Advanced statistics 2401-CS-11-AS-s2

(po angielsku) Contact hours with teacher: - tutorials- 30 hrs - consultations- 30 hrs Self-study hours: - preparation for tutorials - 15 hrs - preparation for test/ examination- 30 hrs Altogether: 115 hrs (4 ECTS)

(po angielsku) Student W1: is familiar with different data segmentation and visualization methods – K_W08 W2: thorough knowledge of quantitative data analysis – in such fields as modeling, data reduction, data visualization – K_W02, K_W05, K_W08 W3: is well acquainted with the concept of supervised and unsupervised learning approaches used in statistics – K_W07, K_W08 W4: knows how to perform advanced analysis of relationships between studied variables using correlations, regression models, decission tree models, factor analysis– K_W06, K_W08

(po angielsku) Student U1: is able to design an advanced statistical data analysis pipeline in specialized software (R / SPSS) – K_U01, K_U06, K_U12 U2: can preprocess, classify, and visualize data using supervised and unsupervised algorithm (k-means, hierarchical cluster analysis, decision trees, factor analysis) - K_U01 , K_U09, K_U12 U3: can conduct an analysis of relationship between variables using multivariate correlational and regression approaches ) – K_U04, K_U07, K_U12 U4: can perform modeling using SPSS / R software - K_U02, K_U03, K_U12

(po angielsku) Student K1: understands the need for appropriate data preprocessing and visualization – K_K01 K2: is well prepared to critically analyze data and draw well-informed conclusions – K_K02 K3: is aware of power and limitations of statistical tools – K_K02 K4: while understanding own limitations and strong points, is able to add value to team works in advanced analytical projects - K_K05

(po angielsku) Expository teaching methods: - informative (conventional) lecture Exploratory teaching methods: - practical - laboratory

- pokaz

- wykład informacyjny (konwencjonalny)
- opis
- pogadanka

- ćwiczeniowa
- laboratoryjna

przedmiot obligatoryjny

(po angielsku) 1. Basics in quantitative data analysis. 2. Knowledge of basic statistical terms (egz.: mean, sd. dev., median) 3. Basic skills in statistical analysis software: SPSS, R, Excel.

Dodatkowe informacje (np. o kalendarzu rejestracji, prowadzących zajęcia, lokalizacji i terminach zajęć) mogą być dostępne w serwisie USOSweb:

• Strona przedmiotu 2401-CS-11-AS-s2 w USOSweb