.

Teaching Plan

The teaching plan has been structured so that in order to successfully finish their studies and obtain their respective degrees, the students will need to obtain 60 ECTS credits, distributed in the following way:

- 36 credits, relative to compulsory courses,

- 12 credits relative to optional courses,

- 12 credits associated with the Master's final project.

The language used throughout the education process is English.

 

Subject and Modules

The subjects (S) that comprise the curriculum are shown below with the courses that constitute them. Additionally, links to module description and learning guides will be available.

 

ACADEMIC COURSE 2019/20 

 

S1. FUNDAMENTALS OF RESEARCH

  • M1: Research methodology (description, learning guide) (1.5 ECTS, 2nd Semester)   

S2. DATA PROCESSING AND INFRASTRUCTURE

Mandatory

  • M1: Data Visualization (description, learning guide) (3 ECTS, 1st Semester)    
  • M2: Big data (description, learning guide) (3 ECTS, 1st Semester) 
  • M3: Cloud computing and big data ecosystems (description, learning guide) (4.5 ECTS, 1st Semester) 
  • M4: Data processes (description, learning guide) (4.5 ECTS, 1st Semester) 

Optional

  • O1: Programming for data processing (description, learning guide) (4.5 ECTS, 2nd Semester)
  • O2: Data Science Seminars (description, learning guide) (4.5 ECTS, 2nd Semester)

S3. DATA ANALYSIS AND EXPLOITATION

Mandatory

Optional

  • O3: Bayesian Networks (3 ECTS, 2nd Semester)
  • O4: Time Series Data Mining (3 ECTS, learning guide, 2nd Semester)
  • O5: Graph Analysis and social networks (3 ECTS, 2nd Semester)
  • O6: Image processing, analysis and classification (4.5 ECTS, 2nd Semester)
  • O7: Information retrieval, extraction and integration (4.5 ECTS, 2nd Semester)
  • O8: Intelligent Systems (4.5 ECTS, 1st Semester)

S4. ETHIC/LEGAL/SOCIAL ASPECTS IN DATA SCIENCE

Mandatory

  • M4: Ethic/legal/social aspects in Data Science (description, learning guide) (3 ECTS, 2nd Semester)   

S5. MASTER FINAL PROJECT

 

 

English