hfsl_course

FE513 Financial Lab: Database Design
Financial Engineering (Lab)
Tue 8:00p.m. - 9:00p.m.
Hanlon Lab 1
Tue 8:00 - 5p.m.
  • Fall On Campus
  • Fall Online
  • Spring On Campus
  • Spring Online
The course provides an introduction to SQL databases and data mining techniques as available to the Hanlon Financial Systems Lab. At the end of the course the students will be familiar with all the lab data resources as well as a working knowledge on how to use them. The students will receive hands on instructions 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 and R. The course will cover accessing databases using API, downloading data using API, working on basic data mining techniques.

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.

After taking this course, the students will be able to: Will be able to extract certain data from database using SQL.  Will get basic knowledge about programming in R. Will understand basic data mining concepts (clustering & classification) and be able to implement them in R. Will get basic knowledge about data visualization using R. Will get basic knowledge about big data analysis.

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

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. 

Week

Topic 

Homework

1

Intro to course, working environment setup

 
2

Basic R programming, Usage of Packages and functions 

Assignment I Publish 

3

R I: Conditional statements and loops 

 
4

R II: functions and visualization 

 
5

SQL I: create table, Input data, Output data 

 
6

SQL II: Basic selection clauses and subquery 

Assignment I Due

Assignment II Publish 

7

Connect R with PostgreSQL & R APIs 

 

 
8

Time series analysis in R & Classification and Clustering in R 

 
9

Text mining in R 

 
10

Social-network as graphs in R

Assignment II Due

Assignment III Publish 

11

MongoDB I: structure of MongoDB 

 
12

MongoDB II: query 

 
13

HADOOP and Big Data Practice 

 
14

Final exam 

Assignment III Due