FE513 Financial Lab: Database Design

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Course Catalog Description

Introduction

The course provides a practical introduction to SQL databases and Hadoop cluster systems as available in the Hanlon Financial Systems Lab. Students will receive hands on instruction about setting up and working with databases. Most of the software will be introduced using case studies or demonstrations, followed by a lecture of related fundamental knowledge. The course covers SQL, NoSQL, and database management systems. The course will cover accessing databases using API.
Campus Fall Spring Summer
On Campus X X
Web Campus X X

Instructors

Professor Email Office
Xingjia(Lauren) Zhang
xzhang21@stevens.edu Babbio 5th floor (in front of Room 547)



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

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

• Use database to store data locally.

• Use statistical methods and visualization to quickly explore data.

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

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


Course Resources

Textbook

None. All lectures videos will be posted online and should be available 48 hours after meeting time.

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: Homework: 60% , Final: 40%

• 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 Intro to course and Working environment setup
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 8 No class
Week 7 Connect R with PostgreSQL, R APIs Assignment I Due & Assignment II Publish
Week 9 Text mining in R
Week 10 Time series analysis in R , Classification and Clustering in R
Week 11 Database Design I Assignment II Due & Assignment III Publish
Week 12 Database Design II
Week 13 MongoDB
Week 14 HADOOP and Big Data Assignment III Due