CTU FEE Moodle
Experimental Data Analysis
B232 - Summer 23/24
Experimental Data Analysis - B2M31AEDA
| Credits | 6 |
| Semesters | Winter |
| Completion | Assessment + Examination |
| Language of teaching | Czech |
| Extent of teaching | 2P+2C |
Annotation
In the course of subject "Experimental Data Analysis", students will acquire knowledge regarding fundamental methods for data analysis and machine learning for evaluation and interpretation of data. In the course of practical lectures, students will solve individual tasks using real data from signal processing in neuroscience research. In the course of semestral project, student will solve complex task and present obtained results. The aim of the subject is to introduce practical application of fundamental statistical methods as well as to teach students to use critical thinking and to acquire additional knowledge in solution of practical tasks.
Study targets
The aim of the subject is to introduce practical application of fundamental statistical methods as well as to teach students to use critical thinking and to acquire additional knowledge in solution of practical tasks.
Course outlines
1. Introduction to the subject "Experimental Data Analysis", introduction to data
2. Introduction to the statistics, probability distributions, and plotting statistical data
3. Hypothesis testing, group differences, paired test, effect size
4. Correlations, normality of data testing, parametric vs. non-parametric tests
5. Analysis of variance, post-hoc testing
6. Type I & Type II errors, multiple comparisons, sample size estimation
7. Factorial analysis of variance
8. Introduction to models, regression analysis
9. Supervised classification
10. Model validation
11. Unsupervised classification
12. Dimensionality reduction, data interpretation
13. Reserve, consultation of semestral projects
14. Presentation of obtained results
2. Introduction to the statistics, probability distributions, and plotting statistical data
3. Hypothesis testing, group differences, paired test, effect size
4. Correlations, normality of data testing, parametric vs. non-parametric tests
5. Analysis of variance, post-hoc testing
6. Type I & Type II errors, multiple comparisons, sample size estimation
7. Factorial analysis of variance
8. Introduction to models, regression analysis
9. Supervised classification
10. Model validation
11. Unsupervised classification
12. Dimensionality reduction, data interpretation
13. Reserve, consultation of semestral projects
14. Presentation of obtained results
Exercises outlines
1. Introduction to Matlab
2. Introduction to the statistics, probability distributions, and plotting statistical data
3. Hypothesis testing, group differences, paired test, effect size
4. Correlations, normality of data testing, parametric vs. non-parametric tests
5. Analysis of variance, post-hoc testing
6. Type I & Type II errors, multiple comparisons, sample size estimation
7. Factorial analysis of variance
8. Introduction to models, regression analysis
9. Supervised classification
10. Model validation
11. Unsupervised classification
12. Dimensionality reduction, data interpretation
13. Reserve, consultation of semestral projects
14. Presentation of obtained results
2. Introduction to the statistics, probability distributions, and plotting statistical data
3. Hypothesis testing, group differences, paired test, effect size
4. Correlations, normality of data testing, parametric vs. non-parametric tests
5. Analysis of variance, post-hoc testing
6. Type I & Type II errors, multiple comparisons, sample size estimation
7. Factorial analysis of variance
8. Introduction to models, regression analysis
9. Supervised classification
10. Model validation
11. Unsupervised classification
12. Dimensionality reduction, data interpretation
13. Reserve, consultation of semestral projects
14. Presentation of obtained results
Literature
[1] Vidakovic B. Statistics for bioengineering sciences: with Matlab and WinBUGS support. New Yourk: Springer, 2011.
[2] Hastie T, Tibshirani R, Friedman JH. The elements of statistical learning : data mining, inference, and prediction: with 200 full-color illustrations. New York: Springer, 2001.
[2] Hastie T, Tibshirani R, Friedman JH. The elements of statistical learning : data mining, inference, and prediction: with 200 full-color illustrations. New York: Springer, 2001.
Requirements
The basic knowledge of Matlab software.