FE513 Financial Lab: Database Design



Course Catalog Description

Introduction

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 or other programs.

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

Instructors

Professor Email Office
Yangyang Yu
yyu8@stevens.edu Hanlon Financial Systems Lab (Babbio 4th floor) and Blackboard Collaborate(Online)

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 Outcomes

At the end of this course, students will be able to:

1. Use R to scrape, clean, and process data..

2. Use database to store data locally.

3. Use statistical methods and visualization to quickly explore data.

4. Apply statistics and computational analysis to make predictions based on data.

5.Effectively communicate the outcome of data analysis using descriptive statistics and visualization.


Course Resources

Textbook

None. Instead, we have a list of recommended readings.

Additional References

There will be a list of recommended readings.


Grading

Grading Policies

Your final grade will be determined by the number of points you collect:

  • 60% Homeworks
  • 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 course
Week 2 Basic R programming, Usage of Packages and functions
Week 3 R I: Conditional Statements and Loops Assignment I Publish
Week 4 R II: Functions and Visualization
Week 5 SQL I: Create table, Input data, Output data
Week 6 SQL II: Basic selection clauses and subquery
Week 7 Connect R with PostgreSQL, R APIs Assignment I Due & Assignment II Publish
Week 8 No Class
Week 9 Better Query
Week 10 Database Design I
Week 11 Database Design II Assignment II Due & Assignment III Publish
Week 12 MongoDB I
Week 13 MongoDB Design
Week 14 HADOOP and Big Data
Week 15 Exercises Assignment III Due & Final Exam Publish(Take home)
Week 16 No Class Final Exam Due