Nevşehir Hacı Bektaş Veli University Course Catalogue
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| Year/Semester of Study | 3 / Fall Semester | ||||
| Level of Course | 1st Cycle Degree Programme | ||||
| Type of Course | Optional | ||||
| Department | BUSINESS ADMINISTRATION | ||||
| Pre-requisities and Co-requisites | None | ||||
| Mode of Delivery | Face to Face | ||||
| Teaching Period | 14 Weeks | ||||
| Name of Lecturer | AYLİN ALKAYA (aylin@nevsehir.edu.tr) | ||||
| Name of Lecturer(s) | |||||
| Language of Instruction | English | ||||
| Work Placement(s) | None | ||||
| Objectives of the Course | |||||
| The objective of the course provides the students with fundamental applications about data and data analysis. Students can make a basic analysis of data. | |||||
| Learning Outcomes | PO | MME | |
| The students who succeeded in this course: | |||
| LO-1 | be able to understand the data and analysis methods of data |
PO- |
Examination |
| LO-2 | be able to learn analyze methods for problem types |
PO- |
Examination |
| LO-3 | be able to use visualize the data on computer applications |
PO- |
Examination |
| PO: Programme Outcomes MME:Method of measurement & Evaluation |
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| Course Contents | ||
| The data and power of data, describing the data, variables and statistical analysis, descriptive statistics, distributions and functions, random variable, hypothesis, regression, Visualizing data | ||
| Weekly Course Content | ||
| Week | Subject | Learning Activities and Teaching Methods |
| 1 | Introduction to data analysis. Introduction to statistical softwares. | Lecturing and discussion |
| 2 | Gathering and collecting data, ethics and property rights. Reliability and Validity | Lecturing and discussion, computer application |
| 3 | Visualizing the data. Working with missing data Variable, variable types, random variable. Coding the variables in software | Lecturing and Computer Application |
| 4 | Probability | Lecturing and Computer Application |
| 5 | Distributions, | Lecturing and Computer Application |
| 6 | Normality and parametric tests | Lecturing and Computer Application |
| 7 | Hypothesis tests | Lecturing and Computer Application |
| 8 | mid-term exam | |
| 9 | ndependent and dependent/paired t tests | Lecturing and Computer Application |
| 10 | Nonparametric tests | Lecturing and Computer Application |
| 11 | Nonparametric tests | Lecturing and Computer Application |
| 12 | Correlation analysis | Lecturing and Computer Application |
| 13 | ANOVA | Lecturing and Computer Application |
| 14 | Regression analysis, regression models | Lecturing and Computer Application |
| 15 | Regression analysis, regression models | Lecturing and Computer Application |
| 16 | final exam | |
| Recommend Course Book / Supplementary Book/Reading | ||
| 1 | Hardy, M. & Alan Bryman, 2014. The hand book of Data Analysis, Sage publications Inc.California 91320 | |
| 2 | Provost, F. & Tom Fawcett, 2013. Data Science for Business, O’Reilly Media Inc. CA, 95472. | |
| Required Course instruments and materials | ||
| Textbook, Blackboard, computer and software | ||
| 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 | 14 | 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 | 8 | 24 |
| b) Search in internet/Library | 3 | 8 | 24 |
| c) Performance Project | 3 | 4 | 12 |
| d) Prepare a workshop/Presentation/Report | 0 | ||
| e) Term paper/Project | 0 | ||
| Oral Examination | 0 | ||
| Quiz | 0 | ||
| Laboratory exam | 3 | 2 | 6 |
| Own study for mid-term exam | 2 | 4 | 8 |
| mid-term exam | 2 | 1 | 2 |
| Own study for final exam | 2 | 5 | 10 |
| final exam | 2 | 1 | 2 |
| 0 | |||
| 0 | |||
| Total work load; | 130 | ||