| Data Science (Master) (with Thesis) | |||||
| Master | TR-NQF-HE: Level 7 | QF-EHEA: Second Cycle | EQF-LLL: Level 7 | ||
| Course Code: | VB5007 | ||||
| Course Name: | Business Intelligence and Data Visualization | ||||
| Semester: |
Fall Spring |
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| Course Credits: |
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| Language of instruction: | Turkish | ||||
| Course Condition: | |||||
| Does the Course Require Work Experience?: | No | ||||
| Type of course: | Departmental Elective | ||||
| Course Level: |
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| Mode of Delivery: | Face to face | ||||
| Course Coordinator: | Doç. Dr. ŞEBNEM ÖZDEMİR | ||||
| Course Lecturer(s): | Şebnem Özdemir | ||||
| Course Assistants: |
| Course Objectives: | The aim of this course is to teach students the basic concepts and techniques in business intelligence and data visualization and to improve their data analysis and reporting skills. |
| Course Content: | 1. The concept and basic principles of business intelligence 2. Data visualization tools and techniques 3. Data analysis and reporting methods 4. Data visualization and business intelligence applications 5. Business intelligence and data visualization ethics and issues |
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The students who have succeeded in this course;
1) 1. Students will be able to understand and apply the principles of business intelligence and data visualization. 2) 2. Students will be able to analyze data using different data visualization tools and techniques. 3) 3. Students will be able to use business intelligence applications in real life. 4) 4. Students will be aware of data visualization and business intelligence ethics. 5) 5. Students will be able to analyze and solve BI and data visualization problems. |
| Week | Subject | Related Preparation |
| 1) | Week 1: Introduction to business intelligence and data visualization. | No Preliminary Preparation Required. |
| 2) | Week 2: Data collection and data sources. | No Preliminary Preparation Required. |
| 3) | Week 3: Data cleaning and data preparation techniques. | No Preliminary Preparation Required. |
| 4) | Week 4: Data visualization tools and techniques. | No Preliminary Preparation Required. |
| 5) | Week 5: Graph and table design. | No Preliminary Preparation Required. |
| 6) | Week 6: Data visualization principles and effective communication. | No Preliminary Preparation Required. |
| 7) | Week 7: Business intelligence and data visualization ethics and issues. | No Preliminary Preparation Required. |
| 8) | Week 8: Midterm exam. | No Preliminary Preparation Required. |
| 9) | Week 9: Examples of BI and data visualization applications. | No Preliminary Preparation Required. |
| 10) | Week 10: Data analysis and reporting methods. | No Preliminary Preparation Required. |
| 11) | Week 11: Use and features of business intelligence tools. | No Preliminary Preparation Required. |
| 12) | Week 12: Sample scenarios for data visualization and BI projects. | No Preliminary Preparation Required. |
| 13) | Week 13: Mentoring and progress monitoring for students' projects. | No Preliminary Preparation Required. |
| 14) | Week 14: In-depth study on data analysis and reporting methods. | No Preliminary Preparation Required. |
| 15) | Week 15: Comparison and selection process of BI and data visualization tools. | No Preliminary Preparation Required. |
| 16) | Week 16: Final exam | No Preliminary Preparation Required. |
| Course Notes / Textbooks: | 1. Alberto Cairo, "The Truthful Art: Data, Charts, and Maps for Communication" 2. Stephen Few, "Information Dashboard Design: Displaying Data for At-a-Glance Monitoring" |
| References: | 1. Cole Nussbaumer Knaflic, "Storytelling with Data: A Data Visualization Guide for Business Professionals" |
| Course Learning Outcomes | 1 |
2 |
3 |
4 |
5 |
|---|---|---|---|---|---|
| Program Outcomes | |||||
| 1) Students who successfully complete this program, Knows the scope of technical applications of data science and the tools that can be used. | 3 | 2 | 3 | 2 | 3 |
| 2) Students who successfully complete this program, Knows the effects of application results on society-culture-law. | 2 | 3 | 3 | 2 | 3 |
| 3) Students who complete this program; Recognizes mathematics and code in application processes. | 3 | 2 | 3 | 2 | 3 |
| 4) Students who complete this program; Explain the effects of processes in data science on output and individual. | 3 | 3 | 2 | 3 | 2 |
| 5) Students who successfully complete this program, Understands the insight-foresight and foresight created by data science as a whole in the face of a certain discipline/case. | 3 | 3 | 3 | 2 | 3 |
| No Effect | 1 Lowest | 2 Average | 3 Highest |
| Program Outcomes | Level of Contribution | |
| 1) | Students who successfully complete this program, Knows the scope of technical applications of data science and the tools that can be used. | 3 |
| 2) | Students who successfully complete this program, Knows the effects of application results on society-culture-law. | 3 |
| 3) | Students who complete this program; Recognizes mathematics and code in application processes. | 3 |
| 4) | Students who complete this program; Explain the effects of processes in data science on output and individual. | 3 |
| 5) | Students who successfully complete this program, Understands the insight-foresight and foresight created by data science as a whole in the face of a certain discipline/case. | 3 |
| Semester Requirements | Number of Activities | Level of Contribution |
| Midterms | 1 | % 50 |
| Final | 1 | % 50 |
| total | % 100 | |
| PERCENTAGE OF SEMESTER WORK | % 50 | |
| PERCENTAGE OF FINAL WORK | % 50 | |
| total | % 100 | |
| Activities | Number of Activities | Workload |
| Course Hours | 14 | 28 |
| Midterms | 7 | 51 |
| Final | 8 | 65 |
| Total Workload | 144 | |