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 |
| SEN4016 | Multivariate Data Analysis | 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: | Non-Departmental Elective |
| Course Level: | Bachelor’s Degree (First Cycle) |
| Mode of Delivery: | Face to face |
| Course Coordinator : | Prof. Dr. MEHMET ALPER TUNGA |
| Recommended Optional Program Components: | None. |
| Course Objectives: | The students will have the ability of applying specific techniques included in multivariate analysis such as principle component analysis, factor analysis, linear regression to specific problems. |
Learning Outcomes
|
The students who have succeeded in this course; 1. Describe multivariate data analysis concepts 2. Define the properties and limitations of PCA and compute PCA through different ways 3. Describe the types of factoring and factor computation 4. Define metric and non-metric scales 5. Describe simple and multiple correspondence analysis and chi squared distances 6. Define variations of MANOVA 7. Evaluate regression coefficients, parameter estimation, hypothesis testing 8. Describe deduction, induction, estimation, tests, correlation 9. Define univariate and multivariate filters |
Course Content
| The course content is composed of principle component analysis (pca), factor analysis, multidimensional scaling, correspondence analysis, multivariate analysis of variance (manova), multiple linear regression, statistical inference, feature subset selection. |
Weekly Detailed Course Contents
| Week | Subject | Related Preparation |
| 1) | Introduction | |
| 2) | Principle Component Analysis (PCA) | |
| 3) | Principle Component Analysis (PCA) | |
| 4) | Factor Analysis | |
| 5) | Factor Analysis | |
| 6) | Multidimensional Scaling | |
| 7) | Correspondence Analysis | |
| 8) | Multivariate Analysis of Variance (MANOVA) | |
| 9) | Multiple Linear Regression | |
| 10) | Multiple Linear Regression | |
| 11) | Statistical Inference | |
| 12) | Statistical Inference | |
| 13) | Feature Subset Selection | |
| 14) | Feature Subset Selection |
Sources
| Course Notes / Textbooks: | Multivariate Data Analysis, 7/E, Joseph F. Hair, Jr, William C. Black, Barry J. Babin, Rolph E. Anderson, Pearson, 2010, 9780138132637 |
| References: | Yok - None. |
Evaluation System
| Semester Requirements | Number of Activities | Level of Contribution |
| Quizzes | 4 | % 20 |
| Homework Assignments | 2 | % 10 |
| Midterms | 1 | % 30 |
| Final | 1 | % 40 |
| Total | % 100 | |
| PERCENTAGE OF SEMESTER WORK | % 60 | |
| PERCENTAGE OF FINAL WORK | % 40 | |
| Total | % 100 | |
ECTS / Workload Table
| Activities | Number of Activities | Duration (Hours) | Workload |
| Course Hours | 14 | 3 | 42 |
| Study Hours Out of Class | 4 | 5 | 20 |
| Homework Assignments | 2 | 5 | 10 |
| Quizzes | 4 | 3 | 12 |
| Midterms | 1 | 15 | 15 |
| Final | 1 | 17 | 17 |
| Total Workload | 116 | ||
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 |