| Course Name | Code | Semester | T+U Hours | Credit | ECTS |
|---|---|---|---|---|---|
| Human Resources Analytics | IKY 519 | 0 | 3 + 0 | 3 | 6 |
| Precondition Courses | |
| Recommended Optional Courses | |
| Course Language | Turkish |
| Course Level | YUKSEK_LISANS |
| Course Type | OPTIONAL |
| Course Coordinator | Prof.Dr. ŞUAYYİP ÇALIŞ |
| Course Lecturers | Prof.Dr. ŞUAYYİP ÇALIŞ, |
| Course Assistants | |
| Course Category | Teaching Suitable For Field |
| Course Objective | The development of HR information systems requires using big data in the the field HR. Therefore it is aimed to analyze the big data based on an evidence-based management approach.
|
| Course Content | HR Analytics includes gathering, processing and analyzing the big data to reach the right decisions. It also includes practical analyzes in the fields of turnover, HR department performance, and performance management. |
| Development Goals |
|---|
|
| # | Course Learning Outcomes | Teaching Methods | Assessment Methods |
|---|---|---|---|
| 1 | Learning the basic concepts about HR Analytics | Problem Solving, | |
| 2 | Gaining in-depth knowledge about HR Analytics | Motivations to Show, Problem Solving, | |
| 3 | Gaining knowledge about software using in HRM field | Problem Solving, | Home Work / Take-home Exam, |
| Week | Course Topics | Preliminary Preparation |
|---|---|---|
| 1 | HR Analytics: Introduction | |
| 2 | HR Analytics: Methods and Software | |
| 3 | Business Intelligence | |
| 4 | Decision Support Systems | |
| 5 | Descriptive Analytics | |
| 6 | Predictive Analytics | |
| 7 | Prescriptive Analytics | |
| 8 | Descriptive Analytics Examples | |
| 9 | Predictive Analytics Examples | |
| 10 | Prescriptive Analytics Examples | |
| 11 | Data Mining | |
| 12 | Data Visualisation | |
| 13 | ||
| 14 | Decision Tree Examples |
| Resources | |
|---|---|
| Course Notes | Marr, Bernard (2018) Veri Stratejisi Büyük Veri ve Nesnelerin İnterneti Nasıl Kar Getirir?, MediaCat, İstanbul. |
| Course Resources | |
| Order | Program Outcomes | Level of Contribution | |||||
|---|---|---|---|---|---|---|---|
| 1 | 2 | 3 | 4 | 5 | |||
| 1 | Deepens the theoretical knowledge of HRM field. | X | |||||
| 2 | Deepens the theoretical knowledge of other related disciplines | X | |||||
| 3 | Examines academic resources related to the field of HRM in a versatile and critical manner | X | |||||
| 4 | Analyzes applications in the field of HRM with a critical point of view. | X | |||||
| 5 | Designs and implements HRM research from different theoretical perspectives. | X | |||||
| 6 | Analyzes, interprets and reports the data obtained with different approaches. | X | |||||
| 7 | evaluate HRM practices in Turkey international perspective. | ||||||
| # | Contribution of Course Learning Outcomes to Program Outcomes | PÇ 1 | PÇ 2 | PÇ 3 | PÇ 4 | PÇ 5 | PÇ 6 | PÇ 7 |
|---|---|---|---|---|---|---|---|---|
| 1 | Learning the basic concepts about HR Analytics | 5 | 1 | 2 | 4 | 2 | 3 | 0 |
| 2 | Gaining in-depth knowledge about HR Analytics | 5 | 1 | 2 | 4 | 2 | 2 | 0 |
| 3 | Gaining knowledge about software using in HRM field | 5 | 1 | 2 | 4 | 2 | 2 | 0 |
| Evaluation System | |
|---|---|
| Semester Studies | Contribution Rate |
| 1. Ara Sınav | 80 |
| 1. Ödev | 20 |
| Total | 100 |
| 1. Final | 50 |
| 1. Yıl İçinin Başarıya | 50 |
| Total | 100 |
| ECTS - Workload Activity | Quantity | Time (Hours) | Total Workload (Hours) |
|---|---|---|---|
| Course Duration (Including the exam week: 16x Total course hours) | 14 | 3 | 42 |
| Mid-terms | 1 | 20 | 20 |
| Assignment | 1 | 20 | 20 |
| Performance Task (Application) | 2 | 20 | 40 |
| Final examination | 1 | 30 | 30 |
| Total Workload | 152 | ||
| Total Workload / 25 (Hours) | 6.08 | ||
| dersAKTSKredisi | 6 | ||