MECHATRONICS ENGINEERING | |||||
Bachelor | TR-NQF-HE: Level 6 | QF-EHEA: First Cycle | EQF-LLL: Level 6 |
Course Code | Course Name | Semester | Theoretical | Practical | Credit | ECTS |
COP4434 | IBM Big Data and Analytics | Fall Spring |
3 | 0 | 3 | 6 |
The course opens with the approval of the Department at the beginning of each semester |
Language of instruction: | En |
Type of course: | Departmental Elective |
Course Level: | Bachelor |
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 |
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. |
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. |
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. |
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 |
Course Notes: | Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die by E. Siegel |
References: |
Semester Requirements | Number of Activities | Level of Contribution |
Attendance | 0 | % 0 |
Laboratory | 0 | % 0 |
Application | 0 | % 0 |
Field Work | 0 | % 0 |
Special Course Internship (Work Placement) | 0 | % 0 |
Quizzes | 2 | % 5 |
Homework Assignments | 0 | % 0 |
Presentation | 0 | % 0 |
Project | 1 | % 25 |
Seminar | 0 | % 0 |
Midterms | 1 | % 30 |
Preliminary Jury | 0 | % 0 |
Final | 1 | % 40 |
Paper Submission | 0 | % 0 |
Jury | 0 | % 0 |
Bütünleme | % 0 | |
Total | % 100 | |
PERCENTAGE OF SEMESTER WORK | % 35 | |
PERCENTAGE OF FINAL WORK | % 65 | |
Total | % 100 |
Activities | Number of Activities | Duration (Hours) | Workload |
Course Hours | 14 | 3 | 42 |
Laboratory | 0 | 0 | 0 |
Application | 0 | 0 | 0 |
Special Course Internship (Work Placement) | 0 | 0 | 0 |
Field Work | 0 | 0 | 0 |
Study Hours Out of Class | 0 | 0 | 0 |
Presentations / Seminar | 0 | 0 | 0 |
Project | 1 | 20 | 20 |
Homework Assignments | 0 | 0 | 0 |
Quizzes | 2 | 14 | 28 |
Preliminary Jury | 0 | 0 | 0 |
Midterms | 1 | 25 | 25 |
Paper Submission | 0 | 0 | 0 |
Jury | 0 | 0 | 0 |
Final | 1 | 30 | 30 |
Total Workload | 145 |
No Effect | 1 Lowest | 2 Low | 3 Average | 4 High | 5 Highest |
Program Outcomes | Level of Contribution | |
1) | Build up a body of knowledge in mathematics, science and Mechatronics Engineering subjects; use theoretical and applied information in these areas to model and solve complex engineering problems. | |
2) | Identify, formulate, and solve complex Mechatronics Engineering problems; select and apply proper modeling and analysis methods for this purpose. | |
3) | Design complex Mechatronic systems, processes, devices or products under realistic constraints and conditions, in such a way as to meet the desired result; apply modern design methods for this purpose. | |
4) | Devise, select, and use modern techniques and tools needed for solving complex problems in Mechatronics Engineering practice; employ information technologies effectively. | |
5) | Design and conduct numerical or pysical experiments, collect data, analyze and interpret results for investigating the complex problems specific to Mechatronics Engineering. | |
6) | Cooperate efficiently in intra-disciplinary and multi-disciplinary teams; and show self-reliance when working on Mechatronics-related problems. | |
7) | Ability to communicate effectively in English and Turkish (if he/she is a Turkish citizen), both orally and in writing. Write and understand reports, prepare design and production reports, deliver effective presentations, give and receive clear and understandable instructions. | |
8) | Recognize the need for life-long learning; show ability to access information, to follow developments in science and technology, and to continuously educate oneself. | |
9) | Develop an awareness of professional and ethical responsibility, and behave accordingly. Be informed about the standards used in Mechatronics Engineering applications. | |
10) | Learn about business life practices such as project management, risk management, and change management; develop an awareness of entrepreneurship, innovation, and sustainable development. | |
11) | Acquire knowledge about the effects of practices of Mechatronics Engineering on health, environment, security in universal and social scope, and the contemporary problems of Mechatronics engineering; is aware of the legal consequences of Mechatronics engineering solutions. |