Here listed all assignments corresponding to each modules.
- Introduction to R
a. Variable types and basic math
b. Vectors, matrices and data frames; data import and export
- Scripting and Graphics in R
a. Coding, loops, and vectorized operations
b. Visualizing data with ggplot2
- Probability
a. Discrete and continuous distributions; marginal and conditional probabilities.
b. Binomial, Poisson, normal, and other common distributions
- Statistics
a. Samples and populations; population parameters
b. Central Limit Theorem; standard errors; T distribution
- Statistical Tests 1
a. Significance, p-values, alpha level, type 1 and type 2 errors
b. Means tests and difference in means tests
- Statistical Tests 2
a. F test and ANOVA
b. Chi-square test
- Bivariate Regression
a. Correlation and partial correlation;
b. OLS; significance tests; R2
- Multiple Regression Basics
a. Interpreting coefficients and regression results
b. Deriving coefficients and R2
- Multiple Regression Topics
a. Causal pathways and variable selection.
b. Common problems and solutions in regression (optional)
- Multiple Regression: Non-linear variables
a. Non-linear independent variables.
b. Categorical dependent variables (optional).
- Unsupervised Machine Learning
a. Factor and principal component analysis
b. Clustering
- supervised Machine Learning
a. Shrinkage methods and elastic net
b. Support vector machines