1	Course Title:	DATA VISUALIZATION
2	Course Code:	EKO5119
3	Type of Course:	Optional
4	Level of Course:	Second Cycle
5	Year of Study:	1
6	Semester:	1
7	ECTS Credits Allocated:	4
8	Theoretical (hour/week):	2
9	Practice (hour/week) :	0
10	Laboratory (hour/week) :	0
11	Prerequisites:	None
12	Recommended optional programme components:	None
13	Language:	Turkish
14	Mode of Delivery:	Face to face
15	Course Coordinator:	Prof. Dr. ZEHRA BERNA AYDIN
16	Course Lecturers:
17	Contactinformation of the Course Coordinator:	e-mail:berna@uludag.edu.tr Tel: 224 2941119 Adres: Uludağ Üniversitesi, İktisadi ve İdari Bilimler Fakültesi, Ekonometri Bölümü,16059, Görükle/Bursa
18	Website:
19	Objective of the Course:	It will be ensured that complex data are presented in a visually understandable way so that they can be easily perceived.
20	Contribution of the Course to Professional Development	Ability to perceive and interpret complex data.

21

Learning Outcomes:

1 The importance of data visualization will be explained.; 2 Data visualization concepts will be introduced.; 3 Visualization tools that can work effectively in multidimensional and very large databases will be introduced.; 4 Information about the types of networks, their resistance and how they propagate information will be given.; 5 It will be explained how the graphics should be interpreted.;

22	Course Content:

Week	Theoretical	Practical
1	Data Visualization definition and conceptual framework
2	Visual Perception, Color Selection and Design Principles
3	Data Visualization Software
4	Data Visualization Software
5	Data Acquisition and data parsing
6	Multivariate Drawing and Graphing
7	Data visualization with Excel and SPSS
8	Data visualization with R
9	Data visualization with R
10	Data visualization with Python
11	Data visualization with Python
12	Introduction of the Tableau program
13	Data Visualization in Business Intelligence Applications - 1
14	Data Visualization in Business Intelligence Applications - 2

23	Textbooks, References and/or Other Materials:	-Simon, P. (2014), “The visual organization: data visualization”, Big Data, and the quest for better decisions. John Wiley & Sons. -Tugay Bilgin, T., Yılmaz Çamurcu, A. (2008), Multidimensional Data Visualization , Çanakkale Onsekiz Mart universty, Çanakkale. Visualizing Data, Ben Fry, O'reilly Sosyal Ağ Analizi, Necmi Gürsakal, Dora Ware, C. (2010). Visual thinking: For design. Morgan Kaufmann. Camões, J. (2016). Data at Work: Best practices for creating effective charts and information graphics in Microsoft Excel, New Riders.
24	Assesment

TERM LEARNING ACTIVITIES	NUMBER	PERCENT
Midterm Exam	0	0
Quiz	0	0
Homeworks, Performances	0	0
Final Exam	1	100
Total	1	100
Contribution of Term (Year) Learning Activities to Success Grade		0
Contribution of Final Exam to Success Grade		100
Total		100
Measurement and Evaluation Techniques Used in the Course	Final exam and application of theoretical knowledge in class.
Information	This course evaluated with an absolute evaluation system.

25

ECTS / WORK LOAD TABLE

Activites	NUMBER	TIME [Hour]	Total WorkLoad [Hour]
Theoretical	14	2	28
Practicals/Labs	0	0	0
Self Study and Preparation	14	3	42
Homeworks, Performances	0	5	20
Projects	0	0	0
Field Studies	0	0	0
Midtermexams	1	10	10
Others	0	0	0
Final Exams	1	20	20
Total WorkLoad			120
Total workload/ 30 hr			4
ECTS Credit of the Course			4

26	CONTRIBUTION OF LEARNING OUTCOMES TO PROGRAMME QUALIFICATIONS
PQ1 PQ2 PQ3 PQ4 PQ5 PQ6 PQ7 PQ8 PQ9 PQ10 PQ11 PQ12 LO1 5 4 4 3 4 3 3 4 3 3 3 2 LO2 4 4 3 2 3 4 3 3 4 4 3 4 LO3 3 3 3 4 3 4 4 2 3 3 3 3 LO4 4 4 3 3 2 3 4 3 3 2 3 3 LO5 3 5 4 4 3 4 3 2 4 3 3 4

LO: Learning Objectives

PQ: Program Qualifications

Contribution Level:

1 Very Low

2 Low

3 Medium

4 High

5 Very High