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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. Instructor Office:
Engineering Building 4419 Classes Tuesday Prerequisites 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. Grading Quiz: 20% Exams 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 (both available in Safari Books): Data Science
for Business: What You Need To Know About Data Mining and Data-Analytic
Thinking (Foster Provost and Tom Fawcett)
Making Sense of NoSQL: A Guide for Managers and the Rest of Us (Dan McCreary and Ann Kelly) Various reading materials will also be given in class. |
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