FE515 Introduction to R
Course Catalog Description
Introduction
Data gathering, cleaning, and visualization
Fundamental topics in statistics (Moments, linear regression and etc.)
Fundamental topics in financial engineering (the BlackScholes model etc.)
Numerical methods in Finance (rootfinding methods, optimization, interpolation, and numerical integration)
Campus  Fall  Spring  Summer 

On Campus  X  X  
Web Campus  X  X 
Instructors
Professor  Office  

Beichen Zhang

bzhang61@stevens.edu 
More Information
Course Resources
Textbook
Lecture Notes and Code
The art of R programming: a tour of statistical software design. Norman Matlo, First Edition,2011. ISBN10: 1593273843, ISBN13: 9781593273842
An Introduction to Analysis of Financial Data with R. Ruey Tsay, First Edition, 2012. ISBN10:0470890819, ISBN13: 9780470890813
Introduction to the Practice of Statistics. David S. Moore, George P. McCabe, Bruce A. Craig, Eighth Edition, 2014. ISBN13: 9781464158933, ISBN10: 1464158932
Additional References
CRAN: http://www.wikibooks.org
Rhelp Info: https://stat.ethz.ch/mailman/listinfo/rhelp
Rhelp Archive: http://r.789695.n4.nabble.com
Quick R: http://www.statmethods.net
Grading
Grading Policies
Attendance: Attendance is not mandatory and will not be counted in the final grade.
Assignments: All the assignments should be submitted with a report in a PDF file, which contains the code and results (with an explanation if needed), and the correct label. You can generate the PDF file using Word or Latex or Rmarkdown or other software. R file or RMD file is optional. Grading is mainly based on the report.
Exams: Both Midterm and Final Exams are taken home. The dates are planned to be the same as the tentative course schedule, any changes will be reflected in the Announcement. The format will be similar to assignments but due in one week.
 30% Homework
 30% Midterm
 40% Final
Late Policy: Assignments received 16 days late will have 20% of the total points deducted. Assignments received more than one week late will receive 0 points. Please send an email before the due date if you need an extension. The extension is usually 2 or 3 days after the due date and should be less than one week.
Lecture Outline
Topic  Reading  

Week 1  R basics(1) Data structures & Loops 
Required: Lecture Notes L1 Optional: The Art of R Prog. Ch.1 
Week 2  R basics(2) Selfdefined functions ”apply” functions 
Required: Lecture Notes L2 Optional: The Art of R Prog. Ch.5 
Week 3  R basics(3) Date and Time objects 
Required: Lecture Notes L3 Optional: The Art of R Prog. Ch.12 
Week 4  R Basic(4) Packages: quantmod 
Required: Lecture Notes L4 
Week 5  Stats in R (1) Generating Random Variables Discrete distribution & Sampling 
Required: Lecture Notes L5 Optional: The Art of R Prog. Ch.8 
Week 6  Stats in R (2) Returns & Moments 
Required: Lecture Notes L6 
Week 7  Stats in R (3) Linear regression Stepwise selection 
Required: Lecture Notes L7 
Week 8  Review Assignment 12  
Week 9  Numerical method in R (1) Bisection method & Newton's Method Gradient Descent  Required: Lecture Notes L8 
Week 10  Numerical method in R (2) BS Model & Implied Volatility 
Required: Lecture Notes L9 
Week 11  Numerical method in R (3) MonteCarlo Simulation 
Required: Lecture Notes 10 
Week 12  Numerical method in R (4) Optimization in R 
Required: Lecture Notes 11 Optional: Optimization in R.pdf 
Week 13  Numerical method in R (5) Interpolation Numerical integration 
Required: Lecture Notes 12 
Week 14  Rmarkdown and Basic LaTeX Review Midterm and Assignment 34 