University of La Verne
College of Business and Public Management
Master’s in data Analytics
Problem Solving Approaches
MDA 572
Instructor & Class Specific Information:
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Name: Dr. Yehia Mortagy
Contact Information:
o E-mail: ymortagy@laverne.edu. Some send their e-mail to the wrong address,
please copy this address and use it.
o Phone # and text messages:
Office: 909-448-4556
o Office Location: Landis 224-C
Class Sessions Time & Location: Tuesday 6:00-9:50 pm; CBPM 142
Office Hours:
o Monday & Wednesday 11:05 - 11:40 am & 1:50 – 3:30 pm
o By appointment in the office or using Zoom. Please schedule time with
instructor. Zoom link is https://ulv-edu.zoom.us/j/4310521436
o I am available during regular day time, and on class day from 5:00 to class time.
Considering that many have full time jobs, we can always meet using zoom
based on our availabilities. Please send an e-mail or a text message so we can
schedule a Zoom meeting. Simple questions can be answered by a text message
or a phone call
Course Objectives:
At the end of the course students will be able to
1- Students will remember the importance of creating monitoring system including
appropriate metrics to track - e.g., critical success factors, or KPI - and discover possible
problems.
2- Students will identify and analyze the problems to determine causes, type of problem.
3- Students will Implement several problem identifications approaches to recognize a
problem to determine the causes.
4- Students will understand the type of problems they are facing.
5- Students will determine the appropriate problem-solving approach and implement it
(e.g., optimization, forecasting & predictive analytics)
Course Topics:
What is Problem Solving Models
 Definition of Problem
 Definition of Problem Solving
 Definition of Decision Making
Approaches
 Analysis
o Root Cause
o Visualization
o Pareto Principle
 Descriptive: How we make decisions
o Simon’s model
o Heuristics
o Scripts (Schank)
o Alison, Graham
 Prescriptive Approaches
o Linear Programming
o Forecasting
 Regression Analysis
 Multi-variable
 Non-Linear
 Logistic Regression
o Decision Trees & CART
 Predictive (Data Mining)
o Supervised
o Unsupervised
o Evolution of AI
Textbook and other material
Text
 Jaggia, Kelly, Lertwachara, Chen “Business Analytics; communications with numbers”
McGraw Hill, latest edition.
 Hyndman, R. & Athanasopoulus, G. “Forecasting, Principles and Practice” 2nd edition, O-Text
online open access textbooks, OR “Forecasting: Principles and Practice”; by Rob J Hyndman
and George Athanasopoulos. Monash University, Australia
Others: (not required)
 Allison, Graham “Essence of Decision”. A classic textbook but is difficult to find. La Verne
library has a copy. I, too, have a copy that I can check out for a limited number of days.
 “Business Forecasting: Practical Problems and Solutions”; by Michael Gilliland, Len
Tashman, Udo Sglavo
 “Forecasting Methods and Applications” 3rd Edition; by STEVEN C. SPYROS MAKRIDAKIS
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Kahneman, D. “Thinking Fast & Slow”; a PDF version is available on-line
Articles:
(This is a long list for those who are interested in knowing the history and evolution of the
subject matter. I included classic articles and others that have been written by the founders of
the field. Articles and issues that are included in examinations will be clearly defined.)
Kahneman, D. and Tversky, A. “Judgment Under uncertainty: Heuristics and Biases”. A copy is
available on Blackboard.
Grade
The number grade is divided between four activities, some are individuals while others are
group.
 Cases, Papers/Assignments - 50%
 Tests (including oral test) - 25%
 In-class Participation 25%
All submissions should be written as a business report or case analysis reports. This means that
it should be comprehensive, consisting of direct statements with minimum use of adjectives or
adverbs. An example of the recommended paper and case reports structure is available online.
The instructor reserves the right to change the student grade by a factor of 10% based on the
student attendance and participation in the class and performance improvement during the
course.
Academic Honesty:
Recent technology developments, e.g., Generative AI, have created tools that students may use to
develop papers and solve class assignments. This class allows students to benefit from these tools as
long as:
1- Appropriate citations are used.
2- Students fully understand and comprehend the material.
The instructor reserves the right to ask students to clarify statements and concepts stated in their
submissions. If a student is unable to answer questions, the grade will be negatively affected including
receiving a 0 on the assignment and/or the class.
Each student is responsible for performing academic tasks in such a way that honesty is not in
question. Unless an exception is specifically defined by an instructor, students are expected to
maintain the following standards of integrity:
a. All tests, term papers, oral and written assignments, recitations, and all other academic
efforts are to be the work of the student presenting the material.
b. Any use of wording, ideas, or findings of other persons, writers, or researchers requires
the explicit citation of the source; use of the exact wording requires a "quotation"
format.
c. Deliberately supplying material to a student for purposes of plagiarism is also culpable.
When academic honesty is in question, the following may occur:
It should be understood that the college and university policies specifies that:
A faculty member who has clear evidence that academic honesty has been violated may
take appropriate disciplinary action. Appropriate disciplinary action may include, but is
not limited to, requiring the student to rewrite a paper or retake a test, giving the
student an F on the assignment and/or in the course, and/or recommending expulsion.
If the action includes giving a course grade of NCR or F and/or a recommendation for
expulsion because of academic dishonesty, the faculty member must report the action
to the Department Chair and/or Academic Dean (or to the Campus/Program Director for
off-campus situations).
Expulsion for academic dishonesty will be noted on the student's transcript by the
words "Expelled for Academic Dishonesty."
Your continued enrollment in this course denotes understanding of and compliance with all
elements of this syllabus.
Other Policies
Please bring your book with you to class.
Strict adherence to university policy
No chewing gum.
No cell phones or pagers, in the class.
Furniture is to be used for its intended purpose.
Scantron 882-ES. For each test please bring 2. Though most of the tests are less than 100
questions, it is better to be safe.
Problems
Introduction and Definition of Terms
Problem Solving
Decision Making
Herbert Simon Model
How we think and solve problems
Heuristics
(descriptive approaches)
Scripts & Frames
How we should solve problems
Analysis Tools
(prescriptive approaches)
Tools
Some Problem Solving Techniques
Solution
Approaches
Forecasting