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Information
| Code | MG3819 |
| Name | Data Analysis |
| Term | 2023-2024 Academic Year |
| Term | Fall and Spring |
| Duration (T+A) | 4-0 (T-A) (17 Week) |
| ECTS | 8 ECTS |
| National Credit | 4 National Credit |
| Teaching Language | Türkçe |
| Level | Doktora Dersi |
| Type | Normal |
| Mode of study | Yüz Yüze Öğretim |
| Catalog Information Coordinator | Doç. Dr. MEHMET ALİ BURAK NAKIBOĞLU |
| Course Instructor |
1 |
Course Goal / Objective
The aim of this course is to make students capable of using suitable and appropriate data analysis methods for different research objectives and cases, according to each methods' specific application rules and processes.
Course Content
This course consists of the subjects of examining the data, descriptive statistics, factor analysis, regression, discriminant, multivariate variance, cluster analysis and basics of structural equation modeling
Course Precondition
No prerequisite for the course
Resources
Hair, J.F.; Black, W.C.; Babin, B.J.; Anderson, R.E.; Tahtam, R.L. Multivariate Data Analysis Sharma, S. Applied Multivariate Techniques Nakip M., Pazarlama Araştırmaları Teknikler Ve SPSS Destekli Uygulamalar Mcdaniel, C., Gates R., Marketing Research Essentials Journal Of Marketing, Journal Of Marketing Research Dergilerinden Seçilmiş Makaleler
Notes
Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking Foster Provots Tom Fawcett
Course Learning Outcomes
| Order | Course Learning Outcomes |
|---|---|
| LO01 | Defines the basics of basic data analysis methods |
| LO02 | List multivariate analyzes and their assumptions |
| LO03 | describes the application processes of multivariate analyzes and related tests |
| LO04 | Uses statistical package programs and analyze data through parametric methods and evaluate all the results in the light of methodological rules and approaches. |
Relation with Program Learning Outcome
| Order | Type | Program Learning Outcomes | Level |
|---|---|---|---|
| PLO01 | Bilgi - Kuramsal, Olgusal | Explains the classical, modern and postmodern theories of marketing science. | |
| PLO02 | Bilgi - Kuramsal, Olgusal | Defines scientific methods and tools used in marketing. | 2 |
| PLO03 | Beceriler - Bilişsel, Uygulamalı | Develops research models by determining the variables related to the subjects of marketing science. | 4 |
| PLO04 | Beceriler - Bilişsel, Uygulamalı | Can interpret the results obtained by applying the research models based on the marketing theories. | 4 |
| PLO05 | Beceriler - Bilişsel, Uygulamalı | Can produce solutions to the problems faced by today's marketing profession groups with appropriate methods. | 3 |
| PLO06 | Beceriler - Bilişsel, Uygulamalı | Can implement the basic steps of the methods used in the field of marketing. | 5 |
| PLO07 | Beceriler - Bilişsel, Uygulamalı | Can develop solutions by using the knowledge gained in the field of marketing. | 4 |
| PLO08 | Yetkinlikler - Bağımsız Çalışabilme ve Sorumluluk Alabilme Yetkinliği | Can work effectively by taking responsibility in individual and/or team work. | |
| PLO09 | Yetkinlikler - Öğrenme Yetkinliği | Keeps track of the latest developments in the field as a recognition of the need for lifelong learning and constant renewal. | |
| PLO10 | Yetkinlikler - Öğrenme Yetkinliği | Utilizes scientific sources in the field, collect the data, synthesizes the obtained information and presents the outcomes effectively. | 4 |
