Modelling and Data Analysis II

Code:

2MiF17

Acronym:

MDA 2

Keywords
Classification	Keyword
OFICIAL	Management Studies

Instance: 2022/2023 - 1S

Active?	Yes
Course/CS Responsible:	Master in Finance

Cycles of Study/Courses

Acronym	No. of Students	Study Plan	Curricular Years	Credits UCN	Credits ECTS	Contact hours	Total Time
MIF	33	Official Syllabus after 2020-2021	2	-	6	42	162

Teaching language

English

Objectives

The course aims to develop the skills to define and use data mining projects.

Learning outcomes and competences

The definition of a data mining project requires: knowing the different data mining tasks, knowing the different methods and algorithms for each task, understanding how the methods work, being able to apply these methods to new data mining problems, being able to evaluate and interpret the results.

Working method

Presencial

Program

1. Introduction to data mining




1. Knowledge, generalization and specialization. Knowledge representation;


2. Data mining tasks;


3. Tools for data mining.




2. Exploratory Data Analysis




1. Collecting;


2. Initial Data Exploration;




1. Cleaning;


2. Data visualization;


3. Attributes selection;


4. Extreme values (outliers);


5. Missing values;




3. Exploratory data analysis;


4. Graphical Visualization;




3. Predictive modelling




1. Distance based methods: k -NN;


2. Probabilistic methods: Bayesian classifiers;


3. Search based methods: decision trees, rules;


4. Optimization methods: SVM, ANN;


5. Evaluation of classification and regression methods: metrics, costs. ROC analysis;


6. Multiple Models;


7. Pre-processing, outliers, missing values, discretization.




4. Descriptive Modelling




1. Cluster analysis,Frequent patterns, Association analysis;


2. Groups analysis.




5. Conclusing remarks




1. Data mining Process Methodologies;


2. Data Mining and Ethics.

 

Mandatory literature

Ian H. Witten; Data mining. ISBN: 1-55860-552-5
Jiawei Han; Data mining. ISBN: 978-0-12-381479-1

Complementary Bibliography

João Manuel Portela da Gama; Extração de conhecimento de dados. ISBN: 978-972-618-914-5

Teaching methods and learning activities

The course is organized in lab sessions, based on modules. The teaching methodology in each module is structured as follows:

• description of the financial problem to solve;


• identification with explanation of the appropriate computacional methods for their resolution;


• exercises (sedimentation and knowledge exploitation).

Software

Jupyter
Python
R

Evaluation Type

Distributed evaluation without final exam

Assessment Components

Designation	Weight (%)
Apresentação/discussão de um trabalho científico	20,00
Teste	30,00
Trabalho prático ou de projeto	50,00
Total:	100,00

Amount of time allocated to each course unit

Designation	Time (hours)
Estudo autónomo	60,00
Frequência das aulas	42,00
Trabalho escrito	20,00
Trabalho laboratorial	40,00
Total:	162,00

Eligibility for exams

According to the General Regulation for the Assessment of First Degreeand Master’s Degree students at the School of Economics and Management of the University of Porto all students enrolled in a course unit fulfill attendance requirements. (article 10th point 5)

Calculation formula of final grade

30% individual assessment + 70% group work (2 works with the same weight)