1: Introduction and Descriptive Statistics
1.1 Introduction to the course.
1.2 Introduction to R. Basics, arithmetic with R, variable assignment. Basic data types in R.
1.3 Vectors, matrices, factors, data frames.
1.4 Reading and writing data in R.
2: Exploring categorical and numerical data data.
2.1 Bar charts, contingency tables, counts, proportions, piecharts.
2.2 Histograms, boxplots, visualizing in higher dimensions.
3: Numerical Summaries.
3.1 Measures of center. Median, median, quartiles and quantiles.
3.2 Measures of variability. Variance, standard deviation, IQR.
3.3 Shape and transformations.
3.4 Outliers.
4. Case Study for lessons 1-3.
5. Multivariate Data
5.1 Description of multivariate data.
5.2 Covariance, correlation, distances.
5.3 Visualization of multivariate data: scatterplots, bubble plots, etc.
6. Principal Component Analysis for visualization
6.1 Introduction and main ideas.
6.2 Implementing PCA in R.
6.3 Case Study.
7. Cluster Analysis for data exploration
7.1 Introduction and main ideas.
7.2 Hierarchical Methods.
7.3 Partitioning Methods.
7.4 Case study.
8. Linear Regression
8.1 Univariate Case.
8.2 Multivariate Case.
8.3 Case Study
9. Introduction to Tidyverse.
9.1 Data wrangling
9.2 Data Visualization: ggplot2
9.3 Grouping and summarizing.
10. Final Real case study.
