FE520 Introduction to Python for Financial Applications



Course Catalog Description


Campus Fall Spring Summer
On Campus X X X
Web Campus X X X

Instructors

Professor Email Office
Zhi Chen zchen100@stevens.edu Babbio Center 110
Zhiyuan Yao zyao9@stevens.edu HFSL Research Room

More Information

Course Description

This course is designed for those students have no experience or limited experience on Python. This course will cover the basic syntax rules, modules, importing packages (numpy, pandas), data visualization, and Intro for machine learning on Python. Students will need to implement what you learn from this course to do a finance-related project. This course aims to get you familiar with Python language, and can finish a simple project with Python.



Course Resources

Textbook

Dive into Python, http://www.diveintopython.net

Python for Data Analysis, Wes McKinney, O'Reilly Media, 2012

Python for Everyone, https://www.py4e.com/

Python official tutorial:, https://docs.python.org/3/tutorial/ (https://docs.python.org/3/tutorial/)

Additional References

Python 3 Object Oriented Programming, Dusty Phillips, Packt Publishing, 2010.

Python for Finance - Analyze Big Financial Data, Yves Hilpisch, O'Reilly Media, 2014



Grading

Grading Policies

  • Homework: 50%
  • Midterm: 20%
  • Final Project: 30% (Report +Presentation)
  • Bonus: 5%

Homework: Homework should be finished by yourself, cheating is zero-tolerate misconduct in Stevens. You will share the score with everyone for first time cheating, and you will be failed for the second time. Copying part of other's work (including p1revious semester) will be recognized as plagiarism as well.

Assignment: You will have at least 10 days to finish each assignment. Late submissions are still accepted but with a penalty: your original score will get 20% off if submitted within 24 hrs, 40% off if submitted between 24 hrs – 48 hrs, 60% off if submitted between 48 hrs – 72 hrs, 80% off if submitted between 72 hrs –96 hrs. You will get 0 after 96 hrs. The assignment portion of the course grade is the average of the four best assignment scores among the five.

Exams: Midterm will be a online quiz, each student will need to answer some questions within specific time (for example: 20 question in 40 minutes). More information will be provided by announcement.

Final Project: You need to choose one topic for your final project from a question list (will be provided around the third week). You are encouraged to use any online resources for your project (but not claim others' project as yours). Please don't limit the scope within the few packages we will introduce in class. Your projects are encouraged to use as much tech skills as you can, and the grades will be evaluated by comparing with the best project. Your submission of this final project can not be projects which you did for other courses, it will be recognized as plagiarism otherwise..

Lecture Outline

Topic Reading
Week 1 Installing Python and IPython Notebook
Week 2 Basic Python Syntax and Data Type Homework 1
Week 3 Dictionary, Loop, and Function
Week 4 Generator, Local/Global variables, Lambda function Homework 2
Week 5 String, Create Module, and Exception Handling
Week 6 Introduction to Class
Week 7 Final Project and Midterm QA Homework 3
Week 8 Numpy Basics Midterm
Week 9 Getting Started with Pandas
Week 10 Pandas II Homework 4
Week 11 Pandas III
Week 12 Time Data Handling and Data Visualization
Week 13 Introduction to Machine Learning Homework 5