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Introduction to Data Science

Code: CC4060     Acronym: CC4060     Level: 400

Classification Keyword
OFICIAL Computer Science

Instance: 2020/2021 - 1S Ícone do Moodle Ícone  do Teams

Active? Yes
Responsible unit: Department of Computer Science
Course/CS Responsible: Master's degree in Data Science

Cycles of Study/Courses

Acronym No. of Students Study Plan Curricular Years Credits UCN Credits ECTS Contact hours Total Time
M:BBC 17 The study plan since 2018 1 - 6 42 162
M:CTN 0 Official Study Plan since 2020_M:CTN 1 - 6 42 162
M:DS 27 Official Study Plan since 2018_M:DS 1 - 6 42 162

Teaching Staff - Responsibilities

Teacher Responsibility
Alípio Mário Guedes Jorge

Teaching - Hours

Theoretical and practical : 3,00
Type Teacher Classes Hour
Theoretical and practical Totals 2 6,00
Alípio Mário Guedes Jorge 3,00
Inês de Castro Dutra 1,50
Mais informaçõesLast updated on 2020-09-18.

Fields changed: Calculation formula of final grade, Componentes de Avaliação e Ocupação, Obtenção de frequência, Melhoria de classificação

Teaching language

Suitable for English-speaking students
Obs.: As aulas serão em inglês no caso de haver estudantes que não falam português. Todos os materiais estão em inglês. Classes are in English iin case there are non-Portuguese speaking students. All materials are in English.


Students will obtain a global perspective on the different steps of a Data Science project. For each of these steps, some of the main techniques and methods will be presented while further details will be addressed in more specific courses.

Learning outcomes and competences

Students should:
- know all the steps of a data science project and its most common operations;
- identify different types of data science problems;
- justifiably select appropriate methods, algorithms and tools to solve these problems
- justifiably apply methods, algorithms and tools to solve these problems
- explain the foundations of methods, algorithms and tools
- evaluate the results and propose improvements
- know the specifics of the application of data science solutions in a production environment

Working method


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

Programming knowledge, especially in Python or R Knowledge of statistics


The CRISP-DM model. Data collection and pre-processing. Modeling and different types of learning problems. Data science algorithms. Model evaluation methods. Putting models into production.

Mandatory literature

Jake VanderPlas; Python Data Science Handbook, O'Reilly, 2016. ISBN: 978-1-491-91205-8

Teaching methods and learning activities

Tutorial classes with theory exposition and problem solving activities.

Evaluation Type

Distributed evaluation with final exam

Assessment Components

designation Weight (%)
Trabalho prático ou de projeto 35,00
Exame 40,00
Teste 25,00
Total: 100,00

Amount of time allocated to each course unit

designation Time (hours)
Elaboração de projeto 78,00
Estudo autónomo 42,00
Frequência das aulas 42,00
Total: 162,00

Eligibility for exams

Grade above zero in the assignment and in the test. Answer to class quastions submitted online.

Calculation formula of final grade

Two practical group works will be carried out.

Class questions will be launched.

A final exam will be carried out.

The final grade is given by the weighted average of theoretical and practical grades according to the following formula:

Final Grade.0 = 0.50 x GradeExam + 0.50 x GradePract

CompIndividual=weighted_avg(GradeExam, GradeTest)

FinalGrade = min (FinalGrade.0, CompIndividual*1.2)

Classification improvement

Assignments are not subject to improvement in the appeal season
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