TYPE OF COURSE UNIT	Elective Course
LEVEL OF COURSE UNIT	Master's Degree Without Thesis
YEAR OF STUDY	-
SEMESTER	-
NUMBER OF ECTS CREDITS ALLOCATED	10
NAME OF LECTURER(S)	Professor Hakkı Okan Yeloğlu
LEARNING OUTCOMES OF THE COURSE UNIT	At the end of this course, the students; 1) know basic big data concepts and terminology 2) understand the business impacts of big data 3) understand fundamental big data processing approaches 4) know big data storage/processing technologies
MODE OF DELIVERY	Face to face
PRE-REQUISITES OF THE COURSE	No
RECOMMENDED OPTIONAL PROGRAMME COMPONENT
COURSE DEFINITION	Big Data is a new field impacting many areas of science, engineering, and industry. Nowadays, data is increasing in volume, is collected faster and has a more complex structure. The companies need to analyze big data in order to increase efficiency, decrease cost and reach the customer easily. In the scope of this course, the fundamentals concepts of big data will be introduced and the fundamental platforms (such as Hadoop, Spark) for data analysis will be introduced. Also, data storage methods, data processing methods and various algorithms will be presented in the course. Lastly, data visualization will be introduced.
COURSE CONTENTS	WEEK TOPICS 1st Week Big data: concepts and terminology 2nd Week Big data: business motvations and drivers 3rd Week Big data: adoption and planning 4th Week Big data: business intelligence 5th Week Distributed Storage and Distributed Processing Apache Hadoop 6th Week Data Warehouse, NoSQL databases 7th Week MapReduce I 8th Week Midterm 9th Week MapReduce II 10th Week Apache Hive 11th Week Sqoop and Kafka 12th Week Big data visualization 13th Week Case studies 14th Week Project presentation
RECOMENDED OR REQUIRED READING	Tom White. Hadoop: The Definitive Guide Third Edition
PLANNED LEARNING ACTIVITIES AND TEACHING METHODS	Lecture,Project
ASSESSMENT METHODS AND CRITERIA	Quantity Percentage(%) Mid-term 1 30 Project 1 30 Total(%) 60 Contribution of In-term Studies to Overall Grade(%) 60 Contribution of Final Examination to Overall Grade(%) 40 Total(%) 100
ECTS WORKLOAD	Activities Number Hours Workload Midterm exam 1 2 2 Preparation for Quiz Individual or group work 14 7 98 Preparation for Final exam 1 50 50 Course hours 14 3 42 Preparation for Midterm exam 1 45 45 Laboratory (including preparation) Final exam 1 2 2 Homework Project 1 50 50 Total Workload 289 Total Workload / 30 9,63 ECTS Credits of the Course 10
LANGUAGE OF INSTRUCTION	Turkish
WORK PLACEMENT(S)	No