BIG DATA MANAGEMENT PROGRAMME COURSE DESCRIPTION

Code	Name of the Course Unit	Semester	In-Class Hours (T+P)	Credit	ECTS Credit
YEM435	BIG DATA MANAGEMENT	7	3	3	7

GENERAL INFORMATION
Language of Instruction :	Turkish
Level of the Course Unit :	, TYY: + , EQF-LLL: , QF-EHEA:
Type of the Course :	Compulsory
Mode of Delivery of the Course Unit	-
Coordinator of the Course Unit
Instructor(s) of the Course Unit	Assist.Prof. SÜREYYA İMRE BIYIKLI
Course Prerequisite	No

OBJECTIVES AND CONTENTS
Objectives of the Course Unit:	This course aims to provide the students with the necessary information to learn about the difficulties encountered in dealing with Big data, to comprehend the big data storage systems and to learn evaluating the different types of big data.
Contents of the Course Unit:	Contents of the course include the subjects such as assessment and evaluation of media contents, database systems, new architectures and design decisions of big data processing systems, processing methods of large-scaled structured data, large-scale data flow, pipeline in big data processing.

KEY LEARNING OUTCOMES OF THE COURSE UNIT (On successful completion of this course unit, students/learners will or will be able to)
Distinguish the differences between qualitative and quantitative data.
Apply the processing methods of large-scale structured data.
Produce news by making the complex data meaningful.
Create new architectures of big data processing systems.

WEEKLY COURSE CONTENTS AND STUDY MATERIALS FOR PRELIMINARY & FURTHER STUDY
Week	Preparatory	Topics(Subjects)	Method
1	Literature Review	Introduction to Big Data Concept	Lecture
2	Literature Review	Big Data and New Media Relation	Demonstrating & Demonstrating with samples
3	Literature Review	Description of Media Contexts	Demonstrating & Demonstrating with samples
4	Literature Review, Using Visual Sources	Database Systems and Data Performance Analysis	Demonstrating & Demonstrating with samples
5	Literature Review, Using Visual Sources	MapReduce Framework	Demonstrating & Demonstrating with samples
6	Literature Review, Using Visual Sources	Apache Spark	Demonstrating & Demonstrating with samples
7	Literature Review, Using Visual Sources	Big SQL Systems	Demonstrating & Demonstrating with samples
8	-	MID-TERM EXAM	-
9	Literature Review, Using Visual Sources	General Revision	Demonstrating & Demonstrating with samples
10	Literature Review, Using Visual Sources	Large-scale Graphics	Demonstrating & Demonstrating with samples
11	Literature Review, Using Visual Sources	Large-scale Flow Operation	Demonstrating & Demonstrating with samples
12	Literature Review, Using Visual Sources	Large-scale Flow Operation Platforms	Demonstrating & Demonstrating with samples
13	Literature Review, Using Visual Sources	Disintegration of Big Data Operations in Pipeline	Demonstrating & Demonstrating with samples
14	Literature Review, Using Visual Sources	Discussion on advanced applications in the field	Demonstrating & Demonstrating with samples
15	Literature Review, Using Visual Sources	Discussion on advanced applications in the field	Demonstrating & Demonstrating with samples
16	-	FINAL EXAM	-
17	-	FINAL EXAM	-

SOURCE MATERIALS & RECOMMENDED READING
Sakr S., Gaber M. M. (2014). Large Scale and Big Data. Florida: An Auerbach Book
Marr B. (2017). Buyuk Veri Is Basinda. Istanbul: Mediacat Books
Davis K. (2016). Ethics of Big Data: Balancing Risk and Innovation. California: O'Reilly Media
Bilgi ve Belge Araştırmaları Dergisi. İstanbul: İstanbul Üniversitesi

ASSESSMENT
Assessment & Grading of In-Term Activities	Number of Activities	Degree of Contribution (%)	Description

Level of Contribution
0	1	2	3	4	5

KNOWLEDGE
Theoretical
	Programme Learning Outcomes	Level of Contribution
		0	1	2	3	4	5
1	List the history of communication, mass media, communication theories and leading theorists.		1
2	List the historical, social and cultural types of communication and explain the related concepts.		1
3	Define the important points of the history and theories of communication through daily life practices and social life.		1

KNOWLEDGE
Factual
	Programme Learning Outcomes	Level of Contribution
		0	1	2	3	4	5
1	Compare the traditional media and new media economic policies.			2
2	Interpret digital culture with constantly updated and self-renewing topics.				3
3	Interpret the technical, socio-political and legal aspects of cyber security issues in the field of new media.					4

SKILLS
Cognitive
	Programme Learning Outcomes	Level of Contribution
		0	1	2	3	4	5
1	Define the basic concepts of communication history, communication theories, traditional and new media channels.		1

SKILLS
Practical
	Programme Learning Outcomes	Level of Contribution
		0	1	2	3	4	5
1	Prepare web pages with CSS codes.	0
2	Produce creative content in new media environments, create an image and sound and practical studies about programming.			2
3	Analyze the sub-texts and their semantics of the studies presented to the society by mass media.		1
4	Use qualitative and quantitative elements to construct arguments on studies in the field of communication.					4

OCCUPATIONAL
Autonomy & Responsibility
	Programme Learning Outcomes	Level of Contribution
		0	1	2	3	4	5
1	Manage social media accounts of brands, corporate firms and public institutions thanks to its advanced knowledge in content production and user experience.			2

OCCUPATIONAL
Learning to Learn
	Programme Learning Outcomes	Level of Contribution
		0	1	2	3	4	5
1	Review local and foreign studies in the field of New Media. Creates innovative works in his/her field.		1
2	Criticize the effects of social media activities on socio-political field.		1

OCCUPATIONAL
Communication & Social
	Programme Learning Outcomes	Level of Contribution
		0	1	2	3	4	5
1	Plan scientific studies in any area that can be encountered in different disciplines and transfer them to people from different disciplines.				3
2	Determine how much of the content produced by the media is right and wrong.			2

OCCUPATIONAL
Occupational and/or Vocational
	Programme Learning Outcomes	Level of Contribution
		0	1	2	3	4	5
1	Follows the developments that have begun to guide the present and the future such as "Software". Produce various software products for different sectors.		1
2	Design using new architectures of big data processing systems.						5
3	Determine the logic of operation of artificial intelligence algorithms and determines the possible effects on media and indirectly society.			2

WORKLOAD & ECTS CREDITS OF THE COURSE UNIT
Workload for Learning & Teaching Activities
Type of the Learning Activites	Learning Activities (# of week)	Duration (hours, h)	Workload (h)
Lecture & In-Class Activities	14	3	42
Preliminary & Further Study	13	2	26
Land Surveying	0	0	0
Group Work	10	1	10
Laboratory	0	0	0
Reading	13	1	13
Assignment (Homework)	13	2	26
Project Work	1	30	30
Seminar	0	0	0
Internship	0	0	0
Technical Visit	0	0	0
Web Based Learning	0	0	0
Implementation/Application/Practice	0	0	0
Practice at a workplace	0	0	0
Occupational Activity	0	0	0
Social Activity	0	0	0
Thesis Work	0	0	0
Field Study	0	0	0
Report Writing	0	0	0
Final Exam	1	1	1
Preparation for the Final Exam	1	20	20
Mid-Term Exam	1	1	1
Preparation for the Mid-Term Exam	1	10	10
Short Exam	0	0	0
Preparation for the Short Exam	0	0	0
TOTAL	68	0	179