PAÜ .:. Eğitim Öğretim Bilgi Sistemi

COURSE INFORMATION

Course Code	Course Title	L+P Hour	Semester	ECTS
UFD 520	BIG DATA ANALYTICS	3 + 0	2nd Semester	6

COURSE DESCRIPTION

Course Level	Master's Degree
Course Type	Elective
Course Objective	Introduce the overview applications, market trend, and the things to learn. Then, I will introduce the fundamental platforms, such as Hadoop, Spark, and other tools, such as IBM System G for Linked Big Data. Afterwards, the course will introduce several data storage methods and how to upload, distribute, and process them. This shall include HDFS, HBase, KV stores, document database, and graph database. The course will go on to introduce different ways of handling analytics algorithms on different platforms. Then, I will introduce visualization issues and mobile issues on Big Data Analytics. Students will then have fundamental knowledge on Big Data Analytics to handle various real-world challenges.
Course Content	Fundamental platforms, such as Hadoop, Spark, and other tools, such as IBM System G for Linked Big Data. data storage methods and how to upload, distribute, and process them. This shall include HDFS, HBase, KV stores, document database, and graph database.
Prerequisites	No the prerequisite of lesson.
Corequisite	No the corequisite of lesson.
Mode of Delivery	Face to Face

COURSE LEARNING OUTCOMES

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COURSE'S CONTRIBUTION TO PROGRAM

ECTS ALLOCATED BASED ON STUDENT WORKLOAD BY THE COURSE DESCRIPTION

Activities	Quantity	Duration (Hour)	Total Work Load (Hour)
Course Duration (14 weeks/theoric+practical)	13	3	39
Hours for off-the-classroom study (Pre-study, practice)	13	2	26
Assignments	2	13	26
Final examination	1	39	39
Midterm	1	26	26
Total Work Load ECTS Credit of the Course			156 6

COURSE DETAILS

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L+P: Lecture and Practice
PQ: Program Learning Outcomes
LO: Course Learning Outcomes

Prospective Student