Checking date: 04/06/2021


Course: 2022/2023

Analysis techniques for Big Data
(18195)
Study: Bachelor in Computer Science and Engineering (218)


Coordinating teacher: ONORATI , TERESA

Department assigned to the subject: Department of Computer Science and Engineering

Type: Electives
ECTS Credits: 3.0 ECTS

Course:
Semester:




Objectives
LEARNING OUTCOMES R1 Knowledge and understanding: To have basic knowledge and understanding of the scientific and technological foundations of Computer Engineering and specific knowledge of Computer Science, Computer Engineering, and Information Systems. R4 Research and Innovation: To be able to use appropriate methods to conduct research and make innovative contributions in computer engineering. R5 Engineering Applications: Graduates will be able to apply their knowledge and understanding to solve problems, conduct research, and design devices or processes in the field of Computer Engineering according to criteria of cost, quality, safety, efficiency, environmental friendliness, and ethical implications. These skills include knowledge, use, and limitations of computer systems, process engineering, computer architectures, computational models, equipment, practical work, technical literature, and information sources.
Skills and learning outcomes
Description of contents: programme
1. Introduction to Python. (Tutorial) 2. Data preparation (Tutorial. NumPy and Pandas) 3. Information Visualization (Visual Analytics) in Python 4. Supervised approaches for classification and regression: linear regression (least-squares, ridge, lasso), logistic regression, support vector machines, ensembles of trees (random forests) 5. Unsupervised approaches: Clustering 6. Deep learning: Artificial Neural Networks, Convolutional Neural Networks with TensorFlow 7. Evaluation and model selection
Learning activities and methodology
* Lectures: 1 ECTS. They aim to achieve the specific cognitive competencies of the subject and the transversal competencies of analysis and abstraction. * Practical classes: 1 ECTS. They aim to develop the specific instrumental competencies and the transversal competencies problem solving and application of knowledge. * Case study: 0,5 ECTS. Started during the practical classes and completed outside of them, it aims to complete and integrate the development of all specific and transversal competencies with the design and implementation of a case study through group work. * Tutorials: TUTORIALS. Individual or group tutoring sessions organized by the teacher for the students. * Final exam: 0,5 ECTS. It aims to influence and complement the development of specific cognitive and procedural skills. It reflects especially the use of the lectures.
Assessment System
  • % end-of-term-examination 20
  • % of continuous assessment (assigments, laboratory, practicals...) 80
Calendar of Continuous assessment

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