Course Unit Page

Academic Year 2018/2019

Learning outcomes

At the end of the course the student will acquire the basic knowledge of computational methods necessary for analysing biological data in the omic era.

Course contents

Prerequisites: Basic calculus.

-) Basic concept of probability, joint probability, conditional probability.
-) Bayes' theorem.
-) Discrete probability distributions: Binomial, Poisson
-) Continuous probability distributions: Normal, Boltzmann, Student, Chi-square, Gumbel (Extreme value)
-) Mean, median, mode, variance. Biased and unbiased computation
-) p-value and E-value
-) Tests for statistical significance: Student, Fisher, Chi-square
-) Vectors and matrices: basic definitions and operations
-) Eigenvalues and Eigenvectors: applications to Principal Component Analysis


Slides of the lectures
Reviews and web sites provided during the lectures

Teaching methods

Personal computer and overhead projector

Assessment methods

Written tests and a final examination.

Teaching tools

Lectures and practical sessions

Office hours

See the website of Pier Luigi Martelli