Introduction to Data Science

Code:

CC4060

Acronym:

CC4060

Level:

400

Keywords
Classification	Keyword
OFICIAL	Computer Science

Instance: 2023/2024 - 1S

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

Cycles of Study/Courses

Acronym	No. of Students	Study Plan	Curricular Years	Credits UCN	Credits ECTS	Contact hours	Total Time
E:BBC	0	PE_Bioinformatics and Computational Biology	1	-	6	42	162
M:A_ASTR	8	Study plan since academic year 2023/2024	1	-	6	42	162
			2
M:BBC	22	The study plan since 2018	1	-	6	42	162
M:CTN	6	Official Study Plan since 2020_M:CTN	1	-	6	42	162
M:DS	24	Official Study Plan since 2018_M:DS	1	-	6	42	162
M:EGEO	1	Official Study Plan.	1	-	6	42	162

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.

Objectives

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

Presencial

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

Programming knowledge, especially in Python or R Knowledge of statistics

Program

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

There will be one test and one group assignment.

There will be activities to promote participation and feedback such as class questions and group discussions.

There will be final exam.

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

Final Grade.0 = 0.40 x GradeExam + 0.35 x GradeAssignment + 0.25 x GradeTest

CompIndividual=weighted_avg(GradeExam, GradeTest)

FinalGrade = min (FinalGrade.0, CompIndividual*1.35)

Classification improvement

Assignments are not subject to improvement in the appeal season