FE520 Introduction to Python for Financial Applications

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Course Catalog Description


Campus Fall Spring Summer
On Campus X X
Web Campus


Professor Email Office
Dan Wang
dwang35@stevens.edu Babbio Center 209
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 basis syntax rules, modules, importing packages (Numpy, pandas), data visualization, and Intro for machine learning on Python. You 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


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

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

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

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 Policies

Quizzes (Weekly): 60%

Final Project: 40% (Report +Presentation)

Final Project: 10%

Homework should be finished by yourself, Cheating is 0 tolerate misconduct in Stevens. You will share the score with everyone for first time cheating, and you will be fail for second time. There are only two requirements for the project: (i) you must use Python, (ii) project must be finance-related. You are encouraged to use any online resources for your project. 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.

Lecture Outline

Topic Reading
Week 1 Installing Python and IPython Notebook
Week 2 Basic Python Language I
Week 3 Basic Python Language II
Week 4 Basic Python Language III
Week 5 Intro to useful standard library
Week6 Numpy,Scipy Basics
Week 7 Getting Started with pandas
Week 8 Plotting and Visualization
Week 9 Time Series I
Week 10 Financial and Economic Data Applications
Week 11 Introduction to Machine Learning I
Week 12 Introduction to Machine Learning II
Week 13 Basic design pattern
Week 14 Final Presentation