SECTION I: GENERAL INFORMATION ABOUT THE COURSE

Course Code	Course Name	Year	Semester	Theoretical	Practical	Credit	ECTS
BVA5104	Big Data Technologies	2	Spring	1	2	2	6

Course Type :	University Elective
Cycle:	Associate      TQF-HE:5. Master`s Degree      QF-EHEA:Short Cycle      EQF-LLL:5. Master`s Degree
Language of Instruction:	Turkish
Prerequisities and Co-requisities:	N/A
Mode of Delivery:
Name of Coordinator:	Instructor ÖZGE DEMİR
Dersin Öğretim Eleman(lar)ı:	Instructor ÖZGE DEMİR
Dersin Kategorisi:	Competency Development (University Elective)

SECTION II: INTRODUCTION TO THE COURSE

Course Objectives & Content

Course Objectives:

The objective of this course is to introduce students to the concept of big data and to provide them with knowledge of current technologies used in processing, storing, and analyzing this data. The course covers the architectures and application areas of big data platforms such as Hadoop, Spark, and NoSQL databases. It also aims to develop students' data processing, analysis, and management skills in big data environments. A key objective of the course is to develop competence in selecting and applying appropriate technologies to address real-world big data problems.

Course Content:

This course covers the concept of big data, its characteristics, and application areas. Within the big data ecosystem, distributed file systems and parallel data processing approaches are examined. The Hadoop ecosystem, Hadoop Distributed File System (HDFS), and MapReduce architecture; the core components and data processing model of Apache Spark are included in the course content. Furthermore, NoSQL databases, data models, and use cases are discussed and compared with relational databases. Throughout the course, data collection, storage, processing, and analysis processes in big data environments are examined through application examples; students are supported in identifying and implementing appropriate technologies for different big data problems.

Course Learning Outcomes (CLOs)

Course Learning Outcomes (CLOs) are those describing the knowledge, skills and competencies that students are expected to achieve upon successful completion of the course. In this context, Course Learning Outcomes defined for this course unit are as follows:

Knowledge (Described as Theoritical and/or Factual Knowledge.)

1) Having knowledge about basic information technologies and computer systems.

Skills (Describe as Cognitive and/or Practical Skills.)

1) Having practical knowledge about the technologies used in big data environments.

2) To be able to produce technological solutions to real-world big data problems.

Competences (Described as "Ability of the learner to apply knowledge and skills autonomously with responsibility", "Learning to learn"," Communication and social" and "Field specific" competences.)

1) Gaining competence in data analysis, data processing and database management.

2) Ability to use appropriate software and tools in the data analysis process.

Weekly Course Schedule

Week	Subject	Materials Sharing *
		Related Preparation	Further Study
1)	Introduction to Big Data and Basic Concepts
2)	Introduction to Big Data and Basic Concepts
3)	Data types and sources
4)	Distributed Systems and File Systems
5)	Distributed Systems and File Systems
6)	Access to databases
7)	MongoDB
8)	Midterm
9)	No-SQL
10)	No-SQL
11)	APACHE
12)	APACHE
13)	hadoop
14)	ElastiCache

*These fields provides students with course materials for their pre- and further study before and after the course delivered.

Recommended or Required Reading & Other Learning Resources/Tools

Course Notes / Textbooks:
References:	Big Data: Principles and Best Practices of Scalable Real-Time Data Systems, Nathan Marz, James Warren,Manning Publications, 2015 Designing Data-Intensive Applications,Martin KleppmannO’Reilly Media, 2017 Hadoop: The Definitive GuideTom White O’Reilly Media, 4th Edition, 2015

DERS ÖĞRENME ÇIKTILARI - PROGRAM ÖĞRENME ÇIKTILARI İLİŞKİSİ

Contribution of The Course Unit To The Programme Learning Outcomes

Ders Öğrenme Çıktıları (DÖÇ)	1	2	3	5	4
Program Öğrenme Çıktıları (PÖÇ)
1) Be able to explain the fundamental concepts, theories, and processes of logistics and supply chain management, as well as the roles and responsibilities of the parties involved in the supply chain.
2) It can handle logistics operations such as warehouse management, order processing, transportation, packaging, and labeling.
3) They can apply basic knowledge of foreign trade, marketing, and customs legislation, as well as foreign trade processes (INCOTERMS, delivery and payment methods, etc.).
4) Can evaluate the internal and external environment of logistics companies in terms of economic indicators.
5) Can develop innovative solutions to complex problems encountered in the field of logistics.
6) Can effectively use theoretical knowledge and information technologies in design, planning, and decision-making processes.
7) Through experience gained in workplace practices, theoretical knowledge can be integrated with practical application.
8) They can act with a sense of professional responsibility within the framework of ethical values, principles of social responsibility, labor law, and occupational health and safety regulations.
9) Can manage logistics processes in accordance with the principles of sustainability, green logistics, and environmental awareness.
10) Can effectively participate in teamwork and communication processes.
11) They can follow current developments and technological innovations in the field of logistics.

