Nevşehir Hacı Bektaş Veli University Course Catalogue
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| Year/Semester of Study | 1 / Spring Semester | ||||
| Level of Course | 2nd Cycle Degree Programme | ||||
| Type of Course | Optional | ||||
| Department | TOURISM TECHNOLOGIES AND INNOVATION (MASTER'S WITH THESIS) | ||||
| Pre-requisities and Co-requisites | None | ||||
| Mode of Delivery | Face to Face | ||||
| Teaching Period | 14 Weeks | ||||
| Name of Lecturer | SEMA ATASEVER (sema@nevsehir.edu.tr) | ||||
| Name of Lecturer(s) | |||||
| Language of Instruction | Turkish | ||||
| Work Placement(s) | None | ||||
| Objectives of the Course | |||||
| To teach data mining and to gain the ability to solve problems in the field of tourism with data mining approaches. | |||||
| Learning Outcomes | PO | MME | |
| The students who succeeded in this course: | |||
| LO-1 | Knows data mining. |
PO-2 It can use innovative research methods to collect big data, evaluate it and use it in decision processes. PO-3 It can identify potential tourist profiles and develop product positioning strategies for destinations and tourism businesses. PO-5 Can apply artificial intelligence approaches to tourism problems. PO-6 Distinguish the current technologies applied in the field of tourism and choose the most suitable one. PO-7 Knows the concepts of innovation and technology in the field of tourism. PO-8 Gain knowledge of price determination in the tourism sector, estimation of seasonal demands, and personalized recommendations. PO-10 Knows how to apply different management tools in tourism business with special emphasis on information and communication technologies and indicative measurements. PO-12 It can search and organize information from different sources and interpret the results to create detailed reports. PO-15 Gain problem solving skills by using research methods used in different disciplines. |
Examination |
| LO-2 | Define the data warehouse and its properties. |
PO-2 It can use innovative research methods to collect big data, evaluate it and use it in decision processes. PO-3 It can identify potential tourist profiles and develop product positioning strategies for destinations and tourism businesses. PO-5 Can apply artificial intelligence approaches to tourism problems. PO-6 Distinguish the current technologies applied in the field of tourism and choose the most suitable one. PO-7 Knows the concepts of innovation and technology in the field of tourism. PO-8 Gain knowledge of price determination in the tourism sector, estimation of seasonal demands, and personalized recommendations. PO-10 Knows how to apply different management tools in tourism business with special emphasis on information and communication technologies and indicative measurements. PO-12 It can search and organize information from different sources and interpret the results to create detailed reports. PO-15 Gain problem solving skills by using research methods used in different disciplines. |
Examination |
| LO-3 | Can analyze time series. |
PO-2 It can use innovative research methods to collect big data, evaluate it and use it in decision processes. PO-3 It can identify potential tourist profiles and develop product positioning strategies for destinations and tourism businesses. PO-5 Can apply artificial intelligence approaches to tourism problems. PO-6 Distinguish the current technologies applied in the field of tourism and choose the most suitable one. PO-7 Knows the concepts of innovation and technology in the field of tourism. PO-8 Gain knowledge of price determination in the tourism sector, estimation of seasonal demands, and personalized recommendations. PO-10 Knows how to apply different management tools in tourism business with special emphasis on information and communication technologies and indicative measurements. PO-12 It can search and organize information from different sources and interpret the results to create detailed reports. PO-15 Gain problem solving skills by using research methods used in different disciplines. |
Examination |
| LO-4 | Can create decision trees. |
PO-2 It can use innovative research methods to collect big data, evaluate it and use it in decision processes. PO-3 It can identify potential tourist profiles and develop product positioning strategies for destinations and tourism businesses. PO-5 Can apply artificial intelligence approaches to tourism problems. PO-6 Distinguish the current technologies applied in the field of tourism and choose the most suitable one. PO-7 Knows the concepts of innovation and technology in the field of tourism. PO-8 Gain knowledge of price determination in the tourism sector, estimation of seasonal demands, and personalized recommendations. PO-10 Knows how to apply different management tools in tourism business with special emphasis on information and communication technologies and indicative measurements. PO-12 It can search and organize information from different sources and interpret the results to create detailed reports. PO-15 Gain problem solving skills by using research methods used in different disciplines. |
Examination |
| LO-5 | Can use classification, clustering and association methods. |
PO-2 It can use innovative research methods to collect big data, evaluate it and use it in decision processes. PO-3 It can identify potential tourist profiles and develop product positioning strategies for destinations and tourism businesses. PO-5 Can apply artificial intelligence approaches to tourism problems. PO-6 Distinguish the current technologies applied in the field of tourism and choose the most suitable one. PO-7 Knows the concepts of innovation and technology in the field of tourism. PO-8 Gain knowledge of price determination in the tourism sector, estimation of seasonal demands, and personalized recommendations. PO-10 Knows how to apply different management tools in tourism business with special emphasis on information and communication technologies and indicative measurements. PO-12 It can search and organize information from different sources and interpret the results to create detailed reports. PO-15 Gain problem solving skills by using research methods used in different disciplines. |
Examination |
| PO: Programme Outcomes MME:Method of measurement & Evaluation |
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| Course Contents | ||
| Introduction to data mining, database, data models, data warehouse and properties, data cleaning, data integration, data reduction and data transformation, time series analysis, ID3 and C4.5 decision tree algorithms, support vector machines, Naive Bayes and k-en near neighbor classification algorithms, k-means and combinatorial hierarchical clustering algorithms, Apriori association algorithm. | ||
| Weekly Course Content | ||
| Week | Subject | Learning Activities and Teaching Methods |
| 1 | Introduction to data mining | Lecture, question-answer, problem solving |
| 2 | Database, data models, data warehouse and properties | Lecture, question-answer, problem solving |
| 3 | Data cleaning, data integration, data reduction and data transformation | Lecture, question-answer, problem solving |
| 4 | Time series analysis: MA, WMA, ARMA, ARIMA models | Lecture, question-answer, problem solving |
| 5 | Decision trees: ID3 algorithm | Lecture, question-answer, problem solving |
| 6 | Decision trees: C4.5 algorithm | Lecture, question-answer, problem solving |
| 7 | Classification: k-nearest neighbor algorithm | Lecture, question-answer, problem solving |
| 8 | mid-term exam | |
| 9 | Classification: k-nearest neighbor algorithm (continued) | Lecture, question-answer, problem solving |
| 10 | Classification: Naive Bayes algorithm | Lecture, question-answer, problem solving |
| 11 | Classification: Support vector machines | Lecture, question-answer, problem solving |
| 12 | Clustering: Associative hierarchical clustering algorithm | Lecture, question-answer, problem solving |
| 13 | Clustering: k-means algorithm | Lecture, question-answer, problem solving |
| 14 | Clustering: k-means algorithm (continued) | Lecture, question-answer, problem solving |
| 15 | Association analysis: Apriori algorithm | Lecture, question-answer, problem solving |
| 16 | final exam | |
| Recommend Course Book / Supplementary Book/Reading | ||
| 1 | Han, J., Kamber, M., & Pei, J. (2011). Data mining concepts and techniques third edition. University of Illinois at Urbana-Champaign Micheline Kamber Jian Pei Simon Fraser University. | |
| 2 | Veri Madenciliği Yöntemleri, Y. Özkan, Papatya Yayıncılık, 2008. | |
| Required Course instruments and materials | ||
| Textbook, computer, projector. | ||
| Assessment Methods | |||
| Type of Assessment | Week | Hours | Weight(%) |
| mid-term exam | 8 | 1 | 40 |
| Other assessment methods | |||
| 1.Oral Examination | |||
| 2.Quiz | |||
| 3.Laboratory exam | |||
| 4.Presentation | |||
| 5.Report | |||
| 6.Workshop | |||
| 7.Performance Project | |||
| 8.Term Paper | |||
| 9.Project | |||
| final exam | 16 | 1 | 60 |
| Student Work Load | |||
| Type of Work | Weekly Hours | Number of Weeks | Work Load |
| Weekly Course Hours (Theoretical+Practice) | 3 | 14 | 42 |
| Outside Class | |||
| a) Reading | 3 | 14 | 42 |
| b) Search in internet/Library | 3 | 14 | 42 |
| c) Performance Project | 0 | ||
| d) Prepare a workshop/Presentation/Report | 0 | ||
| e) Term paper/Project | 0 | ||
| Oral Examination | 0 | ||
| Quiz | 0 | ||
| Laboratory exam | 0 | ||
| Own study for mid-term exam | 2 | 13 | 26 |
| mid-term exam | 1 | 1 | 1 |
| Own study for final exam | 2 | 13 | 26 |
| final exam | 1 | 1 | 1 |
| 0 | |||
| 0 | |||
| Total work load; | 180 | ||