| PLO11 | Yetkinlikler - Öğrenme Yetkinliği | Can use information and communication technologies to access, analyze and interpret information in the field of marketing. | 5 |
| PLO12 | Yetkinlikler - İletişim ve Sosyal Yetkinlik | Can present information, comments and suggestions related to the field of study in written and orally in accordance with the requirements of academic and business life. | |
| PLO13 | Yetkinlikler - Alana Özgü Yetkinlik | Can develop and apply original research methods and tools that will contribute to the development of the field of marketing. | 4 |
| PLO14 | Yetkinlikler - Alana Özgü Yetkinlik | Acts in accordance with the ethical and legal issues encountered in the field of marketing science and marketing profession. | |
| PLO15 | Yetkinlikler - Alana Özgü Yetkinlik | Gains awareness of social, cultural and environmental issues. | |
| PLO16 | Yetkinlikler - Alana Özgü Yetkinlik | Forms the basis for the decision-making process of organizations and practitioners. |
Week Plan
| Week | Topic | Preparation | Methods |
|---|---|---|---|
| 1 | Introduction to Data Analysis | Reading related topics | Öğretim Yöntemleri: Anlatım |
| 2 | Types of Data Analysis | Reading related topics | Öğretim Yöntemleri: Anlatım, Soru-Cevap |
| 3 | Classification of Techniques | Reading related topics | Öğretim Yöntemleri: Anlatım |
| 4 | Examining the Data: Graphical Examination | Reading related topics | Öğretim Yöntemleri: Anlatım, Gösterip Yaptırma |
| 5 | Examining the Data: Missing Data and Outliers | Reading related topics, solving examples | Öğretim Yöntemleri: Anlatım, Gösterip Yaptırma |
| 6 | Examining the Data:Testing Assumptions of Techniques | Reading related chapters, solving problems | Öğretim Yöntemleri: Anlatım, Gösterip Yaptırma |
| 7 | Factor Analysis | Reading related topics, solving examples | Öğretim Yöntemleri: Anlatım, Gösterip Yaptırma, Problem Çözme |
| 8 | Mid-Term Exam | Studying | Ölçme Yöntemleri: Yazılı Sınav |
| 9 | Dependence Techniques: Regression Analysis I | Reading related topics | Öğretim Yöntemleri: Anlatım, Soru-Cevap |
| 10 | Dependence Techniques: Regression Analysis II | Reading related topics, solving examples | Öğretim Yöntemleri: Anlatım, Gösterip Yaptırma, Problem Çözme |
| 11 | Dependence Techniques: Discriminant Analysis | Reading related topics | Öğretim Yöntemleri: Anlatım, Gösterip Yaptırma, Problem Çözme |
| 12 | Dependence Techniques: Analysis of Variance | Reading related topics | Öğretim Yöntemleri: Anlatım, Problem Çözme |
| 13 | Interdependence Techniques: Cluster Analysis | Reading related topics | Öğretim Yöntemleri: Anlatım, Problem Çözme |
| 14 | Advanced Techniques: Structural Equation Modeling (S.E.M.) I | Reading related topics | Öğretim Yöntemleri: Anlatım |
| 15 | Advanced Techniques: Structural Equation Modeling (S.E.M.) II | Reading related topics, solving examples | Öğretim Yöntemleri: Anlatım, Problem Çözme |
| 16 | Term Exams | Studying | Ölçme Yöntemleri: Yazılı Sınav |
| 17 | Term Exams | Studying | Ölçme Yöntemleri: Yazılı Sınav |
Student Workload - ECTS
| Works | Number | Time (Hour) | Workload (Hour) |
|---|---|---|---|
| Course Related Works | |||
| Class Time (Exam weeks are excluded) | 14 | 4 | 56 |
| Out of Class Study (Preliminary Work, Practice) | 14 | 8 | 112 |
| Assesment Related Works | |||
| Homeworks, Projects, Others | 2 | 4 | 8 |
| Mid-term Exams (Written, Oral, etc.) | 1 | 12 | 12 |
| Final Exam | 1 | 24 | 24 |
| Total Workload (Hour) | 212 | ||
| Total Workload / 25 (h) | 8,48 | ||
| ECTS | 8 ECTS | ||