Sistemas de Información (19BBA11004)
General information
Type: |
OB |
Curs: |
2,3 |
Period: |
S semester |
ECTS Credits: |
4 ECTS |
Teaching Staff:
| Group |
Teacher |
Department |
Language |
| Sec: A |
German Sánchez Hernández |
Operaciones, Innovación y Data Sciences |
CAT |
| Group |
Teacher |
Department |
Language |
| Sec: A |
David Roche Valles |
Operaciones, Innovación y Data Sciences |
CAT |
| Group |
Teacher |
Department |
Language |
| Sec: B |
Albert Armisen Morell |
Operaciones, Innovación y Data Sciences |
ESP |
| Group |
Teacher |
Department |
Language |
| Sec: B |
German Sánchez Hernández |
Operaciones, Innovación y Data Sciences |
ESP |
| Group |
Teacher |
Department |
Language |
| Sec: C |
German Sánchez Hernández |
Operaciones, Innovación y Data Sciences |
CAT |
| Group |
Teacher |
Department |
Language |
| Sec: C |
Albert Armisen Morell |
Operaciones, Innovación y Data Sciences |
CAT |
Prerequisites
Information Systems Management I
Workload distribution
Workload distribution:
Lectures: 18 hours
Participatory classes: 20 hours
Independent study: 56 hours
Tutorials: 6 hours
COURSE CONTRIBUTION TO PROGRAM
The availability of data is transforming society and organizations. Big Data and Artificial Intelligence provide a set of frameworks, theory, and methods to take the most advantage of it. This course contributes to the programme in the following way:
1) Students will acquire the basic necessary knowledge to understand the role of artificial intelligence and big data in the organization.
2) Students will develop the necessary skills that enable them to have a comprehensive and general overview of the methods of artificial intelligence and big data.
Course Learning Objectives
- Understand the information system analysis process
- Learn about and apply methods and techniques of systems analysis
- Acquire experience in information systems analysis
- Develop a knowledge and understanding of the different types of information systems in organisations
Competences
| 4. Conveying information and/or knowledge |
| 2. Application of knowledge to achieve results |
| 18. Teamwork and collaboration |
Relation between Activities and Competences
|
4 |
2 |
18 |
| Final exam |
|
|
|
| Mini-tests |
|
|
|
| Team assignments and presentation |
|
|
|
CONTENT
1. Data Sciences in Business * Introduction
* Cloud Computing
* Data Wrangling
* Data Visualization
* No-SQL
|
2. Introduction to Articial Intelligence * Introduction
* Machine Learning
* Content Analysis
* Recommender System
|
Methodology
The methodology applied in this course combines lectures, problem-solving sessions, case study discussions and team projects. For the final project, students will be able to take advantage of monitoring sessions offered throughout the course.
ASSESSMENT
ASSESSMENT BREAKDOWN
| Description |
% |
| Final exam |
40 |
| Mini-tests |
30 |
| Team assignments and presentation |
30 |
Assessment criteria
Participation in class and individual assignments: Class participation will be assessed on 1) the level of students' proactive engagement throughout the sessions, and 2) the quality of their contributions.
The final project presentation will be assessed on: 1) the clarity of the presentation, 2) the structure of the presentation, 3) time management and 4) the ability to answer questions raised by faculty.
The first competence will be assessed through tests and the final exam. In order to pass this course students must obtain at least a 4.5 (out of 10) on the final exam.
The second competence will be assessed on the basis of the presentations students have to give during the course in the group exercises and students' class participation. With regard to the group exercises, they will be presented in the participatory classes.
The third competence will be assessed on the basis of the projects that the students carry out in teams during the course. To pass the course the mark for the final project should be 5 (out of 10) or higher.
Bibliography
Short bibliography:
Provost, F.; Fawxett, T. (2018).: Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking. Kindle Edition
Agrawal, A.; Gans, J.; Goldfarb, A. (2018): Prediction Machines: The Simple Economics of Artificial Intelligence, Harvard Business Review Press
Timetable and sections
| Group |
Teacher |
Department |
| Sec: A |
German Sánchez Hernández |
Operaciones, Innovación y Data Sciences |
Timetable Sec: A
| Group |
Teacher |
Department |
| Sec: A |
David Roche Valles |
Operaciones, Innovación y Data Sciences |
Timetable Sec: A
| Group |
Teacher |
Department |
| Sec: B |
Albert Armisen Morell |
Operaciones, Innovación y Data Sciences |
Timetable Sec: B
| Group |
Teacher |
Department |
| Sec: B |
German Sánchez Hernández |
Operaciones, Innovación y Data Sciences |
Timetable Sec: B
| Group |
Teacher |
Department |
| Sec: C |
German Sánchez Hernández |
Operaciones, Innovación y Data Sciences |
Timetable Sec: C
| Group |
Teacher |
Department |
| Sec: C |
Albert Armisen Morell |
Operaciones, Innovación y Data Sciences |
Timetable Sec: C
