PART A: Numerical treatment of Data

Basic matrix theory. Unitary, Hermitian, positive definite matrices. Elementary matrices (Gauss, Householder)

Numerical solution of linear systems: coefficient matrices in special form (unitary, triangular etc)

Numerical solution of linear systems: Gaussian elimination, pivoting, QR factorization

Iterative solvers: Jacobi, Gauss-Seidel
Evaluation of a polynomial at a point.

Interpolation. Least squares

PARTE B:

Statistical treatment of Data

Introduction to probability and to the statistical methodology. Introduction to the R freeware
statistical software.

Type of statistical variables and their graphical representations. Absolute and relative frequencies.

Measures of central tendency (mean, mode and median). Measure of dispersion (range, variance,
standard deviation, coefficient of variation, quantiles, interquantile range, notion of homogeneity).

Measures of shape (symmetry). Bivariate descriptive statistics: two-way tables, marginals and

joint bivariate distributions (discrete case). Joint graphical representations of two random variables.

Covariance and correlation coefficient. Linear regression: simple and multivariate. Discrete and
continuous random variables (Bernoulli and Gaussian).