Statistical-computing

STAT GR5206 Statistical Computing and Introduction to Data Science taught by Professor Cynthia Rush

The topics discussed in this course includes

Mathematics Basis Review

• Calculus (Gradient, integrals);
• Linear Algebra (inverse, eigenvalue, eigenvector, orthogonal, decomposition);
• Probabilities and Statistics (MLE, MME).

Introduction to R and Rstudio

Working with Data in R

• Data type and data structures in R;
• Data manipulations.

EDA and Data Visualization

Bootstrap and Cross Validation

Web scraping, String manipulation and Regular Expression

Functions

Simulation

• Inverse transformation;
• Rejection sampling.

Optimization

• Gradient descent;
• Newton's method.

KNN

Kmeans Clustering

PCA

Data Manipulation: split/apply/combine and plyr

Databases and Parallel

List of R packages used:

• ISLR
• ggplot2 (grid, gridExtra for multi-plotting)
• plyr
• numDeriv
• RDBI
• RSQLite
• xlsx