FE555 2D Data Visualization Programming for Financial Applications
Course Catalog Description
Introduction
Campus | Fall | Spring | Summer |
---|---|---|---|
On Campus | X | X | |
Web Campus | X | X |
Instructors
Professor | Office | |
---|---|---|
Xiaodi Zhu | xzhu@stevens.edu | Altofer 301 |
More Information
Course Description
Welcome to FE513! The course aims to introduce the required techniques and fundamental knowledge in data science techniques. It helps students to be familiar with database and data analysis tools. Students will be able to manage data in database and solve financial problems using R program packages. This course is designed for graduate students in the Financial Engineering program at the School of Business.
Course Outcome
After taking this course, the students will be able to: Will be able to extract certain data from database using SQL. Will get basic knowledge about programming in R. Will understand basic data mining concepts (clustering & classification) and be able to implement them in R. Will get basic knowledge about data visualization using R. Will get basic knowledge about big data analysis.
Course Resources
Textbook
None. Instead, we have a list of recommended readings.
Additional References
Grading
Grading Policies
- Your final grade will be determined by the number of points you collect.
- 60% Homework
- 40% Final
It is very important to us that all assignments are properly graded. If you believe there is an error in your assignment grading, please submit an explanation via email me within 7 days of receiving the grade. No regrade requests will be accepted orally.
This course has a zero tolerance policy for academic dishonesty, and anyone caught will immediately receive an F for the course grade. You may not under any circumstances give a copy of your code to another student, or use another students’ code to help you write your own code.
Identical assignments not only include 100% identical works, but also include works with different variable names and comments but the same logic, code style, etc..
Due dates are firm. Late submission will not be accepted under any circumstance without prior notice and permission from the instructor. At least 20% Points will be deducted for late submission without notice. For full-time students, excuses such as "busy for on-campus job", "preparing for interview", "working on other courses" are not accepted. For part-time students, please notice the instructor in prior if you have "heavy work load", "business travel", "business meeting", etc. which may affect the homework submission.
Lecture Outline
Topic | Reading | |
---|---|---|
Week 1 | Introduction to Financial Engineering | Ch. 1 and 2 |
Week 2 | Capital Markets Overview | Ch. 3 |
Week 3 | Corporate Finance & Valuation | Ch. 3 |
Week 4 | Equity Analysis | Ch. 4 |
Week 5 | Fixed Income Debt Securities | Ch. 4 |
Week 6 | Overview of Bonds Sectors & Instruments | Ch. 4 |
Week 7 | Valuation of Debt Securities | Ch. 4 |
Week 8 | Securitized Products | |
Week 9 | Leveraged Loans & CLO's | Ch. 5 |
Week 10 | General Principles of Credit Analysis | Ch. 5 |
Week 11 | Foreign Exchange | Ch. 6 |
Week 12 | Poisson Processes and Jump Diffusion | Ch. 11 |
Week 13 | Exotic Options | Ch. 7 |
Week 14 | Review & Catch-up |