BIG DATA ANALYTICS AND MANAGEMENT (ENGLISH, NONTHESIS)
Master	TR-NQF-HE: Level 7	QF-EHEA: Second Cycle	EQF-LLL: Level 7

Course Introduction and Application Information

Course Code	Course Name	Semester	Theoretical	Practical	Credit	ECTS
BDA5011	Big Data and Analytics	Fall Spring	3	0	3	12

This catalog is for information purposes. Course status is determined by the relevant department at the beginning of semester.

Basic information

Language of instruction:	English
Type of course:	Departmental Elective
Course Level:
Mode of Delivery:	Face to face
Course Coordinator :	Assist. Prof. ECE GELAL SOYAK
Course Objectives:	This course provides an overview of the fields of big data analytics and data science. Topics are covered in the context of data analytics include the terminology and the core concepts behind big data problems, applications, and systems. In this course, the students learn how to use Hadoop and related Big Data Processing tools that are used for scalable big data analysis and have made it easier and more accessible.

Learning Outcomes

The students who have succeeded in this course;
• Get a broad understanding of this high trend Big Data and NoSQL concepts
• Learn big data analytics skills that are highly demanded on the market currently.
• Be able to implement and use MapReduce programs and NoSQL databases.

Course Content

In this course, the technologies associated with big data analytics including NoSQL databases, moving data into Hadoop, real-time data analysis using HBase, big data analytical tools such as Apache Hive and Pig will be covered.

Weekly Detailed Course Contents

Week	Subject	Related Preparation
1)	Discussion of course contents, Overview of Hadoop ecosystem
2)	Hadoop Architecture, Basic Linux Commands, Hadoop Installation
3)	Data Transfer into and out from Hadoop (Hands on Exercise -SQOOP)
4)	Overview of Apache Pig and Pig Latin Basics
5)	Pig Latin Operators and Examples
6)	Programming with Pig and Examples
7)	Overview of Hive and Hive Architecture
8)	Hands on HIVE Exercises in Configuration, Database and table operations
9)	Hands on HIVE Exercises in Partitions, Buckets, Operators and Built in Functions, Loading data and Overview of Impala
10)	Hands on HIVE Exercises in Views and Indexes, HIVEQL –Where, Order By and JOINS
11)	Overview of Spark and Spark Architecture
12)	Spark & Hands on Exercises
13)	Spark & Hands on Exercises
14)	Student Presentations of Group Assignments

Sources

Course Notes / Textbooks:	Lecture notes will be provided.
References:	Big Data Science & Analytics: A Hands-On Approach. Bahga, A. and Madisetti, V., 2016.

Evaluation System

Semester Requirements	Number of Activities	Level of Contribution
Project	1	% 20
Midterms	1	% 30
Final	1	% 50
Total		% 100
PERCENTAGE OF SEMESTER WORK		% 30
PERCENTAGE OF FINAL WORK		% 70
Total		% 100

ECTS / Workload Table

Activities	Number of Activities	Duration (Hours)	Workload
Course Hours	14	3	42
Study Hours Out of Class	14	10	140
Presentations / Seminar	1	20	20
Project	1	30	30
Homework Assignments	2	30	60
Final	1	20	20
Total Workload			312

Contribution of Learning Outcomes to Programme Outcomes

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

	Program Outcomes	Level of Contribution
1)	To be able to follow and critically analyze scientific literature and use it effectively in solving engineering problems.
2)	To be able to design, plan, implement and manage original projects related to Big Data Analytics and Management.
3)	To be able to carry out studies on Big Data Analytics and Management independently, take scientific responsibility and critically evaluate the results obtained.
4)	Effectively present the results of his/her research and projects in written, oral and visual form in accordance with academic standards.
5)	To be able to conduct independent research in the field of Big Data Analytics and Management, develop original ideas and transfer this knowledge to practice.
6)	Uses advanced theoretical and practical knowledge specific to the field of Big Data Analytics and Management effectively.
7)	Acts in accordance with professional, scientific and ethical values; takes responsibility by considering the social, environmental and ethical impacts of engineering applications.