Applied Data Analysis
- Credits: 3
- Ending: Examination
- Range: 2C
- Semester: summer
- Year: 1
- Faculty of Economics and Finance
Teachers
Included in study programs
Teaching results
Knowledge
Students will gain knowledge of modern research methods of Big Data and data analysis.
Skills
They will acquire skills in working with Big data, which they will be able to use in their own empirical research.
They will acquire advanced skills for the use of modern software (R, Python) in empirical economic research, they will be able to write scripts, and program more advanced analyses.
Competencies
They will be able to formulate an economic problem and design a research design for its examination through data analysis, formulate hypotheses and analyse or refuse them analytically.
Indicative content
1. How do we estimate f. The trade-off between prediction accuracy and model interpretability.
2. Supervised vs unsupervised learning.
3. Regression vs. classification problems.
4. The bias-variance trade-off. Classification problems.
5. Logistic regression. LDA. QDA. KNN.
6. Cross-validation and bootstrapping.
7. Ridge regression. Lasso regression.
8. Polynomial regression and local regressions.
9. Regression trees.
10. Bagging and random forest.
11. Web scrapping.
12. Principal components analysis.
Support literature
James, G., Witten, D., Hastie, T. and Tibshirani, R., 2013. An introduction to statistical learning: with Applications in R. New York: Springer.
Requirements to complete the course
20 % - activity during seminars
20 % - assignments
60 % - final exam
Student workload
Total study load: 78
Out of that: participation in seminars 26, preparation for seminars 13, assignments 13, preparation for the final exam 26
Language whose command is required to complete the course
Slovak and English languages
Date of approval: 10.02.2023
Date of the latest change: 03.01.2022