COMPUTER ENGINEERING
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:	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)	Adequate knowledge in mathematics, science and computer engineering; the ability to use theoretical and practical knowledge in these areas in complex engineering problems.
2)	Ability to identify, formulate, and solve complex engineering problems; ability to select and apply appropriate analysis and modeling methods for this purpose.
3)	Ability to design a complex system, process, device or product to meet specific requirements under realistic constraints and conditions; ability to apply modern design methods for this purpose.
4)	Ability to develop, select and use modern techniques and tools necessary for the analysis and solution of complex problems encountered in computer engineering applications; ability to use information technologies effectively.	5
5)	Ability to design, conduct experiments, collect data, analyze and interpret results for the study of complex engineering problems or computer engineering research topics.	4
6)	Ability to work effectively within and multi-disciplinary teams; individual study skills.
7)	Ability to communicate effectively in verbal and written Turkish; knowledge of at least one foreign language; ability to write active reports and understand written reports, to prepare design and production reports, to make effective presentations, to give and receive clear and understandable instructions.
8)	Awareness of the necessity of lifelong learning; ability to access information, to follow developments in science and technology and to renew continuously.
9)	To act in accordance with ethical principles, professional and ethical responsibility; information on the standards used in engineering applications.
10)	Information on business practices such as project management, risk management and change management; awareness of entrepreneurship and innovation; information about sustainable development.
11)	Knowledge of the effects of engineering practices on health, environment and safety in the universal and social scale and the problems of the era reflected in engineering; awareness of the legal consequences of engineering solutions.