‫الجامعة اللبنانية‬
‫كلية العلوم االقتصادية‬
‫وإدارة األعمال‬
‫الفرع األول‬
DATA ANALYSIS
Course Syllabus
Instructor:
Course Language:
Class Hours:
Section:
Office Hours:
E-mail:
Dr. A. ELMOUSSAOUI
English
M 4:00 – 6:00
Master II
In Appointment
ali.elmoussaoui@ul.edu.lb
I - Course Description, Objectives and content:
The course will focus on the knowledge and skills to select, apply and evaluate data analysis
and data mining techniques which discover knowledge that can add value to a company.
Students will gain both an in-depth theoretical understanding and practical hands-on
experience, including implementing novel and emerging techniques. Participants will be kept
abreast of current research and state of the art in data analytics related topics.
II - Course Objectives:
1. Practical analytical and technical skills that differentiates you in any modern
enterprise
2. Understands the scientific method of data analysis and data mining.
3. Strategic aspects and business value of data analytics
4. Data capture, validation, reduction, analysis, insights and recommendations
5. Demonstrate the ability to review appropriate literature.
6. In depth expertise in techniques and methods of classification, prediction, and
association
7. Real world data analytic and business intelligence applications.
8. Demonstrate the ability to use SPSS and advanced SPSS in multivariate data
analysis.
III - Means to Accomplish Objectives:
The student will be required to:
1. Participate in class discussion,
2. Attend the Business Computer Center,
3. Take Midterm exam,
4. Take final exam, and
5. Conduct one group project. This project will be prepared in accordance with the
formatting guidelines of the American Psychological Association documented in
Publication Manual of the American Psychological Association. The student will
select a research problem and prepare and turn in a prospectus as follows:
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i. The term project is to develop a finished miniature "Prospectus for
a Thesis." The student is referred to APA for the mechanical
details of a prospectus preparation:
http://www.socialresearchmethods.net/kb/formatting.php
ii. Approval of the research topic by Dr. ElMoussaoui can be
obtained at any time. For approval, each student must submit the
following information:
a. Proposed research topic.
b. Need (significance) for the study
c. Purpose of the study.
d. Statement of the research problem.
e. Preliminary statement of research hypothesis.
f. Preliminary statement of methodology.
IV - COURSE STRUCTURE
Introduction
1. Scope
2. Data
3. Introduction to Data Mining
Descriptive data analysis (SPSS application)
1.
2.
3.
4.
Measurement of Distribution
Measurement of Location (Central Tendency)
Measurement of Dispersion
Graphs an Charts
Inferential Statistical and Tests (Application of SPSS)
1. Test of the average
2. Ki-square test
3. Anova test
Prediction Statistical and Tests (Application of SPSS)
1. Correlation
2. Regression
Data Reduction
1. Factor Analysis
2. Principle Component Analysis
3. Reduction of the size and selection of attributes
Data Classification
1. Discriminant Analysis
Clustering
1. The method K-Means Clustering
2. Hierarchical Clustering
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V - ASSESSMENT
The assessment of students will be divided into two parts: written exams and
continuous assessments
1. Exams: Written exams consist of one partial exam with 30% and one final at the
end of the course with 50%.
2. In course assessment: comprises of written individual or group assignment
(10%) which will be presented by students groups in the last 10 minutes of each
lecture , and individual student’s contribution (10%)
VI - Main text book
 Business Research Methods, 8 edition by William G. Zikmund.
 SPSS for Intermediate Statistics 4th edition by Nancy Leech, Karen C.
Barrett, George A. Morgan.
 A Step-by-Step Guide for SPSS and Exercise Studies by Nikos Ntoumanis.
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