Ficha

Versión en español

Course: 2024/2025

Optimization and Analytics

(16501)

Bachelor in Data Science and Engineering (Plan: 392 - Estudio: 350)

Coordinating teacher: NOGALES MARTIN, FRANCISCO JAVIER

Department assigned to the subject: Statistics Department

Type: Compulsory

ECTS Credits: 6.0 ECTS

Course: 3º

Semester: 1º

Requirements (Subjects that are assumed to be known)

Basic knowledge of mathematics and statistics

Objectives

1. Know how to model and implement optimization methods and simulation techniques in decision-making problems in business. 2. Learn about the conditions to be satisfied by solutions of optimization problems. 3. Learn to use tools of modern optimization and simulation techniques in an efficient way.

Skills and learning outcomes

Link to document

Description of contents: programme

1. Introduction: process modeling in decision-making problems 2. Linear Models: modeling, applications, Simplex method 3. Discrete Models: applications, binary variables, logic constraints, algorithms 4. Non-linear Models: applications, optimality conditions, algorithms for machine learning 5. Case Studies

Learning activities and methodology

Theory (3 ECTS), Practice (3 ECTS). 50% lectures with teaching materials available on the Web. The other 50% practical sessions (computer labs).

Assessment System

% end-of-term-examination 50
% of continuous assessment (assigments, laboratory, practicals...) 50

Calendar of Continuous assessment

The assessment will be made by weighting the continuous evaluation (50%) and the final exam (50%), with a minimum grade of 5 points over 10 in each assessment activity (both the continuous ev. and the final exam).

Extraordinary call: regulations

The course syllabus may change due academic events or other reasons.

More information: Aula Global