Checking date: 15/05/2024

Course: 2024/2025

Data Analysis
Master in Financial Sector Technologies: FinTech (Plan: 461 - Estudio: 313)


Department assigned to the subject: Computer Science and Engineering Department

Type: Electives
ECTS Credits: 3.0 ECTS


Requirements (Subjects that are assumed to be known)
We recommend to have passed the subject of Introduction to Financial Markets
The objective of this course is to provide the student with data analytics skills in areas related to the financial sector. Specifically, the student will be able to use data analysis tools (statistical, visual) and machine learning (classification, regression, etc.) to, for example, make predictive analysis of time series.
Skills and learning outcomes
Description of contents: programme
DATA ANALYSIS 1. Introduction to the Analysis of Financial Data 2. Exploratory analysis and visualization tools 3. Financial data cleaning and transformation 4. Supervised predictive models 5. Model evaluation and backtesting in finance 6. Unsupervised models and other learning paradigms
Learning activities and methodology
The course follows the Master idea complementing on-site classes with e-learning activities. These activities are summarized as follows: AF1: Lectures: Theoretical presentations accompanied by digital presentations AF3: Theoretical and practical classes: Combination of lectures accompanied by the resolution of practical exercises AF4: Laboratory practices: Guided practices in computer rooms AF5: Tutorials: Personalized on-site or remote tutorials AF2: E-learning activities: Remote activities that the student develops independently. These activities include: Participation in forums, viewing pre-recorded contents, and guided exercises AF7: Individual work of students: Individual student activities that complement the other activities (both classroom and non-classroom) and exam preparation Teaching methodology MD1: Teachers give lectures with support of digital presentations, in which they develop the subject. MD3: Practical cases that are solved with a guided provided by the teacher. MD5: Individual or group preparation of practices and reports MD6: Specific e-learning activities including visualization pre-recorded content, self-review activities, participation in forums, etc.
Assessment System
  • % end-of-term-examination 60
  • % of continuous assessment (assigments, laboratory, practicals...) 40

Calendar of Continuous assessment

Basic Bibliography
  • Luis Torgo. Data Mining with R: Learning with Case Studies, Second Edition. CRC Press. 2017

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