Checking date: 28/06/2021

Course: 2021/2022

Numerical Methods for Data Science
Study: Master in Statistics for Data Science (345)

Coordinating teacher: NIÑO MORA, JOSE

Department assigned to the subject: Department of Statistics

Type: Compulsory
ECTS Credits: 3.0 ECTS


The course sets out to develop the following competences: 1) Capacity to formulate data-based analytics models for optimal decision making (operations research) in diverse applications; 2) capacity to analyze such models based on an understanding of their properties; 3) capacity to obtain numerical solutions for such models through computer software; 4) capacity to interpret the numerical solutions obtained in terms of optimal decisions.
Skills and learning outcomes
Description of contents: programme
1. Linear optimization models. 1.1. Introduction: decision optimization, analytics and operations research; formulations. 1.2. Graphical solution; sensitivity analysis; software-based solution. 1.3. Duality; economic interpretation; optimality conditions; sensitivity analysis. 1.4. Applications. 2. Discrete optimization models. 2.1. Formulations; graphical solution; linear relaxations; optimality gap. 2.2. The branch and bound method; strengthening formulations; valid inequalities; software-based solution. 2.3. Applications.
Learning activities and methodology
Theoretical-practical classes with web-based supporting material. Computational sessions with numerical software. The teaching methodology will have an eminently practical approach, being based on the formulation and solution of decision optimization models from diverse application areas. Weekly individual tutorials will be scheduled.
Assessment System
  • % end-of-term-examination 0
  • % of continuous assessment (assigments, laboratory, practicals...) 100
Calendar of Continuous assessment
Basic Bibliography
  • F.S. Hillier, G.J. Lieberman. Introduction to Operations Research. McGraw-Hill.
  • H.A. Taha. Operations Research: An Introduction. Prentice Hall.
  • J. Niño Mora. Introducción a la optimización de decisiones. Pirámide. 2021
Recursos electrónicosElectronic Resources *
Additional Bibliography
  • Robert J. Vanderbei. Linear Programming Foundations and Extensions. Springer . 2020
(*) Access to some electronic resources may be restricted to members of the university community and require validation through Campus Global. If you try to connect from outside of the University you will need to set up a VPN

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