Introduction to Data Science

Code:

CC4046

Acronym:

CC4046

Keywords
Classification	Keyword
OFICIAL	Computer Science

Instance: 2018/2019 - 1S

Active?	Yes
Responsible unit:	Department of Computer Science
Course/CS Responsible:	Bioinformatics and Computational Biology

Cycles of Study/Courses

Acronym	No. of Students	Study Plan	Curricular Years	Credits UCN	Credits ECTS	Contact hours	Total Time
E:BBC	3	PE_Bioinformatics and Computational Biology	1	-	6	42	162

Teaching language

Suitable for English-speaking students

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 in a typical data science project and the most common operations on each stage. They should understand the different problems in the scope of a typical data scientist job and develop the required critical thinking to discuss the pros and cons of the each approach.

Working method

Presencial

Program

The CRISP-DM model. Data collection and pre-processing. Modeling and different types of learning problems. Evaluation methods. Reporting and Deployment.

Mandatory literature

Torgo Luís; Data mining with R. ISBN: 978-1-4398-1018-7

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 (%)
Teste	50,00
Trabalho prático ou de projeto	50,00
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

At least 35% in each of the two theoretical tests, and their average above 9.5 points.

Calculation formula of final grade

There will be two theoretical tests and one practical group assignment. The final grade is given as a weighted average of the theoretical and practical grades using the following formula:

GFinal = 0.50 x GradeTheory + 0.50 x GradePract

where GradeTheory is the average of the grades of the two theoretical tests or of the final exam, and GradePract is the grade of the practical assignment.

The two theoretical tests are not mandatory, but if you obtain at least 35% on each test and your final grade (GradeTheory) is positive, then you don't need to go to the final exam. Otherwise, GradeTheory will be given by your grade at the final exam.

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