COURSE UNIT TITLE

Offered By

BUSINESS ADMINISTRATION (English)

Level of Course Unit

First Cycle Programmes (Bachelor's Degree)

Course Coordinator

PROFESSOR DOCTOR GÜZIN ÖZDAĞOĞLU

Offered to

BUSINESS ADMINISTRATION (English)

Course Objective

This course aims at developing the students knowledge of business intelligence, basic concepts of intelligent systems and the basics of modeling approaches.

Learning Outcomes of the Course Unit

Mode of Delivery

Face -to- Face

Prerequisites and Co-requisites

None

Recomended Optional Programme Components

None

Course Contents

Recomended or Required Reading

1. Text Books:
-Business Intelligence and Analytics: Systems for Decision Support (Sharba, Delen, Turban), Pearson.
-Business Intelligence: Managerial Approach, Efraim Turban, Pearson, 2011.
-Introduction to Data Mining , Pang-Ning Tan, Michael Steinbach, Vipin Kumar, Pearson.

2. Lecture Slides:
Complementary of the text books.
3. Software tools (may change according to the usage constraints)

MS Excel
Rapidminer
R, python
Power BI

Planned Learning Activities and Teaching Methods

1. Lectures
Class lecture is highly interactive and format is direct. The instructor prompts students for response to questions posed and solicits their thoughts on issues discussed. Lectures will focus on the transfer of basic intelligent system concepts and techniques where comprehension is substantially enhanced by additional elaboration and illustration. Students may need to review their knowledge of statistics and mathematics

2. Computer Applications
In the applıcation component, Spreadsheet Software and a particular data analysis packages will be employed to perform analyses of problem domain. Instruction on the use of this software as it relates to business intelligence problems will be provided in class and in the book.

Assessment Methods

Further Notes About Assessment Methods

1. Assignemnents

In-class and out-of-class practice problems will be given frequently. It is essential for the student to work on and understand these problems in order to successfully complete the course. It can also be defined as homework according to the conditions of the course.

Each student will develop their analytical skills and at the same time increase their competence on the software tool, which includes a data mining plug-in spreadsheet tool and a business application for data analysis. Each student will develop their communication and analytical skills through business intelligence concepts and business practices by actively participating in classroom assignments.


2. Term Project

Case studies or real life applications will provide a great opportunity for students to realize their analysis and modeling skills for real situations and develop solutions. The cases will be assigned to each student/group by the instructor at the beginning of the term. Topics will focus on the analysis of cases of business intelligence for problems encountered in the management of a manufacturing or service oriented business, government or non-profit organization.

Case analysis and reports of real life applications will be submitted to the instructor online a week before the last week. Each case and application report will be written using Microsoft Word and / or Excel and will include: (i) a title page containing the title of the case and the full names of the authors, (ii) the main part of the report starting on the second page, (iii) report appendices.

Assessment Criteria

GENERAL REQUIREMENTS:
1. Applications and assignements require a collaborative effort. If group work is done, it is the responsibility of the group to ensure that each group member contributes approximately equally to the group work. Applications will be graded by the faculty member and group members. Each member of the group will be asked to evaluate his and other group members' contribution at the end of the semester. A peer review form will be provided in the last week of the course.

2. Application reports will be evaluated on the basis of the clear understanding of the subject, the originality of the handling and discussion, the accuracy of the results, the comprehensiveness of the report content and the depth of the analysis, clarity and organization, format, punctuation, grammar, and the quality of the visuals.

RUBRIC FOR EVALUATION:

1. The student is able to clearly identify the problem (by stating the reasons, conditions,etc) (5 pts)
2. The student is able to identify research questions (5 pts).
3. The student is able to collect or extract relevant qualitative and/or quantitative data (10 pts).
4. The student is able to apply exploratory data analysis with proper visuals for summarizing your dataset, such as tables, graphs, dashboards (10 pts).
5. The student is able to discover the relevant preprocessing and apply them on the dataset (15 pts).
6. The student is able to select techniques and algorithms to complete the data model aligned with the research questions (give the details of the algorithms with the related literature) (10 pts).
7. The student is able to analyze the selected model using appropriate tools and technologies (Power Bi, Python or R IDEs, RapidMiner etc) (15 pts).
8. The student is able to select and use the appropriate indicators to measure the performance of the model (10 pts).
9. The student is able to explain the performance of your model and discuss its quality for future use (10 pts).
10. The student is able to explain the main findings with their practical implications (10pts).

Language of Instruction

English

Course Policies and Rules

Academic integrity is to demonstrate responsbile and honest behaviors and follow ethical principles in academia. All students should respect the intellectual property rights of others. Specifically every student should avoid plagiarism. All types of plagiarism are serious and violate academic integrity policy.

To understand and prevent plagiarism, please see the following link: https://www.plagiarism.org/understanding-plagiarism.

Contact Details for the Lecturer(s)

During the semester please use communication channels within online.deu.edu.tr platform such as meetings, messages, chatroom, and forum.

Assoc.Prof.Dr.Güzin Özdağoğlu
guzin.kavrukkoca@deu.edu.tr
Office No at the Faculty: 132a



Teaching Assisstant: Elif Çirkin

Office Hours

All communication in the scope of the course will be held within online.deu.edu.tr platform. If you need one-to-one support, you can write messages or request an appointment from the instructor or the teaching assisstant for a meeting (online).

Work Placement(s)

None

Workload Calculation

Contribution of Learning Outcomes to Programme Outcomes