Statistics for Data Science
This course covers techniques of multivariate statistical and time series analysis. Students should be able, given a set of data and a particular goal, choose the appropriate methodology and have critical capacity in relation to the results obtained. They should also have knowledge of the advantages, limitations and conditions for the use of various data analysis methods presented by the course.
Jorge Morais Mendes
Weekly - Available soon
Total - Available soon
Portuguese. If there are Erasmus students, classes will be taught in English
Statistics and linear algebra (recomended)
- Everitt, B. and Hothorn, T. (2011). An Introduction to Applied Multivariate Analysis with R, Springer;
- Johnson, R.A and Winchern (2007), D. W., Applied Multivariate Statistical Analysis, 6th edition, Pearson Prentice Hall;
- Sharma, S., (1996) Applied Multivariate Techniques, John Wiley & Sons;
- Kutner, M.H., Nachtsheim, C.J., Neter, J., Li,W. (2004) Applied Linear Statistical Models, 5th edition, McGraw-Hill.
The course is based on theoretical and practical classes. The classes are aimed at solving problems and exercises.
- (60%) Final exam (1st or 2nd round dates)
- (40%) Project
Remarks: A minimum grade of 9.5 points is required in final exam.
1. Introduction to Multivariate Statistics Data Analysis
2. Fundamentals on data manipulation - introducing R software
3. Multivariate normal distribution
4. Graphical representation of multivariate data
5. Principal components analysis
6. (Exploratory) Factor Analysis
7. Cluster analysis
8. Linear regression méthods
9. Time series analysis
Programs where the course is taught: