Code: | CC4018 | Acronym: | CC4018 | Level: | 400 |
Keywords | |
---|---|
Classification | Keyword |
OFICIAL | Computer Science |
Active? | Yes |
Web Page: | http://www.dcc.fc.up.pt/~ltorgo/DM1_1718 |
Responsible unit: | Department of Computer Science |
Course/CS Responsible: | Master in Computer Science |
Acronym | No. of Students | Study Plan | Curricular Years | Credits UCN | Credits ECTS | Contact hours | Total Time |
---|---|---|---|---|---|---|---|
M:AST | 0 | Plano de Estudos oficial desde_2013/14 | 1 | - | 6 | 42 | 162 |
2 | |||||||
M:CC | 21 | Study plan since 2014/2015 | 1 | - | 6 | 42 | 162 |
MI:ERS | 41 | Plano Oficial desde ano letivo 2014 | 4 | - | 6 | 42 | 162 |
M:SI | 5 | Study plan since 2014/2015 | 1 | - | 6 | 42 | 162 |
This unit has as main objectives to provide an introduction to the main data science methodologies and also to convey knowledge on programming and tools for data analysis using R language.
This unit should provide the students with:
1. theoretical competences on several basic methodologies of data science.
2. competences regards developing software for data science tasks.
3. practical competences on applying datas cience techniques to specific problems.
Syllabus:
- Introduction to Data Science: the CRISP-DM model; data, models and patterns and data science tasks.
- Data Pre-Processing: importing, cleaning, transforming and creating variables, dimensionality reduction techniques
- Exploring and Visualizing Data: data summarization, data visualization
- Descriptive Models: clustering methods
- Predictive Models: classification and regression tasks, linear models, naive Bayes, k-nearest neighbours, tree-based models, neural networks and deep learning, support vector machines, ensembles
- Methodologies for Evaluating and Comparing Models: evaluation metrics, estimation methods, significance tests
The lectures are based on the oral exposition of the topics that are part of the syllabus, as well as illustrations with concrete data mining case studies.
designation | Weight (%) |
---|---|
Teste | 60,00 |
Trabalho prático ou de projeto | 40,00 |
Total: | 100,00 |
Students must participate either on the two tests of the unit or on the final exam, composed by two parts corresponding to the contents of test1 and test2.
The assessment of the unit is distributed without final exam and it is composed by two (2) theoretical tests, and two (2) practical assignments, one group assignment and one individual assignment.
The final grade is given as a weighted average of the theoretical and practical grades using the following formula:
GFinal = 0.60 * GradeTheory + 0.30 * GradePract
where, GradeTheory is the average of the grades on the two theoretical tests or of the final exame and GradePract is given by the grade of the practical assignments, namely using the following weighted average: GradePract = 0.7*GradAss1 + 0.3*GradAss2
There will be 2 tests during the semester. These are not compulsory, but if you obtain at least the minimum grade (35% or 7 out of 20) in both tests, and your final grade (GFinal) is positive (≥9.5), you do not need to go to the final exam. Otherwise, you GradeTheory will be given by your grade at the final exam.
The theoretical tests will take place in two thereotical classes, one in the middle of the semester and the other at the end of it.
The 1st practical assignment will be announced in the middle of the semester and should be completed by the end of the semester. The 2nd practical assignment will be announced later and should also be delivered by the end of the semester.
The student can improve in the theoretical grade by taking the final exam
All of the provided material (e.g. slides, recommended books) is given in English and if there are foreign students the classes will also be given in English.