Data Analysis

Code:

MCI0012

Acronym:

AD

Keywords
Classification	Keyword
OFICIAL	Computer Science

Instance: 2013/2014 - 1S (of 09-09-2013 to 20-12-2013)

Active?	Yes
Web Page:	https://www.fe.up.pt/si/DISCIPLINAS_GERAL.FORMVIEW?P_ANO_LECTIVO=2009/2010&P_CAD_CODIGO=MCI0012&P_PERIODO=2S
E-learning page:	https://moodle.fe.up.pt/
Responsible unit:	Department of Informatics Engineering
Course/CS Responsible:	Master in Information Science

Cycles of Study/Courses

Acronym	No. of Students	Study Plan	Curricular Years	Credits UCN	Credits ECTS	Contact hours	Total Time
MCI	3	Plano de estudos oficial	1	-	6	56	162

Teaching language

Suitable for English-speaking students

Objectives

Background:

After a season in which the different companies / institutions lot invested in data collection within the computerization of their operations, there is now the need to put this data in the service of these companies / institutions. The goal is to be able to extract knowledge from data, improving efficiency and gaining competitive advantage. It is this need that arises the Course (UC) Data Analysis (AD).

Objectives:

To prepare students so as to be able to identify data analysis problems and to properly use the appropriate methods for its resolution.

Learning outcomes and competences

As a learning result, it is intended that students:

-Know the different types of AD tasks.

-Identify issues for decision support that can be represented as AD tasks.

-Know the main methods for each AD task type and understand its essential function.

-Apply these methods to problems in decision support.

-Evaluate the results of a AD project.

Working method

Presencial

Pre-requirements (prior knowledge) and co-requirements (common knowledge)

Prerequisites: -Not being required to have attended any UC in concrete, it is useful to have attended any UC on introduction to statistics.

Program

1. Objectives 2. Data types and description 3. Sampling distributions and central limit theorem 4. Interval estimate 5. Hypothesis Testing 6. Analysis of variance 7. Linear regression 8. Classification 9. Aggregate analysis 10. Association rules

Mandatory literature

J. P. Marques de Sá; Applied statistics using SPSS, STATISTICA and MATLAB. ISBN: 3-540-01156-0

Complementary Bibliography

Rui Campos Guimarães, José A. Sarsfield Cabral; Estatística. ISBN: 978-84-481-5589-6
Matthew North; Data mining for the masses, 2012. ISBN: 0615684378

Teaching methods and learning activities

Theoretical classes are based on the presentation of course unit themes followed by practical experiments.

Software

SPSS
Rapid Miner

Evaluation Type

Distributed evaluation without final exam

Assessment Components

Designation	Weight (%)
Participação presencial	0,00
Trabalho laboratorial	40,00
Teste	60,00
Total:	100,00

Amount of time allocated to each course unit

Designation	Time (hours)
Frequência das aulas	68,00
Estudo autónomo	60,00
Trabalho laboratorial	35,00
Total:	163,00

Calculation formula of final grade

0.3*Test 1 + 0.3*Test 2 + 0.4*Assignment Minimum grades: 0.5*Test 1 + 0.5*Test 2 >= 7.0

Examinations or Special Assignments

The assignment is based on the execution of a group assignment (two people). The grade may be different to each element of the group.

Special assessment (TE, DA, ...)

Special assessment exam will take place at the same time as the improvement of final grade exam, according to General Evaluation Rules of FEUP.

Classification improvement

Students may improve their grades by attending an exam with two components: 1. a part corresponding to the continuous assessment tests; 2. an extra part which aims to assess the skills related to the practical assignment. The improvement of final grade takes place at the corresponding appeal exam in the current edition of the course or in the subsequent ones.