MG3819 Data Analysis

8 ECTS - 4-0 Duration (T+A)- . Semester- 4 National Credit

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

Update Time: 06.06.2023 09:36