SECTION III: RELATIONSHIP BETWEEN COURSE UNIT AND COURSE LEARNING OUTCOMES (CLOs)

Level of Contribution of the Course to PLOs

No Effect	1 Lowest	2 Low	3 Average	4 High	5 Highest

	Programme Learning Outcomes	Contribution Level (from 1 to 5)
1)	Be able to explain the fundamental concepts, theories, and processes of logistics and supply chain management, as well as the roles and responsibilities of the parties involved in the supply chain.	1
2)	It can handle logistics operations such as warehouse management, order processing, transportation, packaging, and labeling.	1
3)	They can apply basic knowledge of foreign trade, marketing, and customs legislation, as well as foreign trade processes (INCOTERMS, delivery and payment methods, etc.).	1
4)	Can evaluate the internal and external environment of logistics companies in terms of economic indicators.	2
5)	Can develop innovative solutions to complex problems encountered in the field of logistics.	1
6)	Can effectively use theoretical knowledge and information technologies in design, planning, and decision-making processes.	2
7)	Through experience gained in workplace practices, theoretical knowledge can be integrated with practical application.	2
8)	They can act with a sense of professional responsibility within the framework of ethical values, principles of social responsibility, labor law, and occupational health and safety regulations.	1
9)	Can manage logistics processes in accordance with the principles of sustainability, green logistics, and environmental awareness.	3
10)	Can effectively participate in teamwork and communication processes.	4
11)	They can follow current developments and technological innovations in the field of logistics.	5

SECTION IV: TEACHING-LEARNING & ASSESMENT-EVALUATION METHODS OF THE COURSE

Teaching & Learning Methods of the Course

(All teaching and learning methods used at the university are managed systematically. Upon proposals of the programme units, they are assessed by the relevant academic boards and, if found appropriate, they are included among the university list. Programmes, then, choose the appropriate methods in line with their programme design from this list. Likewise, appropriate methods to be used for the course units can be chosen among those defined for the programme.)

Teaching and Learning Methods defined at the Programme Level	Teaching and Learning Methods Defined for the Course
Lectures
Problem Solving
Homework
Project Preparation
Seminar
Individual and Group Work
Active Participation in Class

Assessment & Evaluation Methods of the Course

(All assessment and evaluation methods used at the university are managed systematically. Upon proposals of the programme units, they are assessed by the relevant academic boards and, if found appropriate, they are included among the university list. Programmes, then, choose the appropriate methods in line with their programme design from this list. Likewise, appropriate methods to be used for the course units can be chosen among those defined for the programme.)

Aassessment and evaluation Methods defined at the Programme Level	Assessment and Evaluation Methods defined for the Course
Midterm
Final Exam
Homework Evaluation

Contribution of Assesment & Evalution Activities to Final Grade of the Course

Measurement and Evaluation Methods	# of practice per semester	Level of Contribution
Homework Assignments	1	% 15.00
Midterms	1	% 35.00
Semester Final Exam	1	% 50.00
Total		% 100
PERCENTAGE OF SEMESTER WORK		% 50
PERCENTAGE OF FINAL WORK		% 50
Total		% 100

SECTION V: WORKLOAD & ECTS CREDITS ALLOCATED FOR THE COURSE

WORKLOAD OF TEACHING & LEARNING ACTIVITIES
Teaching & Learning Activities	# of Activities per semester	Duration (hour)	Total Workload
Course	14	3	42
Laboratory	0	0	0
Application	14	3	42
Special Course Internship (Work Placement)	0	0	0
Field Work	0	0	0
Study Hours Out of Class	0	0	0
Presentations / Seminar	0	0	0
Project	1	1	1
Homework Assignments	1	10	10
Total Workload of Teaching & Learning Activities	-	-	95
WORKLOAD OF ASSESMENT & EVALUATION ACTIVITIES
Assesment & Evaluation Activities	# of Activities per semester	Duration (hour)	Total Workload
Quizzes	1	1	1
Midterms	1	20	20
Semester Final Exam	1	30	30
Total Workload of Assesment & Evaluation Activities	-	-	51
TOTAL WORKLOAD (Teaching & Learning + Assesment & Evaluation Activities)			146
ECTS CREDITS OF THE COURSE (Total Workload/25.5 h)			6