FE515 Introduction to R
Course Catalog Description
Introduction
Campus | Fall | Spring | Summer |
---|---|---|---|
On Campus | X | X | |
Web Campus |
Instructors
Professor | Office | |
---|---|---|
Yunfan Zhu
|
yzhu25@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. ISBN-10: 1593273843, ISBN-13: 978-1593273842
An Introduction to Analysis of Financial Data with R. Ruey Tsay, First Edition, 2012. ISBN-10: 0470890819, ISBN-13: 978-0470890813
Introduction to the Practice of Statistics. David S. Moore, George P. McCabe, Bruce A. Craig, Eighth Edition, 2014. ISBN-13: 978-1464158933, ISBN-10: 1464158932
Additional References
CRAN: http://www.wikibooks.org
R-help Info: https://stat.ethz.ch/mailman/listinfo/r-help
R-help 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) Special Attention: Course attendance is not counted in final grade. However, if your over-all attendance is less than 50%, you will fail this course automatically.
Lecture Outline
Topic | Reading | |
---|---|---|
Week 1 | R basics(1) Data structures & Loops |
|
Week 2 | R basics(2) Self-defined functions ”apply” functions |
A1 |
Week 3 | R basics(3) Generating random variables Discreet distribution & Sampling |
|
Week 4 | Date and time objects Plots | A2 |
Week 5 | Data downloading packages in R: rblpapi & quantmod | |
Week 6 | Returns and Moments | A3 |
Week 7 | Linear regression models Stepwise selection & goodness criteria | |
Week 8 | On campus Midterm (10 am to 12 pm) | |
Week 9 | More on linear regression | A4 |
Week 10 | Bisection method, Newton’s method and gradient descent | |
Week 11 | Realized Volatility & Implied Volatility GBM and BS Model |
A5 |
Week 12 | Optimization in R | |
Week 13 | Interpolation Numerical integration | A6 |
Week 14 | Rmarkdown and Basic LaTeX | |
Week 15 | On-campus Final exam (10 am to 12 pm) |