clustering and cross-validation.
Phase 10
Advanced Methods
Bayesian statistics, deep learning, NLP and high-dimensional data.
Phase 11
Tools & Programming
Python, R, SQL and Excel — the stack employers screen on.
Phase 12
Data Visualization
Charts as analysis: match the type, cut the junk, label directly.