data warehousing and data mining
“Without data, you’re just another person with an opinion.” Edwards Deming, Statistician
“If you torture the data long enough, it will confess.” Ronald H. Coase
Announcements
Please make sure to check this section frequently!
Announcement #1 2/9/2021: This course consists of two parts: Data Warehousing and Data mining. Keep in your mind it is not a Data mining course (Advanced)
About the course
This course provides advanced aspects of data warehousing and data mining (design, management, acquisition, analysis, query, mining, and visualization). The main objective is to focus on serving the informational and analytical needs of the academic and an enterprise. More precisely, the course consists of two main components. The first component is an introduction to data warehousing and the foundation of understating the subjects involved in building and designing a successful data warehouse. The second component covers data mining, which refers to the discovery of interesting and useful knowledge from the data associated with the usage, content, and structure of different data resources. This includes an overview of data mining and knowledge discovery, data mining pre-processes, and data mining tasks.
Instructor
Dr. Mshabab Alrizah
Location: Faculty office 6
Office hours: Monday 9AM-11AM and Thursday 2PM-4PM
Lectures
Class Sunday: 7-8pm: online (Blackboard)
Class Monday: 7-8pm: classroom 3
Class Thursday: 8-10am: lab 3
Teaching Assistants
TBA
Office Location: nan
Office hours: nan
Email: nan
Prerequisites
Database Management Systems 222-CCS-3
Programing Language: Java, R or Python
Textbook(s)
Data Mining: Concepts and Techniques (3rd Edition) by Jiawei Han , Micheline Kamber , and Jian Pei.
Data Warehouse Design: Modern Principles and Methodologies (1st Edition) by Matteo Golfarelli and Stefano Rizzi
(Extra and not a requirement )The Elements of Statistical Learning: Data Mining, Inference, and Prediction ( 2nd edition). 2009. by Trevor Hastie
More resours
TBA
Homework and programming assignments
Assignments #1:
Assignment #2:
Assignment #3:
Assignment #4:
Assignment #5:
Evaluation and grade
Assignments: 5 % (Assignement1, assignement2, …)
Lab assignments: 5% (Lab1, Lab2,….)
Quizzes: 5% (Quize1, Quize2,…)
Lab Quiz: 5%
One Midterm exams: 20%
Lab exam and tutorial: 10%
Final exam: 50%
Schedule
This schedule is subject to change depending on how the course progresses.
| Date | WEEK | Topic | Slides | Assignment/ HW Due | Note |
|---|---|---|---|---|---|
| 12-Sep | 3 | No class: registrations issues | |||
| 13-Sep | 3 | Overview of Data Warehousing (Warm-up) | |||
| 16-Sep | 3 | Overview of the course & Data Warehousing: basic concept | |||
| 19-Sep | 4 | Data warehousing characteristics and OLAP | |||
| 20-Sep | 4 | Data Cube and OLAP | Quiz1 | ||
| 23-Sep | 4 | Holiday | |||
| 21-Sep | 5 | OLAP vs. OLTP | |||
| 22-Sep | 5 | Introducing Dimensional Modeling | |||
| 30-Sep | 5 | Introduction to Lab Section | Assignment 1 | Lab | |
| 23-Sep | 6 | Designing the Dimensional Modeling | Quiz 2 | ||
| 24-Sep | 6 | Processing Advanced Kinds of Queries by Exploring Cube technology | |||
| 7-Oct | 6 | Revision of SQL | Assignment 2 | Lab | |
| 25-Sep | 7 | Dimensional Modeling Case Study | |||
| 26-Sep | 7 | Midterm | Midterm | ||
| 14-Oct | 7 | Data Warehousing Physical design – Implementation in SQL with indexes | Lab Assignment 1 | Lab | |
| 27-Sep | 8 | Long weekend | |||
| 28-Sep | 8 | Long weekend | |||
| 21-Oct | 8 | Dimensional Modeling tutorials – Case Studies | Lab | ||
| 29-Sep | 9 | Introduction to Data Mining and Knowledge Discovery | |||
| 30-Sep | 9 | Pattern Evaluation Methods | Quiz 3 | ||
| 28-Oct | 9 | Introduction, interfaces and use of Wake (We may change Wake to PyCharm and python) | Assignment 3 | Lab | |
| 1-Oct | 10 | Web Mining (Web content) | |||
| 2-Oct | 10 | Data mining with Weka- Data Preprocessing (We may change Wake to PyCharm and python) | |||
| 4-Nov | 10 | Long weekend | |||
| 3-Oct | 11 | Web Mining ( Structure, and Usage Mining) | |||
| 4-Oct | 11 | Data Pre-Processing (Data preparation for knowledge discovery) | |||
| 11-Nov | 11 | Data mining with Weka- Data Preprocessing (We may change Wake to PyCharm and python) | lab Quiz1 | Lab | |
| 5-Oct | 12 | Data Pre-Processing (Data preparation for knowledge discovery) | |||
| 6-Oct | 12 | Data Transformation and Data Discretization | |||
| 18-Nov | 12 | Data mining with Weka- Data Preprocessing (We may change Wake to PyCharm and python) | Assignment 4 | Lab | |
| 7-Oct | 13 | Mining Frequent Patterns | |||
| 8-Oct | 13 | Associates Rules | Quiz 4 | ||
| 25-Nov | 13 | Association Rules mining with Weka Using Apriorism Algorithm (We may change Wake to PyCharm and python) | Lab | ||
| 9-Oct | 14 | Holiday | |||
| 10-Oct | 14 | Holiday | |||
| 2-Dec | 14 | Holiday | |||
| 11-Oct | 15 | Classification and Prediction Clustering | |||
| 12-Oct | 15 | Techniques to Improve Classification Accuracy | |||
| 9-Dec | 15 | Classificational via Decision Tree in Weka (We may change Wake to PyCharm and python) | Lab Assignment 2 | Lab | |
| 13-Oct | 16 | Clustering | |||
| 14-Oct | 16 | Evaluation of Clustering | Quiz 5 | ||
| 16-Dec | 16 | Clustering: K-means Clustering in Weka (We may change Wake to PyCharm and python) | lab quiz2 | Lab | |
| 15-Oct | 17 | Long weekend | |||
| 16-Oct | 17 | Review | |||
| 23-Dec | 17 | Final Lab Assessment | Final Lab Assessment | ||
| 17-Oct | 18 | Final Exam | Final Exam | ||
| 18-Oct | 18 | Final Exam | Final Exam | ||
| 30-Dec | 18 | Final Exam | Final Exam |