- Main Page
- Information on the Institution
- General Description of the Institution
- Address of İzmir University of Economics
- Academic Authorities
- Academic Calendar
- Recognition of Prior Learning
- Recognition of Study Abroad
- List of Programs Offered
- General Admission Requirements
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- ECTS Credit Allocation
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- Mathematics
- Qualification Awarded
- Level of Qualification
- Specific Admission Requirements
- Qualification Requirements and Regulations
- Recognition of Prior Learning
- Profile of the Program
- Program Outcomes
- Course & Program Outcomes Matrix
- Occupational Profiles of Graduates
- Access to Further Studies
- Program Structure
- Course Structure Diagram with Credits
- Exam Regulations & Assessment & Grading
- Graduation Requirements
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- Program Director (or Equivalent)
- Evaluation Questionnaires
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COURSE INTRODUCTION AND APPLICATION INFORMATION
dm.ieu.edu.tr
| Course Name | |
| Code | Semester | Theory (hour/week) | Application/Lab (hour/week) | Local Credits | ECTS |
|---|---|---|---|---|---|
| Fall/Spring |
| Prerequisites |
| ||||||||
| Course Language | |||||||||
| Course Type | Elective | ||||||||
| Course Level | - | ||||||||
| Mode of Delivery | - | ||||||||
| Teaching Methods and Techniques of the Course | Problem SolvingQ&A | ||||||||
| Course Coordinator | |||||||||
| Course Lecturer(s) | |||||||||
| Assistant(s) | - | ||||||||
| Course Objectives | |
| Learning Outcomes | The students who succeeded in this course;
|
| Course Description |
|
| Core Courses | |
| Major Area Courses | X | |
| Supportive Courses | ||
| Media and Managment Skills Courses | ||
| Transferable Skill Courses |
WEEKLY SUBJECTS AND RELATED PREPARATION STUDIES
| Week | Subjects | Required Materials |
| 1 | Numerical Integration: Trapezoid Rule, Composite Trapezoid Rule. Simpson’s Rule. | Elementary Numerical Analysis (Third edition) by Kendall Atkinson, Weimin Han, John Wiley and Sons, Inc. |
| 2 | Composite Simpson’s Rule. Gaussian Numerical Integration (Quadrature), Weighted Gaussian Quadrature. | Elementary Numerical Analysis (Third edition) by Kendall Atkinson, Weimin Han, John Wiley and Sons, Inc. |
| 3 | Numerical Differentiation: Finite Difference Formulas | Numerical Analysis by Timothy Sauer, 2006, Pearson –Addison Wesley. |
| 4 | Rounding Error, Extrapolation | Elementary Numerical Analysis (Third edition) by Kendall Atkinson, Weimin Han, John Wiley and Sons, Inc. |
| 5 | Solutions of Systems of Equations by Iterative Methods: Jacobi Method | Numerical Analysis by Timothy Sauer, 2006, Pearson –Addison Wesley. |
| 6 | Gauss-Seidel Method and SOR | Numerical Analysis by Timothy Sauer, 2006, Pearson –Addison Wesley. |
| 7 | Convergence of Iterative Methods, Nonlinear Systems of Equations | Numerical Analysis by Timothy Sauer, 2006, Pearson –Addison Wesley. |
| 8 | Midterm | |
| 9 | Least Squares and the Normal Equations: Inconsistent System of Equations | Numerical Analysis by Timothy Sauer, 2006, Pearson –Addison Wesley. |
| 10 | Fitting models to data, conditioning of least squares | Applied Numerical Analysis Using Matlab (Second Edition) by Laurene V.Fausett, 2008, PearsonPrentice Hall. |
| 11 | Eigenvalue and Singular Values: Power Iteration | Applied Numerical Analysis Using Matlab (Second Edition) by Laurene V.Fausett, 2008, PearsonPrentice Hall. |
| 12 | Convergence of Power Iteration, Inverse Power Iteration, | Applied Numerical Analysis Using Matlab (Second Edition) by Laurene V.Fausett, 2008, PearsonPrentice Hall. |
| 13 | Numerical Solutions of Higher Order Equations, Systems | Applied Numerical Analysis Using Matlab (Second Edition) by Laurene V.Fausett, 2008, PearsonPrentice Hall. |
| 14 | Finite Difference Methods for Linear BVPs | Applied Numerical Analysis Using Matlab (Second Edition) by Laurene V.Fausett, 2008, PearsonPrentice Hall. |
| 15 | Finite Difference Methods for Nonlinear BVPs | Applied Numerical Analysis Using Matlab (Second Edition) by Laurene V.Fausett, 2008, PearsonPrentice Hall. |
| 16 | Review of the semester |
