Subjects

Your browser does not support JavaScript, or its support is disabled. Some features may not be available.

Introduction to data analysis - NMMB333

Title:	Základy analýzy dat
Guaranteed by:	Department of Algebra (32-KA)
Faculty:	Faculty of Mathematics and Physics
Actual:	from 2020
Semester:	winter
E-Credits:	5
Hours per week, examination:	winter s.:2/2, C+Ex [HT]
Capacity:	unlimited
Min. number of students:	unlimited
4EU+:	no
Virtual mobility / capacity:	no
State of the course:	taught
Language:	Czech
Teaching methods:	full-time
Teaching methods:	full-time

Guarantor:	RNDr. Pavel Charamza, CSc.
Class:	M Mgr. MMIB M Mgr. MMIB > Povinně volitelné
Classification:	Mathematics > Mathematics, Algebra, Differential Equations, Potential Theory, Didactics of Mathematics, Discrete Mathematics, Math. Econ. and Econometrics, External Subjects, Financial and Insurance Math., Functional Analysis, Geometry, General Subjects, , Real and Complex Analysis, Mathematics General, Mathematical Modeling in Physics, Numerical Analysis, Optimization, Probability and Statistics, Topology and Category

Opinion survey results Examination dates WS schedule Noticeboard

Annotation -

Last update: doc. Mgr. et Mgr. Jan Žemlička, Ph.D. (09.05.2018)

The lecture covers standard methods of data analysis, including modern trends of big data analysis using machine learning. Modelling over real data in the R environment.

Course completion requirements -

Last update: doc. Mgr. et Mgr. Jan Žemlička, Ph.D. (28.10.2019)

Students have to pass oral exam.

Literature -

Last update: doc. Mgr. et Mgr. Jan Žemlička, Ph.D. (09.05.2018)

Zvára Karel: Regresní analýza, Academia 1989

Hebák, Hustopecký: Vícerozměrné statistické metody 1, 2, 3, Informatorium, 2007

Kolaczyk, Csardi: Statistical Analysis of Network Data with R, Springer, 2014

Munzert, Rubba, Meissner, Nyhuis: Automated Data Collection with R, Wiley, 2015

Requirements to the exam -

Last update: doc. Mgr. et Mgr. Jan Žemlička, Ph.D. (28.10.2019)

Students have to pass oral exam. The requirements for the exam correspond to what has been done during lectures and practicals.

Syllabus -

Last update: doc. Mgr. et Mgr. Jan Žemlička, Ph.D. (09.05.2018)

1) Basics of linear regression, logistic regression, lasso regression, principles of hypotheses testing, likelihood ratio tests, stepwise algorithms

2) Basics of multidimensional statistics - principle component analysis, factor analysis, cluster analysis

3) Discrimination measures - Kolmogorov-Smirnov, Gini coefficient, Somer’s d

4) Back test principles, cross validation and bootstrapping

5) Regression trees, random forests

6) Gradient boosting

7) Bayes networks, neural networks

8) Linear optimization, Support vector machine

Labs: Programming in R, practical work with data