Laboratory
Keywords |
Classification |
Keyword |
OFICIAL |
Information Technology |
Instance: 2020/2021 - 1S
Cycles of Study/Courses
Teaching language
English
Objectives
Development of practical skills in the formulation and resolution of data analysis problems.
Development of practical skills in exploratory data analysis, data visualization, predictive and descriptive modeling.
Learning outcomes and competences
By the end of the course, the student should be able to:
- select and apply appropriated methods to
- describe
- visualize
- develop predictive modelling
- use appropriate software for data anaysis
- concisely summarize results of data analysis
Working method
Presencial
Program
Case 1 - Kaggle competition: exploratory data analysis, data visualization, attribute selection, extreme values.
Case 2 - Data Analysis
Case 3 - Descriptive Modeling. Cluster analysis.
Mandatory literature
Han, Jiawei; Kamber, Micheline ; Data mining: concepts and techniques, Morgan Kaufmann, 2001
Witten, Ian H.; Frank, Eibe; Hall, Mark A; Data Mining: Practical Machine Learning Tools and Techniques , Elsevier, 2011
M Berthold, DJ Hand; Intelligent data analysis: an introduction , Springer, 2007
Teaching methods and learning activities
Example classes with data analysis case studies.
Software
R
Knime
Evaluation Type
Distributed evaluation without final exam
Assessment Components
Designation |
Weight (%) |
Trabalho prático ou de projeto |
100,00 |
Total: |
100,00 |
Amount of time allocated to each course unit
Designation |
Time (hours) |
Estudo autónomo |
84,00 |
Frequência das aulas |
42,00 |
Trabalho escrito |
36,00 |
Total: |
162,00 |
Eligibility for exams
Delivery of 75% of final project preliminary reports.
Calculation formula of final grade
Preliminary reports: 40%
Final project: 40%
Oral presentation: 20%