Issue Date:11/7/2021 issue:02
ZU/QP10F004
Faculty: Information Technology
Program: Bachelor Program Department: Data science
and AI
Semester:
Academic year:
Course Plan
First: Course Information
Credit Hours: 3 Course Title:
Data Mining and data warehousingCourse No.
1505355
Lecture Time:
Section No.: 1 Prerequisite:
1501222
Obligatory Faculty Requirement Elective University Requirement Obligatory University Requirement Faculty Requirement
Course Elective Specialty Requirement Obligatory Specialization requiremen
tType Of Course:
Face-to-Face Learning
Blended Learning (2 Face-to-Face + 1Asynchronous) Online Learning (2 Synchronous+1 Asynchronous) Type of
Learning:
Second: Instructor’s Information
Academic Rank:
Name:
E-mail:
Ext. Number:
Office Number:
Sunday Tuesday Thursday Office Hours:
Third: Course Description
Knowledge discovery fundamentals, data mining concepts and functions, data pre-processing,
data reduction, mining association rules in large databases, classification and prediction techniques,
clustering analysis algorithms, data visualization, mining complex types of data (text mining,
multimedia mining, Web mining … etc), data mining languages, data mining applications and new
trends.
Issue Date:11/7/2021 issue:02
ZU/QP10F004
Fourth: Learning Source
Data Mining: Concepts and Techniques Main Reference:
Publication Year: 2012 Issue No.: 3
rdAuthor: Han, J. and Kamber,M
Data Mining Techniques – Arun K Pujari,2nd edition, Universities Press.
Data Warehousing in the Real World – Sam Aanhory & Dennis Murray Pearson Edn Asia.
Insight into Data Mining,K.P.Soman,S.Diwakar,V.Ajay,PHI,2008.
Data Warehousing Fundamentals – Paulraj Ponnaiah Wiley student Edition
Additional
Sources&Websites :
Classroom Laboratory Workshop MS Teams Moodle Teaching Type:
Fifth: Learning Outcomes
Connection To Program ILOs Code Course Intended Learning Outcomes (CILOs)
Course Code
Knowledge
*PK1, PK2, PK4 Concepts and Theories:
Overview of the basic data mining concepts.
List the basic terminologies of a data mining.
**K1
Contemporary Trends, Problems and Research: PK3
List and overview of the data mining advantages.
K2
PK3, PK4 Professional Responsibility
K3
Skills Problem solving skills: PS1
Learn how to handle different types of the data types.
***S1
PS2,PS4 Modeling and Design
Learn how to design a data-mining model.
Learn how to build sample test cases for a data mining system.
Learning how to apply its concepts with real-world examples.
S2
PS2, PS3 Application of Methods and Tools
Take an idea about association rules such as a Road Map Frequent Item set Mining Methods and the Apriority Algorithm.
S3
Competencies Overview the importance of data reduction PC1 Distinguish between data visualization approach.
****C1
PC2 Understanding idea of the data warehousing and how to prepare to
build it, and OLAP technology.
Understanding the concept of cluster analysis and apply this concept in real data
C2
PC2, PC5 Analyze and investigate the data mining models and design.
C3
PC3,PC4 Communication
C4
PC1,PC2,PC5 Discuss several case studies and solving real-world problems through
simple projects C5
* P: Program, **K: knowledge, ***S: skills, ****C: competencies.
Issue Date:11/7/2021 issue:02
ZU/QP10F004
Sixth: Course Structure
Lecture Date
Intended Teaching Outcomes(ILOs)
Topics Teaching Procedures*
Teaching
Methods** References***
Week 1
K1 What is DM
Face to face
Lecturing , quizzes and assignments
Slides on the Moodle Part 1
K1 Why DM?
