Data Mining I

Code:

CC4018

Acronym:

CC4018

Level:

400

Keywords
Classification	Keyword
OFICIAL	Computer Science

Instance: 2016/2017 - 1S

Active?	Yes
Web Page:	http://www.dcc.fc.up.pt/~ltorgo/DM1
Responsible unit:	Department of Computer Science
Course/CS Responsible:	Master in Computer Science

Cycles of Study/Courses

Acronym	No. of Students	Study Plan	Curricular Years	Credits UCN	Credits ECTS	Contact hours	Total Time
M:AST	1	Plano de Estudos oficial desde_2013/14	1	-	6	42	162
			2
M:CC	23	Study plan since 2014/2015	1	-	6	42	162
MI:ERS	17	Plano Oficial desde ano letivo 2014	4	-	6	42	162
M:SI	6	Study plan since 2014/2015	1	-	6	42	162

Teaching language

Suitable for English-speaking students

Objectives

This unit has as main objectives to provide an introduction to the main data mining methodologies and also to convey knowledge on programming and tools for data analysis using R language.

Learning outcomes and competences

This unit should provide the students with: 
1. theoretical competences on several basic methodologies of data mining. 
2. competences regards developing software for data mining tasks. 
3. practical competences on applying data mining techniques to specific problems. 

Working method

Presencial

Program

Syllabus:

- Introduction to Data Mining: data, models and patterns, data mining tasks, and introduction to the data mining process.

- Data Pre-Processing: importing, cleaning, transforming and creating variables, dimensionality reduction techniques

- Exploring and Visualizing Data: data summarization, data visualization

- Descriptive Models: clustering methods 

- Predictive Models: classification and regression tasks, linear regression models, naive Bayes, k-nearest neighbours,  decision trees, neural networks, support vector machines, ensembles 

- Methodologies for Evaluating and Comparing Models: evaluation metrics, estimation methods, significance tests

Mandatory literature

000105684. ISBN: 9781439810187 hbk
J. Han , M. Kamber and J. Pei ; Data Mining - Concepts and Techniques (3rd edition), 2011. ISBN: 9780123814791

Complementary Bibliography

Peter Flach; Machine Learning, Cambridge University Press, 2012. ISBN: 978-1-107-42222-3
000075501. ISBN: 9780262082907 hbk

Teaching methods and learning activities

The lectures are based on the oral exposition of the topics that are part of the syllabus, as well as illustrations with concrete data mining case studies. 

Software

R - software para análise de dados
Rstudio - IDE para o R

keywords

Technological sciences > Technology > Information technology
Physical sciences > Computer science > Modelling tools
Physical sciences > Computer science > Informatics > Applied informatics
Technological sciences > Technology > Computer technology > Software technology

Evaluation Type

Distributed evaluation with final exam

Assessment Components

designation	Weight (%)
Teste	60,00
Trabalho prático ou de projeto	40,00
Total:	100,00

Eligibility for exams

Students must participate either on the two tests of the unit or on the final exam, composed by two parts corresponding to the contents of test1 and test2.

Calculation formula of final grade

The assessment of the unit is distributed without final exam and it is composed by two (2) theoretical tests, one written and oral work (1) and one (1) practical assignment.

The final grade is given as a weighted average of the theoretical and practical grades using the following formula:

GFinal = 0.60 * GradeTheory + 0.30 * GradePract

where,
GradeTheory is the average of the grades on the two theoretical tests or of the final exame and
GradePract is given by the grade of the practical assignment

There will be 2 tests during the semester. These are not compulsory, but everyone that obtains minimum grade in each test, and in the practical assignment, and whose final grade (GFinal) is positive (above 9.5), does not need to go to the final exam.

Minimum grade of the tests and pratical assignment : 35% (7 out of 20)

Examinations or Special Assignments

The theoretical tests will take place in two thereotical classes, one in the middle of the semester and the other at the end of it.

The practical assignmentswill be announced in the middle of the semester and should be completed by the end of the semester. 

Special assessment (TE, DA, ...)

Students that are in special situations according to the legislation, can arrange to have tests and exams in dates other than the regular ones.

Classification improvement

The student can improve in the theoretical grade by taking the final exam

Observations

All of the provided material (e.g. slides, recommended books) is given in English and if there are foreign students the classes can also be given in English.