Introduction to Data Science
Instance: 2020/2021 - 1S
Cycles of Study/Courses
Teaching Staff - Responsibilities
Teaching - Hours
|Theoretical and practical :
Last updated on 2020-09-18.
Fields changed: Calculation formula of final grade, Componentes de Avaliação e Ocupação, Obtenção de frequência, Melhoria de classificação
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
- 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
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.
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.
Distributed evaluation with final exam
|Trabalho prático ou de projeto
Amount of time allocated to each course unit
|Elaboração de projeto
|Frequência das aulas
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
FinalGrade = min (FinalGrade.0, CompIndividual*1.2)
Assignments are not subject to improvement in the appeal season