COURSE UNIT TITLE

: QUANTATIVE BUSINESS ANALYSIS II

DEGREE PROGRAMMES

Description of Individual Course Units

Course Unit Code Course Unit Title Type Of Course D U L ECTS
ENM 5014 QUANTATIVE BUSINESS ANALYSIS II ELECTIVE 3 0 0 8

Offered By

Graduate School of Natural and Applied Sciences

Level of Course Unit

Second Cycle Programmes (Master's Degree)

Course Coordinator

PROFESSOR DOCTOR ADIL BAYKASOĞLU

Offered to

ENGINEERING MANAGEMENT- NON THESIS (EVENING PROGRAM)

Course Objective

This course will prepare the students to become skilled and effective business analysts. Those topics relevant to decision making in today s business world will be covered. The most commonly used management science techniques will be provided and how these tools can be implemented using spreadsheets will be shown. The course includes variety of topics such as probability, statistics, operations research, and other mathematical disciplines with an emphasis on how you can use them to obtain business insight and guide decision making.

Learning Outcomes of the Course Unit

1   This course is expected to help the student to learn basic principles of selected topics in mathematical programming such as non-linear programming, multi-objective optimization, decision theory etc.
2   To enable students to perform effective business analysis.
3   To enable students to solve complex non-linear programming models via optimizaton softwares.
4   To enable students to discuss and solve multiple attribute decision making problems.

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Week Subject Description
1 Network Models in Management Science
2 Network Models in Management Science
3 Introduction to non-linear programming
4 Introduction to non-linear programming
5 Goal Programming and Multiple Objective Optimization
6 Goal Programming and Multiple Objective Optimization
7 Model solving in non-linear programming with computer applications
8 Model solving in non-linear programming with computer applications
9 Midterm
10 Decision Analysis
11 Decision Analysis
12 Introduction to Queueing Systems and Markov Chains
13 Case discussions & Project Presentations
14 Case discussions & Project Presentations

Recomended or Required Reading

C. T., Ragsdale, Spreadsheet Modeling and Decision Analysis, 4e, South-Western College Publishing, 2004

Planned Learning Activities and Teaching Methods

Class presentations, case studies and practical applications

Assessment Methods

SORTING NUMBER SHORT CODE LONG CODE FORMULA
1 MTE MIDTERM EXAM
2 ASG ASSIGNMENT
3 FIN FINAL EXAM
4 FCGR FINAL COURSE GRADE (RESIT) MTE * 0.25 + ASG * 0.25 + FIN * 0.50
5 RST RESIT
6 FCGR FINAL COURSE GRADE (RESIT) MTE * 0.25 + ASG * 0.25 + RST * 0.50


*** Resit Exam is Not Administered in Institutions Where Resit is not Applicable.

Further Notes About Assessment Methods

None

Assessment Criteria

Midterm : 25%
Final Term: 50%
Homework: 25%

Language of Instruction

English

Course Policies and Rules

To be announced.

Contact Details for the Lecturer(s)

Prof.Dr.Adil Baykasoğlu
+90 232 301 76 00

Office Hours

To be announced.

Work Placement(s)

None

Workload Calculation

Activities Number Time (hours) Total Work Load (hours)
Lectures 13 3 39
Preparations before/after weekly lectures 13 7 91
Preparation for midterm exam 1 15 15
Preparation for final exam 1 25 25
Preparing presentations 1 30 30
Midterm 1 3 3
Final 1 3 3
TOTAL WORKLOAD (hours) 206

Contribution of Learning Outcomes to Programme Outcomes

PO/LOPO.1PO.2PO.3PO.4PO.5PO.6PO.7PO.8PO.9PO.10
LO.14432444
LO.2352113
LO.354445
LO.43435434

CONTACT INFORMATION
Dokuz Eylül Üniversitesi Cumhuriyet Bulvarı No: 144 35210 Alsancak / İZMİR
Phone: +90(232) 412 12 12 - Fax: +90 (232) 464 81 35

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