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# FE515 Introduction to R
- URL: https://fsc.stevens.edu/fe515-introduction-to-r/
- Published: 2019-10-21T15:29:05.000Z
- Updated: 2026-10-09T19:22:28.000Z
- Author: Arjun Sivakumar
- Tags: Courses, #Import 2024-05-30 10:26, #Import 2025-11-08 22:14

Course Catalog Description

# Introduction

In this course the students will learn the basics of the open source programming language R. The language will be introduced using financial data and applications. Basic statistical knowledge is required to complete the course. The course is designed so that upon completion the students will be able to use R for assignments and research using data particularly in finance. 

  
| Campus     | Fall | Spring | Summer |
| ---------- | ---- | ------ | ------ |
| On Campus  | X    | X      |        |
| Web Campus | X    | X      |        |

  
Instructors

| Professor    | Email               | Office     |
| ------------ | ------------------- | ---------- |
| Sean O'Leary | soleary@stevens.edu | Babbio 640 |

  
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\. 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

**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.

- 35% Homework
- 25% Midterm
- 35% Final
- 5% Attendance

 **Late Policy:**  Assignments received 1-6 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)Self-defined 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 1-2                                                          |                                                                |
| 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)Monte-Carlo 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 3-4                   |                                                                |