Checking date: 05/09/2019

Versión en español

Course: 2019/2020

Mathematics for data analysis
(17228)
Master in Big Data Analytics (Plan: 352 - Estudio: 322)
EPI


Coordinating teacher: RASCON DIAZ, CARLOS

Department assigned to the subject: Mathematics Department

Type: Compulsory
ECTS Credits: 3.0 ECTS

Course: 1º
Semester: 1º



Requirements (Subjects that are assumed to be known)
Proficiency in high school mathematics
Objectives
While there are many applied mathematics techniques and concepts that are useful (and used) in the Big Data analysis context, this course focus on the basics of those based on linear algebra, as it underlies many of the most importants applications and algorithms. Thus, the course is intended to understand the mathematical ideas behind those applications and algorithms (usually implemented in black-box software) so practitioners have a deeper knowledge of the results arising from them, allowing for a better interpretation.
Description of contents: programme
1. Linear Systems 2. Vectors 3. Matrices 4. Diagonalization 5. Orthogonality 6. Symmetric Matrices
Learning activities and methodology
Theoretical classes (lectures) Practical problems that students must solve individually as homework Tutorials
Assessment System
  • % end-of-term-examination 100
  • % of continuous assessment (assigments, laboratory, practicals...) 0

Basic Bibliography
  • David C. Lay, Steven R. Lay, Judi J. McDonald. Linear Algebra and Its Applications. Pearson; 5 edition. 2016
Recursos electrónicosElectronic Resources *
Additional Bibliography
  • W. Keith Nicholson. Linear Algebra with Applications. McGraw-Hill, 6th edition. 2009
(*) 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.