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 | ECONOMICS (PhD) | ||||
| Pre-requisities and Co-requisites | None | ||||
| Mode of Delivery | Face to Face | ||||
| Teaching Period | 14 Weeks | ||||
| Name of Lecturer | SERAP ÇOBAN (seraps@nevsehir.edu.tr) | ||||
| Name of Lecturer(s) | |||||
| Language of Instruction | Turkish | ||||
| Work Placement(s) | None | ||||
| Objectives of the Course | |||||
| Learning Outcomes | PO | MME | |
| The students who succeeded in this course: | |||
| LO-1 |
PO-4 have theoretical and empirical information to analyze how to authorities, consumers and firms effect each other’s and how to make decision. PO-5 have upper explanation ability on the effects of economy policies on individuals and firms under the principles which are dominating economy. PO-7 have ability on considering the basic items of Money, Banking and Financial System and the treatment process of these items. PO-8 have ability on working with quantitative data about economics and using the tools about statistically data analysis. PO-9 have ability on commenting current economic issues considering economic theory in the view of sociology. PO-10 have ability on modeling the economic theories mathematically. PO-11 have ability on defining economic variables and comment on the relationships between these variables. PO-12 can know the stages that need to apply in a research and do research. PO-17 can know the importance of sustainable growth and development with environmental conscious. |
Examination Laboratory Exam Performance Project |
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| LO-2 | Knowledge of the basic methods in the field of Data Science and Big Data Analysis. |
PO- |
Examination Performance Project |
| LO-3 | Ability to model and solve practical problems using Data Science and Big Data Analysis methods |
PO- |
Examination Performance Project |
| PO: Programme Outcomes MME:Method of measurement & Evaluation |
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| Course Contents | ||
| Weekly Course Content | ||
| Week | Subject | Learning Activities and Teaching Methods |
| 1 | Introduction to Data Science and Big Data Analysis | Narration Method Discussion Method |
| 2 | Relational Databases and Data Modeling | Narration Method Discussion Method |
| 3 | Data Warehousing and Integration | Narration Method Discussion Method |
| 4 | Parallel Databases/Hadoop | Narration Method Discussion Method |
| 5 | Mapreduce/Spark | Narration Method Discussion Method |
| 6 | Data Visualisation | Narration Method Discussion Method |
| 7 | Introduction to Machine Learning | Narration Method Discussion Method |
| 8 | mid-term exam | |
| 9 | Classification and Regression | Narration Method Discussion Method |
| 10 | Condensation | Narration Method Discussion Method |
| 11 | Introduction to Natural Language Processing | Narration Method Discussion Method |
| 12 | Introduction to Information Access | Narration Method Discussion Method |
| 13 | Network Analysis | Narration Method Discussion Method |
| 14 | Project Presentations | Narration Method Discussion Method |
| 15 | Project Presentations | Narration Method Discussion Method |
| 16 | final exam | |
| Recommend Course Book / Supplementary Book/Reading | ||
| 1 | Data Science from Scratch, O’Reilly Media, Joel Grus (2015) | |
| Required Course instruments and materials | ||
| 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 | 2 | 14 | 28 |
| b) Search in internet/Library | 3 | 10 | 30 |
| c) Performance Project | 0 | ||
| d) Prepare a workshop/Presentation/Report | 4 | 8 | 32 |
| e) Term paper/Project | 0 | ||
| Oral Examination | 0 | ||
| Quiz | 0 | ||
| Laboratory exam | 0 | ||
| Own study for mid-term exam | 2 | 7 | 14 |
| mid-term exam | 1 | 1 | 1 |
| Own study for final exam | 3 | 6 | 18 |
| final exam | 1 | 1 | 1 |
| 0 | |||
| 0 | |||
| Total work load; | 166 | ||