CS 504 Principles of Data Management and Mining
| Instructor: | Kevin Molloy |
| Time: | Thursdays from 7:20 pm to 10:00 pm |
| Location: | Art and Design Building 2026 |
| Office Hours: | By appointment (prefer Thursday) |
| Course updates: | course web site |
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.
Announcements:
Description
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.
Disability Statement
If a disability or other condition affects your academic performance, you need to document it with the Office of Disability Services.
Honor Code
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.
Required Textbooks
Data modeling textbooks will be recommended from Safari sometime during the week of Sept 6th.
Optional Textbooks
This textbook will not be directly used in the class, however, if you intend on applying data science to business, it is a good read. We will be covering some of this material in class through other text. The data modeling book is recommended for those that might continue to build/design data marts.
- First Course in Database Systems: by Ullman snd Widow
-
Data Science for Business: What you need to know about data mining and data-analytic thinking [Paperback] by Foster Provost , Tom Fawcett
Grading
| Homeworks |
30% |
| Midterm |
35% |
| Final Project |
35% |