FE515 Introduction to R
Course Catalog Description
Introduction
Upon completion the students will gain an understanding of the programming syntax and should be able to use R in any future courses.
Campus  Fall  Spring  Summer 

On Campus  X  X  
Web Campus 
Instructors
Professor  Office  

Ziwen Ye 
zye2@stevens.edu  Altorfer 301 
Course Resources
Textbook
 The art of R programming: a tour of statistical software design. Norman Matloff, 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
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
For this course, all students will have the midterm and final exams. Both exams are 2 hours length and will be held during the class. As a coding class, we only test the coding skill from students. Therefore, both exams will be open book. Students can use any materials during exams (such as notes, Google search engine and etc.) to help them answer all questions. However, any communication tools (such as Skype, email and etc.) and tutoring websites are NOT allowed.
If students have any concern or questions regarding to the teaching contents and homework, they are encouraged to seek help from the instructor. Discussing homework with classmates are prohibited for this course. All code and reports must be written by yourself. Copying solutions from sources other than your brain is strictly forbidden. This kind of behavior will be considered as academic dishonesty/misconduct and will be dealt with according to the Stevens honor board policy.
Grade distribution
Assignments – 30% Midterm – 30% Final – 40% Bonus – TBD (Bonus includes but not limited to attendance and bonus questions)
Lecture Outline
Topic  Reading  

Week 1  R basics(1)
Data structures & Loops 

Week 2  Labor Day, No Classes  
Week 3  R basics(2)
Selfdefined functions ”apply” functions 

Week 4  R basics(3)
Generating random variables Discreet distribution & Sampling 

Week 5  Date and time objects
Simple return and compounded return Plots 

Week 6  Download data through R:
Bloomberg API, Yahoo API (Equity and option) 

Week 7  Yahoo API (advanced)
Basic statistics 

Week 8  Linear regression models
Stepwise selection & goodness criteria 

Week 9  Midterm  
Week 10  Ttest and ANOVA  
Week 11  Newton’s method and gradient descent  
Week 12  Volatility
GBM and BS Model 

Week 13  GGplot  
Week 14  Rmarkdown and LaTeX 