| MATHEMATICS (TURKISH, PHD) | |||||
| PhD | TR-NQF-HE: Level 8 | QF-EHEA: Third Cycle | EQF-LLL: Level 8 | ||
| Course Code | Course Name | Semester | Theoretical | Practical | Credit | ECTS |
| COP4434 | IBM Big Data and Analytics | Fall | 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: | |
| 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 |