Download Course Syllabus

SZABIST
Karachi Campus
Course Syllabus
Course Name:
Course Code:
Credit Hours:
Course Prerequisites:
Quarter/Year:
Instructor:
Contact No:
Consultation Hours:
E-mail:
Introduction to Data Mining
3 hours of class per week
Statistical Modeling (MBI)
Bilal Farooq
Use Email
Please Email
[email protected]
1. Course Description:
This course will provide comprehensive introduction to the data mining process;
build conceptual and practical foundations of key data mining techniques such as
association rule mining, logistic regression and clustering. Emphasis will be placed on
the design and implementation of various statistical techniques to perform effective
analysis on the large datasets. The students will get hands on experience through the
implementation of various statistical techniques via several assignments and projects.
2. Course Objectives:
The objective is to learn basic to advance data mining techniques using a statistical
tool R. Focus will be on learning various statistical methods to effectively analyze and
predict.
3. Learning Outcomes:
Become expert data miners.
4. Textbook:
Applied Data Mining (Statistical Methods for Business & Industry) by Paolo Giudici. (Wiley
Student Edition is available locally).
5. Reference Book(s):
Predictive data mining: a practical guide By Sholom M. Weiss, Nitin Indurkhya
6. Teaching and Learning Methodology:
I will teach using primarily power point presentation and white board. From time to time I
will also conduct labs to demonstrate data mining techniques using R.
7. Materials and Supplies:
Scientific calculators and R is required.
Course Name here
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SZABIST
Karachi Campus
8. Grading Policy/Student Assessment:
Assessment Instruments*
Percentage**
Quizzes
Midterm Exam
Project
Final Exam
10 %
30 %
20 %
40 %
9. Attendance Policy:
Prompt arrival and regular attendance are extremely important. Refer to student handbook
for policies on late entry, maximum absences allowed, leave application etc.
10. Expected Class Conduct:
I would expect my students to avoid eating in class, no usage of mobile phones and late
arrivals are unacceptable. I also expect my students to follow strict timelines for submission
of assignments and quizzes etc.
11. Weekly Course Outline:
Session
Session Topic
1
Introduction to Data Mining Process
2
Introduction to Data Mining Process
3
Overview of the Data Mining Process
4
Overview of the Data Mining Process
5
6
7
Data Exploration and Dimension Reduction
Assessments*
Percentage**
Instructor
Evaluation
Data Exploration and Dimension Reduction
Evaluating Classification and Predictive
Performance
8
Mid Term
9
Multi Linear Regression
10
Multi Linear Regression
11
Logistic Regression
Course Name here
Mid-Term
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30%
SZABIST
Karachi Campus
12
Logistic Regression
13
Survival Analysis
14
Survival Analysis
15
Cluster Analysis
16
Final Exam
Final Exam
12. Students with Physical or Educational Challenges:
Students with educational and/or physical challenges are entitled to extra attention and time
from the instructor. Therefore students are advised to notify the course instructor at the
beginning of the course. Special arrangement may also be made on prior request based on
specific challenges.
13. Academic Integrity
This course seeks to empower students for independent learning, resourcefulness, clear
thinking, and perception. All submitted work and activities should be genuine reflections of
individual achievement from which the student should derive personal satisfaction and a
sense of accomplishment. Plagiarism and cheating subvert these goals and will be treated
according to the policy stated in the Student Handbook. The instructor reserves the right to
utilize electronic means to help prevent plagiarism.
14. Instructor’s Course Portfolio:
The Instructor’s Course Portfolio (ICP) folder must be carried and maintained weekly (it
must include all work pertaining each respective week) by the course instructor over the
period of the semester. This folder must be submitted to the faculty coordinator at the end
of the course and should include the following items:
a. Course Syllabus
b. Presentation Slides
c. Handouts
d. Lecture Notes
e. Reading Material/assignments
f. Homework
g. Projects
h. Exams
i. Quizzes
j. Final Exam
k. Model answers for all assessments
l. Any other material pertaining to the course should also be included into the portfolio
folder.
15. Comments and/or Suggestions:
Course Name here
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SZABIST
Karachi Campus
Students and Instructors may contact the Institutional Research Department if there is a
need to make suggestions or comments that can help further improve the course. A link is
also provided on your ZABDESK account for frequent and trouble-free feedback. The
Institutional Research Department would like to hear your feedback about the following:
Students
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Course Content/ thoroughness
Lecture Delivery/Supplementary Material
Facilities/Labs/Software/Hardware Support
Course alignment with learning outcomes
Any other comments/feedback
Course Name here
Instructors
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Availability of teaching material
Facilities/Internet/Administrative Support
Labs: Software/Hardware/Technical support
Availability and quality of Teaching Instruments
Any other comments/feedback
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