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Data Science - English - Master
Data Science
BUSN531

DATA ANALYSIS & COMPUTER APPLICATIONS IN MANAGEMENT

Course: BUSN531 - DATA ANALYSIS & COMPUTER APPLICATIONS IN MANAGEMENT
Department: Master of Business Administration - English - Master
Course Type: Course
Status: Required
Language: English
Credit: 3
ECTS: 8
T+P+L: 3 + 0 + 0
Course Coordinator(s): Prof. Dr. Cem TANOVA
Prerequisite: -
Keywords: Research Process

Statistical Analysis

SPSS Software

Data Interpretation

Descriptive and Inferential Techniques

Course Description

This course covers a description of the research process in general and each of the steps involved in more detail. The course focuses on alternative ways of carrying out each step and guidelines for selecting among these alternatives according to the needs of a specific research problem. The course also describes using software packages like SPSS and interpretation of the results of data analysis. Course is designed to enable student to apply commonly used statistical techniques to different datasets. For this purpose, the course will focus on descriptive analysis, correlation, t-test, ANOVA, regression and mediation and moderation analysis. The students will use SPSS software to work on the datasets.

Evaluation Tools (Active Term)

Item	Type	Weight (%)
Midterm	Midterm	40
Final	Final	45
Project	Project	15
Total		100

Course outcomes

No course outcomes have been defined yet.

Course Syllabus

Week	Topic
Week 1	Introduction of the course, overview / Research process
Week 2	Descriptive statistics
Week 3	Introduction to SPSS/Data cleaning
Week 4	Descriptive statistics (Class Practice)
Week 5	Reliability and validity
Week 6	Reliability and validity (Class Practice)
Week 7	Reliability and validity (Class Practice)
Week 8	Midterm
Week 9	T-test, ANOVA
Week 10	T-test, ANOVA (Class Practice)
Week 11	Correlation
Week 12	Correlation (Class Practice)
Week 13	Regression
Week 14	Regression (Class Practice)
Week 15	Data Practice

Reference Books & Course Materials

01 Field, A. 2024. Discovering Statistics Using IBM SPSS Statistics, Sage, London.
02 Pituch, K.A. & Stevens, J.P. 2016. Applied multivariate statistics for the social sciences, Taylor& Francis: New York.
03 Te Grotenhuis, M. & Matthijssen, A. 2016. Basic SPSS Tutorial, Sage: UK.

Learning Outcomes

No learning outcomes have been defined.

Program Outcomes

Demonstrate a thorough understanding of the theories, frameworks, and models in order to assess and comprehend the state of the art in data science.
Review the literature and apply data science theories and methodology in new research and experiments.
Analyze datasets using supervised and unsupervised machine learning techniques.
Design, develop and test statistics and informatics software systems for data management, analysis and problem solving.
Conceptualize and develop efficient visuals for a range of data types and analytical tasks, and carry out independent research on a range of theoretical and applied subjects in visualization and visual analytics.
Obtain a high level of proficiency in communication, problem solving, research or project-related activities and function effectively as a team member or a leader to accomplish a common goal in a multidisciplinary team.
Develop and implement optimal solutions to overcome challenges associated with managing large datasets by utilizing parallel methods, cloud computing, and non-relational data storage and retrieval (NoSQL).
Demonstrate an understanding of the interdisciplinary of data, information, and communications, as well as the ability to evaluate the leading research methods for data collection and analysis.
Demonstrate a deep understanding of the ethical issues surrounding the use of data and apply ethical decision making in real-world data-related applications.
Demonstrate capability of analyzing, synthesizing, and evaluating knowledge from a wide range of fields and be capable of lifelong self-directed learning.

Po-Lo Matrix

The PO-LO matrix has not been populated yet.