GRADUATE SCHOOL
Master of Business Administration (MBA) (With Thesis)
BA 585 | Course Introduction and Application Information
| Course Name |
Data Analytics for Business
|
|
Code
|
Semester
|
Theory
(hour/week) |
Application/Lab
(hour/week) |
Local Credits
|
ECTS
|
|
BA 585
|
Fall/Spring
|
3
|
0
|
3
|
7.5
|
| Prerequisites |
None
|
|||||
| Course Language |
English
|
|||||
| Course Type |
Elective
|
|||||
| Course Level |
Second Cycle
|
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| Mode of Delivery | - | |||||
| Teaching Methods and Techniques of the Course | Case StudyApplication: Experiment / Laboratory / WorkshopLecture / Presentation | |||||
| Course Coordinator | ||||||
| Course Lecturer(s) | ||||||
| Assistant(s) | ||||||
| Course Objectives | The course aims to show how to deal with business problems via data-driven approach. Its objective is to equip students with 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 the use and application of data science for business problems. It teaches the use of R programming in the application of data analytic models on various business case studies. |
|
|
Core Courses |
X
|
| Major Area Courses | ||
| 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 | Chapter 1. Kabacoff, R. (2011). R in Action. Shelter Island, NY, USA: Manning publications. ISBN: 9781935182399. |
| 2 | Creating Data Sets for Business Problems | Chapter 2. Ledolter, J. (2013). Data mining and business analytics with R. Hoboken, NJ: Wiley. ISBN: 978-1-118-44714-7 |
| 3 | Making Data Ready for Business Analysis | Chapter 4. Kabacoff, R. (2011). R in Action. |
| 4 | Making Data Ready for Business Analysis | Chapter 4. Kabacoff, R. (2011). R in Action. |
| 5 | Gaining Insights on Business Problems by Visualizing Data | Chapter 6. Kabacoff, R. (2011). R in Action. |
| 6 | Gaining Insights on Business Problems by Visualizing Data | Chapter 6. Kabacoff, R. (2011). R in Action. |
| 7 | Basic Statistics for Business | |
| 8 | Sales Data Analysis with Multiple Linear Regression | Chapter 3. Ledolter, J. (2013). Data mining and business analytics with R. |
| 9 | Midterm Exam | |
| 10 | Financial Risk Analysis with Multiple Logistic Linear Regression | Chapter 7. Ledolter, J. (2013). Data mining and business analytics with R. |
| 11 | Product Defect Risk Analysis with Decision Trees | Chapter 13. Ledolter, J. (2013). Data mining and business analytics with R. |
| 12 | Customer Segmentation | Chapters 15-16. Ledolter, J. (2013). Data mining and business analytics with R. |
| 13 | Text Analysis on Customer Reviews | Chapter 19. Ledolter, J. (2013). Data mining and business analytics with R. |
| 14 | Which Method to Use for Which Business Problems? | |
| 15 | Semester Review | |
| 16 | Final Exam |
| Course Notes/Textbooks | Ledolter, J. (2013). Data mining and business analytics with R. Hoboken, NJ: Wiley. ISBN: 978-1-118-44714-7 Kabacoff, R. (2011). R in Action. Shelter Island, NY, USA: Manning publications. ISBN: 9781935182399 |
| Suggested Readings/Materials |
EVALUATION SYSTEM
| Semester Activities | Number | Weigthing |
| Participation |
1
|
5
|
| Laboratory / Application | ||
| Field Work | ||
| Quizzes / Studio Critiques | ||
| Portfolio | ||
| Homework / Assignments |
1
|
25
|
| Presentation / Jury | ||
| Project | ||
| Seminar / Workshop | ||
| Oral Exams | ||
| Midterm |
1
|
30
|
| Final Exam |
1
|
40
|
| Total |
| Weighting of Semester Activities on the Final Grade |
3
|
60
|
| Weighting of End-of-Semester Activities on the Final Grade |
1
|
40
|
| 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
|
5
|
70
|
| Field Work |
0
|
||
| Quizzes / Studio Critiques |
0
|
||
| Portfolio |
0
|
||
| Homework / Assignments |
1
|
32
|
32
|
| Presentation / Jury |
0
|
||
| Project |
0
|
||
| Seminar / Workshop |
0
|
||
| Oral Exam |
0
|
||
| Midterms |
1
|
30
|
30
|
| Final Exam |
1
|
40
|
40
|
| Total |
220
|
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 able to master the state-of-the-art literature in the area of specialization. |
|||||
| 3 | To be able to evaluate the performance of business organizations through a holistic approach. |
X | ||||
| 4 | To be able to effectively communicate scientific ideas and research results to diverse audiences. |
X | ||||
| 5 | To be able to deliver creative and innovative solutions to business-related problems. |
|||||
| 6 | To be able to solve business related problems using analytical and technological tools and techniques. |
X | ||||
| 7 | To be able to take a critical perspective in evaluating business knowledge. |
|||||
| 8 |
To be able to exhibit an ethical and socially responsible behavior in conducting research and making business decisions. |
|||||
| 9 | To be able to carry out a well-designed independent and empirical research. |
X | ||||
| 10 | To be able to use a foreign language to follow information about the field of finance and participate in discussions in academic environments. |
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*1 Lowest, 2 Low, 3 Average, 4 High, 5 Highest