Checking date: 28/04/2022


Course: 2022/2023

Optimization and Analytics
(16501)
Study: Bachelor in Data Science and Engineering (350)


Coordinating teacher: NOGALES MARTIN, FCO. JAVIER

Department assigned to the subject: Statistics Department

Type: Compulsory
ECTS Credits: 6.0 ECTS

Course:
Semester:




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
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 course syllabus may change due academic events or other reasons.


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