Excerpt from course description

Data Science for Finance


This course covers the fundamental statistical tools used by quantitative analysts with a focus on forecasting financial series. Several key methods used in modern data science (also known as “statistical inference” and “machine learning”) are presented and applied to financial data.

Course content

  • Introduction to statistical learning. Features of financial data.
  • K-nearest neighbour. The bias-variance trade-off.
  • Least squares and linear projections. Weighted least squares.
  • Modelling nonlinearities with interaction terms and local regressions.
  • Ridge regression.
  • Cross-validation.
  • Modelling nonlinearities with regression splines.
  • Introduction to time-varying models.
  • Maximum likelihood (ML).
  • Penalized ML. Weighted ML. Local ML. Splines and ML.
  • Bootstrap and bagging.
  • Introduction to Bayesian inference.
  • Approximate Bayesian inference. Markov Chain Monte Carlo.
  • State space models. Kalman filter. Introduction to the particle filter.


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