PILOTAGE (EN)
Bachelor	TR-NQF-HE: Level 6	QF-EHEA: First Cycle	EQF-LLL: Level 6

Course Introduction and Application Information

Course Code	Course Name	Semester	Theoretical	Practical	Credit	ECTS
COP4434	IBM Big Data and Analytics	Fall	3	0	3	6

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:	Non-Departmental Elective
Course Level:	Bachelor’s Degree (First Cycle)
Mode of Delivery:	Face to face
Course Coordinator :	Dr. Öğr. Üyesi CEMAL OKAN ŞAKAR
Course Lecturer(s):	Prof. Dr. TAŞKIN KOÇAK Dr. Öğr. Üyesi CEMAL OKAN ŞAKAR
Recommended Optional Program Components:	None
Course Objectives:	The students will take lectures from senior executives from IBM. Each lecture will focus on a different subject and the lecturer will share his/her own experiences together with the theoretical basis of the subject. The courses will include Business Analytics & Big Data capabilities and service areas including key concepts, services, IBM software, hardware offerings and IBM assets. In addition, industry use cases are used to illustrate effective use of Big Data services. The courses will help students to prepare for a successful professional career.

Learning Outcomes

The students who have succeeded in this course;
Expected benefits are multidimensional such as:
- Graduating engineers being much more ready for the professional work
- Directing academic research (including thesis) to real life problems and business needs
- Creating new industry projects formed as the application of new technologies
Hence, this course will be another good addition to our activities in industry-academia partnership.

Course Content

The course will include Big Data and analytics capabilities and service areas including key concepts, services, IBM software, hardware offerings and IBM assets. In addition, industry use cases are used to illustrate effective use of Big Data services.

Weekly Detailed Course Contents

Week	Subject	Related Preparation
1)	Big Data & Analytics for Better Business Outcomes
2)	Industry Aligned Big Data, Top Use Cases
3)	Overview of Big Data Technology & IBM Big Data Platform
4)	IBM Big Data Platform, Data Explorer
5)	Data Warehousing
6)	Information Integration, Master Data Management, Guardium, OPTIM
7)	Hadoop Technology
8)	Midterm
9)	Master Data Management for Customer
10)	Integrating Unstructured Data in the Enterprise
11)	Text Analytics
12)	Infrastructure for Big Data & Analytics
13)	Infrastructure for Big Data & Analytics
14)	Recap

Sources

Course Notes / Textbooks:	Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die by E. Siegel
References:

Evaluation System

Semester Requirements	Number of Activities	Level of Contribution
Quizzes	2	% 5
Project	1	% 25
Midterms	1	% 30
Final	1	% 40
Total		% 100
PERCENTAGE OF SEMESTER WORK		% 35
PERCENTAGE OF FINAL WORK		% 65
Total		% 100

ECTS / Workload Table

Activities	Number of Activities	Duration (Hours)	Workload
Course Hours	14	3	42
Project	1	20	20
Quizzes	2	14	28
Midterms	1	25	25
Final	1	30	30
Total Workload			145

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)	Apply scientific methods or concepts in solving aviation-related problems	3
2)	Analyze and interpret data	5
3)	Work effectively on multi‐disciplinary and diverse teams	5
4)	Make professional and ethical decisions	3
5)	Communicate effectively, using both written and oral communication skills	4
6)	Engage in and recognize the need for life‐long learning	5
7)	Use the techniques, skills, and modern technology necessary for professional practice	4
8)	Assess the national and international aviation environment	3
9)	Apply pertinent knowledge in identifying and solving contemporary problems in the field of aviation	3
10)	Apply knowledge of business sustainability to aviation issues	3