Big Data Seminar

Credits
2
Types
Compulsory
Requirements
This subject has not requirements
Department
ESSI
The students will be introduced to recent trends in Big Data. Seminars will be lectured by guest speakers, who will present business cases, research topics, internships and master's thesis subjects. Also, the three specialisations will be presented and discussed with the students within the seminars umbrella. Students will also perform a state-of-the art research in one topic, which will be presented and jointly evaluated by all partners in the mandatory eBISS summer school. Participation in the summer school is also included in this course.

Teachers

Person in charge

  • Oscar Romero Moral ( )

Objectives

  1. Read and understand scientific papers
    Related competences:
  2. Develop critical thinking when assessing scientific papers
    Related competences:
  3. Write and explain a state-of-the-art in a rigorous manner
    Related competences:
  4. Elaborate on recent trends in Big Data
    Related competences:

Contents

  1. Seminars: the seminars will present advanced topics related to Big Data in the industrial and research settings
    Seminars will take place during the semester. Industrial and academic practitioners will provide insights on hot topics not covered by the semester.

Activities

Activity Evaluation act


Seminars

The student attends the seminars and participate actively.
Objectives: 1 2 3 4
Contents:
Theory
0h
Problems
11h
Laboratory
0h
Guided learning
0h
Autonomous learning
0h

Specialisation presentations

The student is expected to attend and listen, since they will be asked to choose an specialisation after the presentations
Objectives: 4
Theory
0h
Problems
3h
Laboratory
0h
Guided learning
0h
Autonomous learning
0h

Report on the state-of-the-art of a research field

The student must choose a research field from a list provided during the course and generate a rigorous state-of-the-art
Objectives: 1 2 4
Week: 18
Type: final exam
Theory
0h
Problems
0h
Laboratory
0h
Guided learning
0h
Autonomous learning
36h

Teaching methodology

This course is based on seminars. During the semester the student will be mandatorily attend the course seminars and learn about new topics or practices related with Big Data.

To show the comprehension of one of these areas, the student must generate a state-of-the-art in group of 2-3 people.

Evaluation methodology

Final Mark = 40% A + 40% R + 20% RPr where,

A = Attendance to the seminars,
R = The mark obtained on the written state-of-the-art report,
RPr = Face-to-face presentation of the report

Bibliography

Basic: