Nevşehir Hacı Bektaş Veli University Course Catalogue
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| Year/Semester of Study | 1 / Fall 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 | İBRAHİM AKIN ÖZEN (akin@nevsehir.edu.tr) | ||||
| Name of Lecturer(s) | |||||
| Language of Instruction | Turkish | ||||
| Work Placement(s) | None | ||||
| Objectives of the Course | |||||
| The aim is to gain an overview of big data approaches and applications and to gain a critical understanding of the opportunities and limitations of big data analytics. | |||||
| Learning Outcomes | PO | MME | |
| The students who succeeded in this course: | |||
| LO-1 | They will learn big data concepts, terminology, data analytics features, big data types such as 5V-structural-unstructured-metadata. |
PO-2 It can use innovative research methods to collect big data, evaluate it and use it in decision processes. 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. |
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| LO-2 | qualitative - quantitative data mining, statistical analysis |
PO-2 It can use innovative research methods to collect big data, evaluate it and use it in decision processes. PO-12 It can search and organize information from different sources and interpret the results to create detailed reports. |
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| LO-3 | Graphical examination of the content and features of big data |
PO-2 It can use innovative research methods to collect big data, evaluate it and use it in decision processes. PO-6 Distinguish the current technologies applied in the field of tourism and choose the most suitable one. PO-12 It can search and organize information from different sources and interpret the results to create detailed reports. |
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| LO-4 | Advanced topics and applications in big data |
PO-2 It can use innovative research methods to collect big data, evaluate it and use it in decision processes. PO-12 It can search and organize information from different sources and interpret the results to create detailed reports. |
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| PO: Programme Outcomes MME:Method of measurement & Evaluation |
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| Course Contents | ||
| In this course, features of big data, how to collect big data, big data sources, crowdsourcing, methods of big data analysis are discussed. | ||
| Weekly Course Content | ||
| Week | Subject | Learning Activities and Teaching Methods |
| 1 | Introduction to Big Data: Covers concepts, terminology, features and types of Big Data such as 5V, structured, unstructured, semi-structured and metadata. | Lecture, Q&A |
| 2 | Big data sources and collection. | Lecture, Q&A |
| 3 | Storage and Analytics in Big Data: Covers storage concepts such as clusters. | Lecture, Q&A |
| 4 | Big Data Analysis Techniques | Lecture, Q&A |
| 5 | Visualization in Big Data Sets. | Lecture, Q&A |
| 6 | Advanced topics and applications in big data | Lecture, Q&A |
| 7 | Advanced topics and applications in big data | Lecture, Q&A |
| 8 | mid-term exam | |
| 9 | Advanced topics and applications in big data | Lecture, Q&A |
| 10 | Advanced topics and applications in big data | Lecture, Q&A |
| 11 | Advanced topics and applications in big data | Lecture, Q&A |
| 12 | Advanced topics and applications in big data | Lecture, Q&A |
| 13 | Advanced topics and applications in big data | Lecture, Q&A |
| 14 | Advanced topics and applications in big data | Lecture, Q&A |
| 15 | Advanced topics and applications in big data | Lecture, Q&A |
| 16 | final exam | |
| Recommend Course Book / Supplementary Book/Reading | ||
| Required Course instruments and materials | ||
| books, articles, internet resources, lecture notes | ||
| Assessment Methods | |||
| Type of Assessment | Week | Hours | Weight(%) |
| mid-term exam | 8 | 2 | |
| 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 | 2 | |
| 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 | 2 | 12 | 24 |
| b) Search in internet/Library | 2 | 12 | 24 |
| c) Performance Project | 0 | ||
| d) Prepare a workshop/Presentation/Report | 0 | ||
| e) Term paper/Project | 4 | 14 | 56 |
| Oral Examination | 0 | ||
| Quiz | 0 | ||
| Laboratory exam | 0 | ||
| Own study for mid-term exam | 3 | 5 | 15 |
| mid-term exam | 1 | 2 | 2 |
| Own study for final exam | 3 | 5 | 15 |
| final exam | 1 | 2 | 2 |
| 0 | |||
| 0 | |||
| Total work load; | 180 | ||