| Course Notes/Textbooks | The extracts above and exercises will be given. |
| Suggested Readings/Materials | http://tandon-books.com/Mathematics/MA4423%20-%20Introductory%20Numerial%20Analysis/(MA4423)%20Sauer%20-%20Numerical%20Analysis%202e.pdf http//ins.sjtu.edu.cn/people/mtang/textbook.pdf |
EVALUATION SYSTEM
| Semester Activities | Number | Weigthing |
| Participation | ||
| Laboratory / Application | ||
| Field Work | ||
| Quizzes / Studio Critiques | ||
| Portfolio | ||
| Homework / Assignments | 2 | 10 |
| Presentation / Jury | ||
| Project | 2 | 20 |
| Seminar / Workshop | ||
| Oral Exam | ||
| Midterm | 1 | 30 |
| Final Exam | 1 | 40 |
| Total |
| Weighting of Semester Activities on the Final Grade | 5 | 60 |
| Weighting of End-of-Semester Activities on the Final Grade | 1 | 40 |
| Total |
ECTS / WORKLOAD TABLE
| Semester Activities | Number | Duration (Hours) | Workload |
|---|---|---|---|
| Course Hours (Including exam week: 16 x total hours) | 16 | 3 | 48 |
| Laboratory / Application Hours (Including exam week: 16 x total hours) | 16 | ||
| Study Hours Out of Class | 16 | 2 | |
| Field Work | |||
| Quizzes / Studio Critiques | |||
| Portfolio | |||
| Homework / Assignments | 2 | 5 | |
| Presentation / Jury | |||
| Project | 2 | 20 | |
| Seminar / Workshop | |||
| Oral Exam | |||
| Midterms | 1 | 20 | |
| Final Exams | 1 | 30 | |
| Total | 180 |
COURSE LEARNING OUTCOMES AND PROGRAM QUALIFICATIONS RELATIONSHIP
| # | Program Competencies/Outcomes | * Contribution Level | ||||
1 | 2 | 3 | 4 | 5 | ||
| 1 | To have a grasp of basic mathematics, applied mathematics and theories and applications of statistics. | X | ||||
| 2 | To be able to use theoretical and applied knowledge acquired in the advanced fields of mathematics and statistics, | X | ||||
| 3 | To be able to define and analyze problems and to find solutions based on scientific methods, | X | ||||
| 4 | To be able to apply mathematics and statistics in real life with interdisciplinary approach and to discover their potentials, | X | ||||
| 5 | To be able to acquire necessary information and to make modeling in any field that mathematics is used and to improve herself/himself, | X | ||||
| 6 | To be able to criticize and renew her/his own models and solutions, | X | ||||
| 7 | To be able to tell theoretical and technical information easily to both experts in detail and nonexperts in basic and comprehensible way, | X | ||||
| 8 | To be able to use international resources in English and in a second foreign language from the European Language Portfolio (at the level of B1) effectively and to keep knowledge up-to-date, to communicate comfortably with colleagues from Turkey and other countries, to follow periodic literature, | X | ||||
| 9 | To be familiar with computer programs used in the fields of mathematics and statistics and to be able to use at least one of them effectively at the European Computer Driving Licence Advanced Level, | X | ||||
| 10 | To be able to behave in accordance with social, scientific and ethical values in each step of the projects involved and to be able to introduce and apply projects in terms of civic engagement, | X | ||||
| 11 | To be able to evaluate all processes effectively and to have enough awareness about quality management by being conscious and having intellectual background in the universal sense, | X | ||||
| 12 | By having a way of abstract thinking, to be able to connect concrete events and to transfer solutions, to be able to design experiments, collect data, and analyze results by scientific methods and to interfere, | X | ||||
| 13 | To be able to continue lifelong learning by renewing the knowledge, the abilities and the compentencies which have been developed during the program, and being conscious about lifelong learning, | X | ||||
| 14 | To be able to adapt and transfer the knowledge gained in the areas of mathematics and statistics to the level of secondary school, | X | ||||
| 15 | To be able to conduct a research either as an individual or as a team member, and to be effective in each related step of the project, to take role in the decision process, to plan and manage the project by using time effectively. | X | ||||
*1 Lowest, 2 Low, 3 Average, 4 High, 5 Highest