The course addresses the concepts, skills, methodologies, and models of data warehousing.
The course addresses proper techniques for designing data warehouses for various business domains,
and covers concpets for potential uses of the data warehouse and other data repositories
in mining opportunities.
COURSE LEARNING GOALS:
1. Course Objectives:
In today's organization, the data warehouse is the center of the information systems' knowledge repository.
Data warehousing supports informational processing by providing a solid platform of integrated, historical data
from which to perform enterprise-wide data analysis. This helps improve profit and guide strategic decision making
Data mining is a recent advancement in data analysis. Data mining exploits the knowledge that is held in
enterprise data warehouses and other data stores by examining the data to reveal untapped patterns that suggest
better ways to improve quality of product, customer satisfaction and retention, and profit potentials
This course will cover the concepts and methodologies of both data warehousing and data mining.
Data warehousing topics include: modeling data warehouses, concepts of data marts, the star schema
and other data models, Fact and Dimension tables, data cubes and multi-dimensional data, data extraction,
data transformation, data loads, and metadata.
Data mining topics include: concepts and techniques for exploiting untapped business intelligence potentials
from real business model data warehouses
The focus of the course will be on the following topics:
Techniques for Developing Proper Data Warehouses
Data Warehouses and Data Marts and ODS
Dimenstion and Facts tables
The Star Schema and other Data Warehouse models
ETL - Data Extract, Translation, and Load
Retail, inventory and order management data warehousing
CRM, human resouces, and accounting data warehouses
Clickstream and web traffic data warehousing
Data Mining concepts and techniques
2. Student Learning Outcomes:
Understand the purpose of an OLTP (transactional application/database)
Understand the purpose of an ODS (operational reporting database)
Understand the purpose of an DW or DM (data warehouse or data mart)
Learn proper techniques for data normalization and de-normalization
Distinguish between various database design models for OLTP, ODS, and DW
Ability to create well structured dimension tables
Ability to create well structured fact tables
Ability to model STAR and Snowflake schemas
Learn techniques for integrating various DM STAR schemas to create a data warehouse
Clearly articulate the various steps of an ETL process (extract, transform and load)
Understand the various issues of data transformation and integration
Define dimension hierarchies and aggregates facts
Understand the various techniques for data mining
Use data mining tools to help segment/classify your data
The Data Warehouse Toolkit: The Definitive Guide to Dimensional Modeling (3rd Edition)
Authors - Ralph Kimball, Margy Ross
Publisher - Wiley
Teach Yourself SQL in One Hour a Day (5th Edition)
Authors - Ryan Stephens and Ron Plew
Publisher - Sams
Recommended Reading & Materials -
Building the Data Warehouse (4th edition)
Author - William Inmon
Publisher - Wiley
GRADE ASSIGNMENT AND EVALUATION
Contributing factors for determining your course grade include:
Class Attendance & Participation - 15%
Homework - 15%
Team Project - 20%
Midterm Exam - 25%
Final Exam - 25%
Details of Assignment and Evaluation.
Class Attendance and Participation: To receive full credit for class attendance and particitation, you must attend all classes since much of the learning occurs during class presentation and discussions.
You must also participate, engage, and contribute to class discussion during every class session. Please contact the instructor if you anticipate missing any part of the class.
Grades will be based on:
Involvement in class discussions, dialogue and activities
Participation which demonstrates integration of reading, class work, relevance and application.
Willingness to learn by accepting feedback, trying new skills and approaches, etc.
Quality/quantity of providing effective and balanced feedback.
Homework: Homeworks must be submitted on time within 1 week after date assigned.
Late submission will severely impact your homework grade, or may not be accepted altogether at instructor discretion.
All homework pages must be stapled together, no exception (paper clips not accepted)
Print your homework code and resulting output and bring with you to class, or post to NYU Classes as per Professor instructions
I will not accept homework via email unless you are not able to attend the class.
Proper indentation where appropriate is a must. If not properly indented I may return it without grading it.
Class Project Presentation: There will be a group/team class presentation.
The presentation will be a culmination of verbal, visual and presentation skills. It will also be the culmination of topics, concepts and competencies learned in this class.
Midterm Exam: There will be a midterm exam. The exam will be an open book, open notes style exam.
The exam will test the student's acquisition of topics, concepts and competencies learned by midterm.
Final Exam: There will be a final exam. The exam will be an open book, open notes style exam.
The exam will test the student's acquisition of topics, concepts and competencies learned in this class.
The final exam will not be cumulative. It will only cover topics discussed since the midterm.
Grades are FINAL
Please do not negotiate for a better grade. Professor will not provide any "make-up" or "extra credit assignment" to make up for a low grade.
If you are expecting to receive a grade of an "A" at the end of the semester,
then I expect you to study hard, to attend all sessions (unless you previously notify me), to participate in all classes,
to turn in your homework on time, and to keep up with the class reading material.
If you see yourself falling behind do not hesitate to ask for help.
This will ensure that you stay current with the class, and will ensure that you get a good grade on your work.