FE515 Introduction to R
Course Catalog Description
Introduction
Campus  Fall  Spring  Summer 

On Campus  X  X  
Web Campus 
Instructors
Professor  Office  

Ziwen Ye

zye2@stevens.edu  Altorfer 301 
More Information
Course Resources
Textbook
Lecture Notes and Code
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
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
The plan is to schedule 5 assignments for this semester. The assignments will due exactly before the next class. All LATE SUBMISSION will be punished unless you send me an email BEFORE DUE and get approved. If your submission passes the due for less than 24 hours, your highest score will be 67%; between 24 and 48 hours, your highest score will be 33%; after 48 hours this assignment will be graded as 0. If the assignments I give out is more than 5, the lowest grade will be dropped in final grading calculation.
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.
 30% Assignments
 30% Midterm
 40% Final
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  R basics(2) Selfdefined functions ”apply” functions 
A1 
Week 3  R basics(3) Generating random variables Discreet distribution & Sampling 

Week 4  Date and time objects Simple return and compounded return Plots  A2 
Week 5  Data downloading packages in R: rblpapi & quantmod  
Week 6  Basic statistics and time series  A3 
Week 7  Linear regression models Stepwise selection & goodness criteria  
Week 8  On campus Midterm (10 am to 12 pm)  
Week 9  Ttest and ANOVA Intro. to machine learning (Tentative)  
Week 10  Bisection method, Newton’s method and gradient descent  
Week 11  Realized Volatility & Implied Volatility GBM and BS Model 

Week 12  GGplot  
Week 13  Rmarkdown and Basic LaTeX Transition from R to Python (Tentative) Review session  A6 
Week 14  Advanced Topic (Tentative): Highfrequency data analysis  
Week 15  Oncampus Final exam (10 am to 12 pm) 