CS 504 - Principles of Data Management and Mining

Dr. Jessica Lin

Spring 2014


Course Description (From Catalog)

Techniques to store, manage, and use data including databases, relational model, schemas, queries and transactions. On Line Transaction Processing, Data Warehousing, star schema, On Line Analytical Processing. MOLAP, HOLAP, and hybrid systems. Overview of Data Mining principles, models, supervised and unsupervised learning, pattern finding. Massively parallel architectures and Hadoop.


Dr. Jessica Lin

Office: Engineering Building 4419
Phone: 703-993-4693
Email: jessica [AT] cs [DOT] gmu [DOT] edu
Office Hours:  Tuesday/Thursday 2-3pm


Robinson Hall B220


Graduate Standing

Note: This course cannot be taken for credit by students of the MS CS, MS ISA, MS SWE, MS IS, CS PhD or IT PhD programs.


Quiz: 20%
Homework/Class Participation: 20%
Midterm: 25%

Final: 35%


There will be 4 or 5 quizzes, a midterm exam and a final exam covering lectures and readings (in class, closed book). The final exam is comprehensive. With the exception of the quizzes, which must be taken at the time they are given, prior arrangement needs to be made with the instructor if you cannot make it to the exam. Missed exams cannot be made up.

Honor Code Statement

Please be familiar with the GMU Honor Code. In addition, the CS department has its own Honor Code policies. Any deviation from this is considered an Honor Code violation. 

Disability Accommodations

If you are a student with a disability and you need academic accommodations, please see me and contact the Office of Disability Services (ODS) at 993-2474, http://ods.gmu.edu. All academic accommodations must be arranged through the ODS.


Required: Data Science for Business: What You Need To Know About Data Mining and Data-Analytic Thinking
Purchase options: The bookstore will have the eBook for $15.50. If you want the printed version (recommended), google the book and you'll see various purchase/rent options, e.g. buy or rent from Amazon, Barnes & Noble, etc.

Various reading materials will also be given in class.

Tentative Schedule

Date Topic
1 1/21
Introduction to Database Management
ER Model 1
2 1/28
ER Model 2
Relational Model 1
3 2/4
Relational Model 2
4 2/11 SQL 1
5 2/18 SQL 2
6 2/25
Midterm Review
7 3/4 Midterm
8 3/11 Spring Break
9 3/18 Post-midterm Review
Data Warehouse
10 3/25 NoSQL / MapReduce
11 4/1 Data Mining 1
12 4/8 Data Mining 2
13 4/15 Data Mining 3
14 4/22 Data Mining 4
15 4/29 Final Review
16 5/6 No Class
Final Exam (4:30-7:15pm)

Course Website