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
Assignment
4
15
Quiz
3
15
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
2,5
2,5
Preparation for Quiz
3
1
3
Individual or group work
1
5
5
Preparation for Final exam
1
40
40
Course hours
14
3
42
Preparation for Midterm exam
1
50
50
Laboratory (including preparation)
Final exam
1
2,5
2,5
Homework
4
3
12
Quiz
3
,5
1,5
Total Workload
158,5
Total Workload / 30
5,28
ECTS Credits of the Course
5
LANGUAGE OF INSTRUCTION
Turkish
WORK PLACEMENT(S)
No
KEY LEARNING OUTCOMES (KLO) / MATRIX OF LEARNING OUTCOMES (LO)