Fundamentals of numerical analysis: calculation errors; functions (calculation of function value; approximation, interpolation and extrapolation of function); Taylor Series; matrix algebra; eigenvalues and eigenvectors; random variables; probability and statistics. The system of linear algebraic equations: theorems, transformations of equivalence; permutation matrices; solving solutions (Gauss's elimination process, triangular decomposition) and optimal equation ordering (Quasi-optimal methods and Tunney optimal schemes). Space matrices techniques: static and dynamic storage schemes. Matrix inversion: classical methods and matrix inversion lemmas. The system of nonlinear algebraic equations: approximate solution; iterative solution corrections; bracketing a root and combined methods; basic and modified Newton-Raphson methods; basic and accelerated Gauss-Seidel methods. Practical problems: numerical stability and stability of (non)linear systems; Ill-conditioning. Solving of ordinary differential equations: one-stage and multi-stage methods: Runge-Kutta, Euler, predictor-corrector. Regression analysis: data model; correlation; residual; weighted Least Squares; linear regression; sensitivity analysis and model quality assessment. Cluster Analysis: type of clusters, clustering algorithms. Graph Theory: Power System Network Matrices (incidence matrices; Network equations and Network matrices; Bus/Branch Impedance/Admittance Matrix). Application in power systems: data modelling; grouping of "similar" time-series; power flow, short circuit, non-linear weighted Least Squares State Estimation, linear and non-linear programming; optimization of the distribution network (radial structure; Volt-Var optimization; etc.); etc.