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Day 1: Exploratory data analysis
(Lectors: Adéla Vrtková, Martina Litschmannová)

• Types of Variables
• Description and Visualization of Qualitative Variables (frequency tables, bar charts, pie charts, …)
• Description and Visualization of Quantitative Variables (measures of central tendency, measures of spread, measures of position, measures of shape of distribution, histograms, box plots)
• Data Normality (normal distribution, q-q plot)
• Introduction to R
• Exploratory Data Analysis with R

Day 2: Introduction to Statistical Inference
(Lectors: Adéla Vrtková, Martina Litschmannová))

• Introduction to Estimation Theory (point estimates, confidence intervals)
• Introduction to Hypothesis Testing (definition of terms, testing process, p-value, common test statistics)
• Solving the particular real-life problems using statistical inference with R

Day 3: Analysis of Variance, Contingency Tables
(Lectors: Adéla Vrtková, Martina Litschmannová))

• Parametric and Nonparametric One-Way ANOVA
• Contingency Tables (how to read contingency tables, chi-square test of independence, odds ratio, risk ratio, mosaic plot, …)

Day 4: Introduction to Time Series Analysis and Introduction to Bayesian Statistics
(Lectors: Ondřej Vencálek, Tomáš Fürst)

• Introduction to Time Series Analysis (definitions, exploratory time series data analysis, moving averages, smoothing techniques)
• Introduction to Bayesian Statistics

Day 5: Data Visualization with Shiny
(Lector: Matyáš Theuer)

• Using Shiny – an R package that makes it easy to build interactive web apps