BAHÇEŞEHİR UNIVERSITY BİLGİ PAKETİ / DERS KATALOĞU
| MOLECULAR BIOLOGY AND GENETICS | |||||
| 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 |
| COP4408 | OBASE Business Intelligence | Spring | 3 | 0 | 3 | 5 |
| 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 : | Prof. Dr. ADEM KARAHOCA |
| Course Lecturer(s): |
Prof. Dr. ADEM KARAHOCA |
| Recommended Optional Program Components: | None |
| Course Objectives: | The course provides the student with an introduction to the basic and more advanced concepts of Business Intelligence and discusses the architectures of possible solutions. |
Learning Outcomes
|
The students who have succeeded in this course; 1. Define basic concepts and categories of business intelligence and business intelligence market 2. Describe data warehouse architectures 3. Define relational models, construct normalized data models and identify queries to data sources with SQL. 4. Discuss case studies in terms of business intelligence concepts 5. Specify data mining and clustering methods 6. Describe neural networks 7. Define decision trees 8. Identify business intelligence front end applications 9. Prepare project presentations |
Course Content
| Concepts of business intelligence, data warehousing, rdbms concepts, modeling the dimensions and creating the aggregations, panel - case studies, introduction to data mining unsupervised methods, supervised methods, business intelligence front end |
Weekly Detailed Course Contents
| Week | Subject | Related Preparation |
| 1) | Introduction to Business Intelligence | |
| 2) | Data Warehousing | |
| 3) | RDBMS Concepts I | |
| 4) | RDBMS Concepts II | |
| 5) | Modeling the Dimensions and Creating the Aggregations | |
| 6) | Modeling the Dimensions and Creating the Aggregations | |
| 7) | Panel - Case Study: Migros | |
| 8) | Introduction to Data Mining Unsupervised Methods | |
| 9) | Introduction to Data Mining Unsupervised Methods | |
| 10) | Supervised Methods | |
| 11) | Supervised Methods | |
| 12) | Business Intelligence Front End | |
| 13) | Project Presentations | |
| 14) | Panel – Case Study: Turkcell |
Sources
| Course Notes / Textbooks: | Corporate Information Factory, W. H. Inmon, Claudia Imhoff, Ryan Sousa, 2001, 0471399612 Business intelligence : a managerial approach, E. Turban, R. Sharda, J.E. Arnsson, D. King, 2007, 013234761X The Data Warehouse Toolkit, R. Kimball, M. Ross, 1996, 0471153370 |
| References: | Yok |
Evaluation System
| Semester Requirements | Number of Activities | Level of Contribution |
| Attendance | 1 | % 10 |
| Project | 1 | % 35 |
| Midterms | 1 | % 15 |
| Final | 1 | % 40 |
| Total | % 100 | |
| PERCENTAGE OF SEMESTER WORK | % 25 | |
| PERCENTAGE OF FINAL WORK | % 75 | |
| Total | % 100 | |
ECTS / Workload Table
| Activities | Number of Activities | Duration (Hours) | Workload |
| Course Hours | 14 | 3 | 42 |
| Study Hours Out of Class | 3 | 5 | 15 |
| Project | 1 | 20 | 20 |
| Midterms | 1 | 18 | 18 |
| Final | 1 | 20 | 20 |
| Total Workload | 115 | ||
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) | Utilize the wealth of information stored in computer databases to answer basic biological questions and solve problems such as diagnosis and treatment of diseases. | 3 |
| 2) | Acquire an ability to compile and analyze biological information, clearly present and discuss the conclusions, the inferred knowledge and the arguments behind them both in oral and written format. | 4 |
| 3) | Develop critical, creative and analytical thinking skills. | 5 |
| 4) | Develop effective communication skills and have competence in scientific speaking, reading and writing abilities in English and Turkish. | 3 |
| 5) | Gain knowledge of different techniques and methods used in genetics and acquire the relevant laboratory skills. | 4 |
| 6) | Detect biological problems, learn to make hypothesis and solve the hypothesis by using variety of experimental and observational methods. | 4 |
| 7) | Gain knowledge of methods for collecting quantitative and qualitative data and obtain the related skills. | 3 |
| 8) | Conduct research through paying attention to ethics, human values and rights. Pay special attention to confidentiality of information while working with human subjects. | 5 |
| 9) | Obtain basic concepts used in theory and practices of molecular biology and genetics and establish associations between them. | 4 |
| 10) | Search and use literature to improve himself/herself and follow recent developments in science and technology. | 5 |
| 11) | Be aware of the national and international problems in the field and search for solutions. | 4 |