Face to face
Lecturing ,quizzes and assignments
Slides on the Moodle Part 1
K1 DM Task
Face to face
Lecturing , quizzes and assignmentsSlides on the Moodle Part 1
Week 2
K1, K2, K3 Motivating Challenges
Face to face
Lecturing , quizzes and assignmentsSlides on the Moodle Part 1
K1, K2, K3 Attributes and Objects
Face to face
Lecturing , quizzes and assignmentsSlides on the Moodle Part 1
S1, K1, K2 Types of Data
Face to face
Lecturing , quizzes and assignmentsSlides on the Moodle Part 1
Week 3
S1, K1, K2 Data Quality
Face to face
Lecturing , quizzes and assignmentsSlides on the Moodle Part 1
S1,S2, K1, K2 Data
Preprocessing
Face to face
Lecturing , quizzes and assignmentsSlides on the Moodle Part 2
S1,S2, K1, K2,K3 Data Preprocessing
Face to face
Lecturing , quizzes and assignmentsSlides on the Moodle Part 2
Week 4
S1,S2,S3, K1, K2,K3 Data Preprocessing
Face to face
Lecturing , quizzes and assignmentsSlides on the Moodle Part 2
S1,S2,S3, K1, K2,K3 What is Statistics
Face to face
Lecturing , quizzes and assignmentsSlides on the Moodle Part 2
C1, S1,S2,S3, K1, K2,K3
Types of Sampling
Face to face
Lecturing , quizzes and assignmentsSlides on the Moodle Part 2
Week 5
C1, S1,S2,S3 Descriptive Statistics
Face to face
Lecturing , quizzes and assignmentsSlides on the Moodle Part 3
C1, S1,S2,S3 Descriptive Statistics
Face to face
Lecturing , quizzes and assignmentsSlides on the Moodle Part 3
C1, S1,S2,S3 Descriptive Statistics
Face to face
Lecturing , quizzes and assignmentsSlides on the Moodle Part 3
Week 6
C1, C2, S1,S2,S3 Probabilities
Face to face
Lecturing , quizzes and assignmentsSlides on the Moodle Part 3
C1, C2, S1,S2,S3 Probabilities
Face to face
Lecturing , quizzes and assignmentsSlides on the Moodle Part 3
Issue Date:11/7/2021 issue:02
ZU/QP10F004
C1, C2, S1,S2,S3 Probabilities
Face to face
Lecturing , quizzes and assignmentsSlides on the Moodle Part 3
Week 7
C1, C2, C3, S1,S2 Classification:
Decision Tree
Face to face
Lecturing , quizzes and assignmentsSlides on the Moodle Part 4
C1, C2, C3, S1,S2 Classification:
Decision Tree
Face to face
Lecturing , quizzes and assignmentsSlides on the Moodle Part 4
C1, C2, C3, S1,S2 Classification:
RandomForest
Face to face
Lecturing , quizzes and assignmentsSlides on the Moodle Part 4
Week 8
C1, C2, C3, C4 Linear Regression
Face to face
Lecturing , quizzes and assignmentsSlides on the Moodle Part 4
C1, C2, C3, C4 Linear Regression
Face to face
Lecturing , quizzes and assignmentsSlides on the Moodle Part 5
C1, C2, C3, C4 Linear Regression
Face to face
Lecturing , quizzes and assignmentsSlides on the Moodle Part 5
Week 9
C1, C2, C3, C4,C5 Association Analysis
Face to face
Lecturing , quizzes and assignmentsSlides on the Moodle Part 5
C1, C2, C3, C4,C5
Mining Association
Rules
Face to face
Lecturing , quizzes and assignmentsSlides on the Moodle Part 5
C1, C2, C3, C4,C5
Mining Association
Rules
Face to face
Lecturing , quizzes and assignmentsSlides on the Moodle Part 5
Week 10
C1, C2, C3, C4,C5
Mining Association
Rules
Face to face
Lecturing , quizzes and assignmentsSlides on the Moodle Part 5
C1, C2,C3, C4 , C5 K-mean
Clustering
Face to face
Discussion Slides on the Moodle C1, C2,C3, C4 , C5 K-meanClustering
Face to face
Discussion Slides on the MoodleWeek 11
C1, C2,C3, C4 , C5 Data warehousing
Face to face
Discussion Slides on the Moodle C1, C2,C3, C4 , C5 Datawarehousing
Face to face
Discussion Slides on the Moodle C1, C2,C3, C4 , C5 Data MartsFace to face
Discussion Slides on theMoodle
Week 12
C1, C2,C3, C4 , C5 Data Marts
Face to face
Discussion Slides on the Moodle C1, C2,C3, C4 , C5 Data CentersFace to face
Discussion Slides on theMoodle C1, C2,C3, C4 , C5
Data Centers
Face to face
Discussion Slides on the MoodleWeek 13
C1, C2,C3, C4 , C5 Revision
Face to face
Discussion - C1, C2,C3, C4 , C5 RevisionFace to face
Discussion - C1, C2,C3, C4 , C5 RevisionFace to face
Discussion -* Learning procedures: (Face-to-Face, synchronous, asynchronous). * * Teaching methods: (Lecture, video…..). ** * Reference: (Pages of the book, recorded lecture, video….).
Issue Date:11/7/2021 issue:02
ZU/QP10F004
Seventh: Assessment methods
Methods Face-To-Face
Learning
Measurable Course (ILOs)
First Exam Second Exam
Mid-term Exam 35
K1, S1, S2, S3, S4, S5, C1Participation 15
K1, K2, K3, S1, S2, S3, S4, S5, C1, C2, C3Asynchronous Activities 0
Final Exam 50
K1, K2, K3, S1, S2, S3, S4, S5, C1, C2, C3, C4Eighth: Course Policies
All course policies are applied on all teaching patterns (online, blended, and face- to-face Learning) as follows:
a. Punctuality.
b. Participation and interaction.
c. Attendance and exams.
Academic integrity: (cheating and plagiarism are prohibited).
