Course Specification
(IS 421 Data Mining)
University:
Helwan University
Faculty:
Faculty of Computers & Information
Department:
Information systems
1. Course Data
Code:
IS 421
Course title:
Level:
Specialization:
Credit hours:
Number of learning
units (hours):
Data Mining
4
Information systems
3 hours
(3) theoretical (2) practical
2. Course Objective
Knowledge discovery in databases, Data mining process, Data cleaning and
preparation, Mining association rules, Classification, Prediction, Clustering, Web mining,
Applications of data mining, Mining advanced databases.
3. Intended Learning Outcomes:
A. Knowledge and Understanding:
A27. Elaborate Data mining techniques.
B. Intellectual Skills
B9. Design and implement Programming methods.
C. Professional and Practical Skills
D. General and Transferable Skills
D6. Practice Independent Learning techniques.
4. Course contents
Topics
Find association rules
1.1. Market basket analysis, rules, frequent
item sets, A Priori algorithm, scalability
No. of
hours
Lecture
Tutorial/
Practical
6
2
2
Determine clusters
2.1. data vectors, scaling, metric spaces, Kmeans algorithm, finds clusters,
agglomerate into hierarchical clusters
Grow and prune decision trees to classify
3.1. recursive decisions, Gini index and
entropy, prune to avoid over fitting,
scalability
Predict complex linear relationships
4.1. vector spaces, linear regression, cross
validation, scalability
Hyper surfaces model complex decisions
5.1. missing data, decision trees decide
knots, artificial neural nets, scalability
Outlook
6.1. Summaries breadth, connections and
software for data mining
9
3
3
9
3
3
6
2
2
6
2
2
6
2
2
Mapping contents to ILOs
Topic
Find association
rules
1.1. Market basket
analysis, rules,
frequent item sets, A
Priori algorithm,
scalability
Determine clusters
2.1. data vectors,
scaling, metric
spaces, K-means
algorithm, finds
clusters, agglomerate
into hierarchical
clusters
Grow and prune
decision trees to
classify
Predict complex
linear relationships
Hyper surfaces
model complex
decisions
Outlook
Intended Learning Outcomes (ILOs)
Knowledge and
Intellectual Professional
understanding
Skills
and practical
skills
A27
B9
General and
Transferable
skills
A27
B9
D6
A27
B9
A27
B9
A27
D6
5. Teaching and Learning Methods
Lectures
Exercises
Lab Work
6. Teaching and Learning Methods for students with limited capability
Using data show
e-learning management tools
7. Students Evaluation
a) Used Methods
Written Exams to assess Concepts Database Management
Lab to assess understanding of Database Concepts
Assignments to assess understanding of database design concepts.
b) Time
Assessment 1: Test 1
Assessment 2: Test 2
Assessment 3: Midterm Exam
Assessment 4: Practical Exam
Assessment 5: final written exam
Week 4
Week 7
Week 10
Week 14
Week 16
c) Grades Distribution
Mid-term Examination
Final-Year Examination
Semester Work
Practical Exam
Total
Any formative only assessments
List of Books and References
a) Notes
Course Notes
b) Mandatory Books
20 %
50 %
20 %
10%
100%
- Han, J & Kamber, M 2001, Data mining: concepts and techniques, Morgan
Kaufmann, San Francisco.
c) Suggested Books
- CHand, D, Mannila, H & Smyth, P 2001, Principles of data mining, MIT Press,
Cambridge, Mass
d) Other publications
Course Coordinator: Prof. Dr. Ahmed Sharaf
Chairman of the Department: Prof. Dr. Yehia Helmy