Programming for Data Science (PDS-DAB)
Área
AC Gestão > UC Mestrados
Activa nos planos curriculares
Data Analytics for Business > Data Analytics for Business > 2º Ciclo > Unidades Curriculares Obrigatórias > Programming for Data Science
Nível
2º Ciclo (M)
Tipo
Estruturante
Regime
Semestral
Carga Horária
Aula Teórica (T): 0.0 h/semana
Aula TeoricoPrática (TP): 3.0 h/semana
Trabalho Autónomo: 121.0 h/semestre
Créditos ECTS: 6.0
Objectivos
L0 1.Consolidate main programming concepts
L0 2.Understand programming techniques to manipulate and visualize data
L0 3.Understand main algorithms that implemented in programming languages
L0 4.Solve problems using programming and algorithms.
Programa
1.Introduction to Programming
2.Object Oriented Programming
3.Extract, clean, prepare, and mine data
4.Data Visualization
5.Text and image processing
6.Machine Learning algorithms
a.Regressions and Classification
b.Unsupervised learning
7.Introduction to web programming
Metodologia de avaliação
All the classes are theoretical and practical. Lectures typically have a small presentation of theory, context of usage and techniques used. Lecturer also illustrate some practical cases. In this demonstration, the lecturer needs to use computer and adequate compilers/interpreters and IDE. Students may or may not follow this presentation in his own desktop. Then, there are several exercises where students are supported by the lecturer. Individual work is complemented with groupworks. Laboratory work may be individual or group work. Students also must perform a project in group.
Students performance evaluation will derive from laboratory work, submitted during classes (30%) the assigned team-works project presented during the semester (40%) and from a final individual exam (30%).
Bibliografia
Principal
Machine Learning with Python Cookbook: Practical Solutions from Preprocessing to Deep Learning
Albon, C.
2018
(1 edition). Sebastopol, CA: O?Reilly Media.
Data Science from Scratch with Python: Step by Step Guide
Morgan, P.
2018
AI Sciences
Mastering Python for Data Science
Madhavan, S.
2015
Packt Publishing Ltd
Think Python
Downey, A. B.
2016
2nd Edition. O?Reilly Media, Inc.
Secundária
Não existem referências bibliográficas secundárias.