Course Details

Course Information

Semester	Course Unit Code	Course Unit Title	T+P+L	Credit	Number of ECTS Credits	Last Updated Date
4	DSBE512	DATA VISIUALISATION AND MANAGEMENT	0+0+0	3	7,5	13.05.2025

Course Details

Language of Instruction	English
Level of Course Unit	Master's Degree
Department / Program	DATA SCIENCE
Type of Program	Formal Education
Type of Course Unit	Elective
Course Delivery Method	Face To Face
Objectives of the Course	Understanding the role of statistics in research and business practices. Developing skills in data gathering and analysis. Interpreting statistical resultsAraştırma ve iş uygulamalarında istatistiğin rolünü anlamak. Veri toplama ve analiz etme becerileri geliştirmek. İstatistiksel sonuçları yorumlayabilmek.
Course Content	The focus of the course is on broad treatment of applications of statistics concentrating on techniques used in fields of business and economics. The course aims to focus on how to define, collect, organize, visualize and analyze data for a business problem by applying formal statistical techniques. Topics include descriptive statistics, parameter estimation, confidence intervals, hypothesis testing, analysis of variance and linear regression. In addition, course content includes computer implementations using available up-to-date statistical software.
Course Methods and Techniques	The course covers data visualization principles, data cleaning, transformation techniques, dashboard design, and practical use of software such as Excel, Tableau, and R.
Prerequisites and co-requisities	None
Course Coordinator	Associate Prof.Dr. Umut Türk
Name of Lecturers	None
Assistants	None
Work Placement(s)	No

Recommended or Required Reading

Resources	-
Course Notes	"Data Visualization: A Practical Introduction" by Kieran Healy "Storytelling with Data: A Data Visualization Guide for Business Professionals" by Cole Nussbaumer Knaflic

Course Category

Engineering	%20
Social Sciences	%80

Planned Learning Activities and Teaching Methods

Activities are given in detail in the section of "Assessment Methods and Criteria" and "Workload Calculation"

Assessment Methods and Criteria

ECTS Allocated Based on Student Workload

Activities	Quantity	Duration	Total Work Load
Ev Ödevi	7	3	21
Sunum	2	15	30
Proje	2	30	60
Araştırma	2	20	40
Ders dışı çalışma	1	30	30
Yüz Yüze Ders	14	3	42
Total Work Load			Number of ECTS Credits 7,5 223

Course Learning Outcomes: Upon the successful completion of this course, students will be able to:

No	Learning Outcomes
1	Calculate descriptive statistics numerically and graphically.
2	Compute confidence intervals for unknown parameters of distributions.
3	Perform hypothesis testing with two samples.
4	Use linear, multiple regression models in data analysis.

Weekly Detailed Course Contents

Week	Topics	Study Materials	Materials
1	Introduction and the Role of Statistics
2	Types of Data and Data Collection Methods
3	Descriptive Statistics: Mean, Median, Mode, Dispersion
4	Graphical Representations and Principles of Visualization
5	Basic Statistics and Charting with Excel
6	Confidence Intervals
7	Hypothesis Testing: One and Two Samples
8	Estimation techniques: point estimation and interval estimation
9	Analysis of Variance (ANOVA)
10	Linear Regression Analysis
11	Multiple Regression and Model Interpretation
12	Data Cleaning and Transformation Techniques
13	Data Visualization with Tableau and/or Power BI
14	Storytelling with data: Crafting a compelling narrative

Contribution of Learning Outcomes to Programme Outcomes

Contribution: 1: Very Slight 2:Slight 3:Moderate 4:Significant 5:Very Significant

https://sis.agu.edu.tr/oibs/bologna/progCourseDetails.aspx?curCourse=75115&lang=en