GRADUATE SCHOOL
Master of Business Administration - Distance Learning (e-MBA) (Turkish)
EISL 518 | Course Introduction and Application Information
| Course Name |
Data Analytics for Business
|
|
Code
|
Semester
|
Theory
(hour/week) |
Application/Lab
(hour/week) |
Local Credits
|
ECTS
|
|
EISL 518
|
Fall/Spring
|
3
|
0
|
3
|
5
|
| Prerequisites |
None
|
|||||
| Course Language |
Turkish
|
|||||
| Course Type |
Elective
|
|||||
| Course Level |
Second Cycle
|
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| Mode of Delivery | Online | |||||
| Teaching Methods and Techniques of the Course | Group WorkProblem SolvingCase StudyLecture / Presentation | |||||
| Course Coordinator | ||||||
| Course Lecturer(s) | ||||||
| Assistant(s) | - | |||||
| Course Objectives | The course aims to show the usefulness of data-driven approach in business life. Its goal is to teach essential data science knowledge, tools, and practice in the analysis of business problems. |
| Learning Outcomes |
The students who succeeded in this course;
|
| Course Description | This course shows how to exploit data analytics in business problems. It teaches the application of data analytic techniques on business problems via an open source programming language. |
|
|
Core Courses | |
| Major Area Courses |
X
|
|
| Supportive Courses | ||
| Media and Management Skills Courses | ||
| Transferable Skill Courses |
WEEKLY SUBJECTS AND RELATED PREPARATION STUDIES
| Week | Subjects | Related Preparation |
| 1 | Introduction to Business Analytics and R | Chapters 2 & 3 (p. 30-80). Hızıroğlu vd. (2022). Veri Modelleme ve Veri Analitiği - Sağlık ve İşletme Uygulamaları. Nobel Akademik Yayıncılık. ISBN: 978-625-417-474-2 |
| 2 | Creating Data Sets for Business Problems | Chapter 4 (p. 84-120). Hızıroğlu vd. (2022). Veri Modelleme ve Veri Analitiği - Sağlık ve İşletme Uygulamaları |
| 3 | Making Data Ready for Business Analysis | Chapter 4 (p. 84-120). Hızıroğlu vd. (2022). Veri Modelleme ve Veri Analitiği - Sağlık ve İşletme Uygulamaları |
| 4 | Making Data Ready for Business Analysis | Chapters 10 & 11 (p. 177-191). Arslan İ. (2015). R İle İstatistiksel Programlama Veri Analitiği İçin Yeni Bir Yazılım Platformu. Pusula. ISBN: 6056460894 |
| 5 | Gaining Insights on Business Problems by Visualizing Data | Chapter 14 (p. 249-308). Arslan İ. (2015). R İle İstatistiksel Programlama Veri Analitiği İçin Yeni Bir Yazılım Platformu |
| 6 | Gaining Insights on Business Problems by Visualizing Data | Chapter 14 (p. 249-308). Arslan İ. (2015). R İle İstatistiksel Programlama Veri Analitiği İçin Yeni Bir Yazılım Platformu |
| 7 | Basic Statistics | |
| 8 | Sales Data Analysis with Multiple Linear Regression | Chapter 16 (p. 339-357). Arslan İ. (2015). R İle İstatistiksel Programlama Veri Analitiği İçin Yeni Bir Yazılım Platformu |
| 9 | Financial Risk Analysis with Multiple Logistic Linear Regression | Chapter 13 (p. 379-386). Hızıroğlu vd. (2022). Veri Modelleme ve Veri Analitiği - Sağlık ve İşletme Uygulamaları |
| 10 | Product Defect Risk Analysis with Decision Trees | Chapter 11 (p. 284-313). Hızıroğlu vd. (2022). Veri Modelleme ve Veri Analitiği - Sağlık ve İşletme Uygulamaları |
| 11 | Customer Segmentation | Chapter 8 (p. 208-232). Hızıroğlu vd. (2022). Veri Modelleme ve Veri Analitiği - Sağlık ve İşletme Uygulamaları |
| 12 | Text Analysis on Customer Reviews | Chapter 17 (p. 460-491). Hızıroğlu vd. (2022). Veri Modelleme ve Veri Analitiği - Sağlık ve İşletme Uygulamaları |
| 13 | Homework Presentations | |
| 14 | Homework Presentations | |
| 15 | Semester Review | |
| 16 | Final Exam |
| Course Notes/Textbooks | Hızıroğlu, A., Cebeci, H.İ., Codal K.Ç. (2022). Veri Modelleme ve Veri Analitiği - Sağlık ve İşletme Uygulamaları. Nobel Akademik Yayıncılık. ISBN: 978-625-417-474-2
Arslan, İ. (2015). R İle İstatistiksel Programlama Veri Analitiği İçin Yeni Bir Yazılım Platformu. Pusula. ISBN-10: 6056460894 |
| Suggested Readings/Materials |
EVALUATION SYSTEM
| Semester Activities | Number | Weigthing |
| Participation |
1
|
5
|
| Laboratory / Application | ||
| Field Work | ||
| Quizzes / Studio Critiques | ||
| Portfolio | ||
| Homework / Assignments |
1
|
15
|
| Presentation / Jury | ||
| Project | ||
| Seminar / Workshop | ||
| Oral Exams | ||
| Midterm | ||
| Final Exam |
1
|
80
|
| Total |
| Weighting of Semester Activities on the Final Grade |
2
|
20
|
| Weighting of End-of-Semester Activities on the Final Grade |
1
|
80
|
| Total |
ECTS / WORKLOAD TABLE
| Semester Activities | Number | Duration (Hours) | Workload |
|---|---|---|---|
| Theoretical Course Hours (Including exam week: 16 x total hours) |
16
|
3
|
48
|
| Laboratory / Application Hours (Including exam week: '.16.' x total hours) |
16
|
0
|
|
| Study Hours Out of Class |
14
|
2
|
28
|
| Field Work |
0
|
||
| Quizzes / Studio Critiques |
0
|
||
| Portfolio |
0
|
||
| Homework / Assignments |
1
|
34
|
34
|
| Presentation / Jury |
0
|
||
| Project |
0
|
||
| Seminar / Workshop |
0
|
||
| Oral Exam |
0
|
||
| Midterms |
0
|
||
| Final Exam |
1
|
40
|
40
|
| Total |
150
|
COURSE LEARNING OUTCOMES AND PROGRAM QUALIFICATIONS RELATIONSHIP
|
#
|
Program Competencies/Outcomes |
* Contribution Level
|
||||
|
1
|
2
|
3
|
4
|
5
|
||
| 1 | To be able to demonstrate general business knowledge and skills. |
X | ||||
| 2 | To be able to demonstrate business communication skills effectively. |
|||||
| 3 | To be able to deliver creative and innovative solutions to the business-related problems. |
|||||
| 4 | To be able to evaluate the performance of business organizations through a holistic approach. |
|||||
| 5 | To be able to take a critical perspective in evaluating business knowledge. |
|||||
| 6 | To be able to exhibit an ethical and socially responsible behavior in conducting research and making business decisions. |
|||||
| 7 | To be able to solve business related problems using analytical and technological tools and techniques. |
X | ||||
| 8 | To develop a solution to business problems through systematic research. |
X | ||||
| 9 | To be able to use a foreign language to follow information about the field of business and participate in discussions in academic environments. |
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*1 Lowest, 2 Low, 3 Average, 4 High, 5 Highest