FE513 Financial Lab: Database Design
Course Catalog Description
Introduction
Campus | Fall | Spring | Summer |
---|---|---|---|
On Campus | X | X | |
Web Campus | X | X | X |
Instructors
Professor | 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 |