| Course Type | Elective Course |
|---|
| Course Level | Bachelor's Degree |
|---|
| Year of Study | - |
|---|
| Semester | - |
|---|
| ECTS | 5 |
|---|
| Name of Lecturer(s) |
- |
| Learning Outcomes |
At the end of this course, the students;
- Examine big data sources in the tourism industry.
- Explore basic data mining and machine learning methods.
- Compare artificial intelligence and machine learning.
- Discuss the use of big data analysis for customer relationship management, destination management, and online reputation management.
- Organize and clean data sets using Python.
|
| Mode of Delivery | Face to face |
|---|
| Prerequisites | No |
|---|
| Recommended Optional Programme Components | None |
|---|
| Course Description | This course covers topics related to big data sources, analysis methods, and their applications in tourism. Big data analysis methods will be discussed in the context of smart destination management, customer relationship management, and online reputation management. Additionally, common machine learning and data mining techniques will be emphasized. Students will use essential Python libraries to perform big data analysis. |
|---|
| Course Contents |
| WEEK |
TOPICS |
|
1st Week |
Introduction: course content overview |
|
2nd Week |
Big data sources in tourism and hospitality, variables in the tourism industry |
|
3rd Week |
Big data sources in tourism and hospitality, variables in the tourism industry |
|
4th Week |
Big data usage for tourism and hospitality, customer relationship management and online reputation management |
|
5th Week |
Big data usage for tourism and hospitality, smart destination management |
|
6th Week |
Big data analysis methods and tools, predictive analytics, machine learning |
|
7th Week |
Topic review |
|
8th Week |
Midterm exam |
|
9th Week |
Big data analysis; data mining process |
|
10th Week |
Text mining, sentiment, and opinion analysis |
|
11th Week |
Working with DataFrame, data cleaning and organization using the Pandas library |
|
12th Week |
Working with DataFrame, data cleaning and organization using the Pandas library |
|
13th Week |
Topic review |
|
14th Week |
Topic review |
|
| Recommended or Required Reading | Anaconda navigator
WEKA
|
|---|
| Planned Learning Activities and Teaching Methods |
- Lecture
- Discussion
- Questions/Answers
- Presentation
|
|---|
| Assessment Methods and Criteria |
|
Quantity |
Percentage (%) |
| Mid-term |
1 |
30 |
| Project |
1 |
20 |
| Total(%) |
|
50 |
| Contribution of In-term Studies to Overall Grade(%) |
|
50 |
| Contribution of Final Examination to Overall Grade(%) |
|
50 |
| Total(%) |
|
100 |
|
| ECTS Allocated Based on Student Workload |
| Activities |
Quantity |
Duration (Hour) |
Total Workload (Hour) |
| Midterm exam |
1 |
1 |
1 |
| Preparation for Quiz |
|
|
|
| Individual or group work |
14 |
2 |
28 |
| Preparation for Final exam |
1 |
30 |
30 |
| Course hours |
13 |
3 |
39 |
| Preparation for Midterm exam |
1 |
20 |
20 |
| Laboratory (including preparation) |
|
|
|
| Final exam |
1 |
1 |
1 |
| Homework |
|
|
|
| Project |
1 |
25 |
25 |
| Total Work Load | 144 |
| Total Work Load / 30 (h) | 4.8 |
| ECTS Credit of the Course | 5 |
|
| Language of Instruction | Turkish |
|---|
| Work Placement(s) | No |
|---|
