Introduction

The objective of this course is to provide students with formal training on various advanced skills in R, students will be pre-loaded with these skills prior to entering the workplace. After taking this course, students will be able to understand 1) advanced R, 2) basic regression techniques, and 3) how to construct and run simple machine learning algorithms. This lab session will employ a lecture. There will be 5 homework to be completed outside of the lab session; these assignments will be related to what material is taught in the main lecture.

Course Outcomes

After successful completion of this course, students will be able to:

• Write codes in R as a convenient, user-friendly, handy, and powerful software in quantitative finance
• Understand the concept of programming, a skill they need for the rest of their career and feel comfortable with writing their own codes
• Use different functions and packages in R, and obtain programming skills
• Import data from other resources such as files or online sources, and connect to basic databases

Textbook

Dalgaard, Peter. Introductory statistics with R. Springer Science & Business Media, 2008. 

Additional Resources

W. N. Venables, D. M. Smith, and the R Core Team,\An Introduction to R: Notes on R: A Programming Environment for Data Analysis and Graphics, Version 3.5.2, 2018-12-20", https://cran.r-project.org/

The R Project for Statistical Computing, https://www.r-project.org/

R-Bloggers,", https://www.r-bloggers.com/