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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
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
Classes
Tuesday
4:30-7:10pm
Robinson Hall B220
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%
Homework/Class Participation: 20%
Midterm: 25%
Final: 35%
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.
Textbooks
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
Week
|
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
|
17
|
5/13
|
Final
Exam (4:30-7:15pm) |
Course Website
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