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
Email: jessica [AT] gmu [DOT] edu
Office Hours: Tuesday 1:30-3:30pm or by appointment
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.
There will be 4 or 5 quizzes, a midterm exam and a final exam covering lectures and readings. 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.
Data Science for Business: What You Need To Know About Data Mining and Data-Analytic Thinking by Foster Provost and Tom Fawcett
NoSQL Distilled: A Brief Guide to the Emerging World of Polyglot Persistence by Pramod J. Sadalage and Martin Fowler
Both books are available on Safari Books for free with your GMU account. More reading materials will be given in class.
The GMU Honor Code is in effect at all times. In addition, the CS Department has further honor code policies regarding programming projects, which are detailed here. Any deviation from the GMU or the CS department Honor Code is considered an Honor Code violation. All assignments for this class are individual unless otherwise specified.
If you have a documented learning disability or other condition which may affect academic performance, make sure this documentation is on file with the Office of Disability Services and then discuss with the professor about accommodations.