GAZI UNIVERSITY INFORMATION PACKAGE - 2019 ACADEMIC YEAR

COURSE DESCRIPTION
QUANTITATIVE ANALYSIS/MAT-202
Course Title: QUANTITATIVE ANALYSIS
Credits 3 ECTS 5
Course Semester 4 Type of The Course Compulsory
COURSE INFORMATION
 -- (CATALOG CONTENT)
 -- (TEXTBOOK)
 -- (SUPPLEMENTARY TEXTBOOK)
 -- (PREREQUISITES AND CO-REQUISITES)
 -- LANGUAGE OF INSTRUCTION
  Turkish
 -- COURSE OBJECTIVES
 -- COURSE LEARNING OUTCOMES
Recognize, classify and formulize numerical methods
Interpret the results of the numerical techniques that they use
Decide which algorithm to use when encountered with a numerical problem
Know the advantages and disadvantages of the numerical algorithm they use, and have a realistic estimation of how the algorithm will operate
 -- MODE OF DELIVERY
  The mode of delivery of this course is Face to face
 --WEEKLY SCHEDULE
1. Week   Systems of numbers and errors
2. Week   Computer representations of numbers, integers and floating-point numbers (IEEE notations) Errors due to these impressions.
3. Week   Numerical solution methods of nonlinear equations, Bisection Method
4. Week  Regula Falsi Method, Newton Raphson Method
5. Week  Fixed Point Iteration, Secant Method
6. Week  Solution of Linear Equations Systems, Cramer Rule, Gauss Elimination Method
7. Week   Jacobi Iteration, Gauss-Seidel Method
8. Week   Jacobi Iteration, Gauss-Seidel Method, Midterm Exam
9. Week  Lagrange Interpolation Newton Interpolation
10. Week  Curve Fitting, Least Squares Method Numerical differentiation methods Richardson Extrapolation
11. Week  Numerical integral methods, The Trapezoidal Methods, Romberg Method
12. Week  Simpson and Gauss Formulas
13. Week  Initial Value Problems, Euler Methods, Runge-Kutta Methods
14. Week  Initial Value Problems, Euler Methods, Runge-Kutta Methods, Final Exam
15. Week  
16. Week  
 -- TEACHING and LEARNING METHODS
 -- ASSESSMENT CRITERIA
 
Quantity
Total Weighting (%)
 Midterm Exams
1
60
 Assignment
0
0
 Application
0
0
 Projects
0
0
 Practice
0
0
 Quiz
0
0
 Percent of In-term Studies  
60
 Percentage of Final Exam to Total Score  
40
 -- WORKLOAD
 Activity  Total Number of Weeks  Duration (weekly hour)  Total Period Work Load
 Weekly Theoretical Course Hours
14
3
42
 Weekly Tutorial Hours
14
0
0
 Reading Tasks
14
2
28
 Searching in Internet and Library
7
2
14
 Material Design and Implementation
14
0
0
 Report Preparing
14
0
0
 Preparing a Presentation
14
0
0
 Presentation
14
1
14
 Midterm Exam and Preperation for Midterm Exam
2
5
10
 Final Exam and Preperation for Final Exam
1
10
10
 Other (should be emphasized)
0
 TOTAL WORKLOAD: 
118
 TOTAL WORKLOAD / 25: 
4.72
 Course Credit (ECTS): 
5
 -- COURSE'S CONTRIBUTION TO PROGRAM
NO
 PROGRAM LEARNING OUTCOMES
1
2
3
4
5
1Professional and ethical responsibility gains knowledge.X
2Ability to design experiments, conduct experiments, analyze and interpret the results of experiments.X
3The project-based work culture adopts workplace practices, awareness of employees health, environment and work safety; To train graduates with an awareness of the legal consequences of their engineering practices.X
4Ability to select and use the techniques and modern tools necessary for engineering applications and computer software, information and communication technologies.X
5To be aware of the problems of the age and awareness of entrepreneurship and innovation.X
6Knowledge of the necessity of using information resources and lifelong learning, including developments in science and technology.X
7The breadth of education required to understand the effects of engineering solutions on universal and social dimensions.X
8Ability to communicate effectively with oral and written and technical drawings in Turkish and English.X
9Professional and ethical responsibility.X
10Defining and formulating engineering problems, and selecting and applying appropriate analytical methods and modeling techniques for this purpose.X
11Ability to work in their own discipline and in multi-disciplinary teams.X
12The ability to design a system, part, or process that meets the desired requirements by considering realistic constraints and conditions.X
13Ability to design experiments, conduct experiments, analyze and interpret the results of experiments.X
14Knowledge of mathematics, science and own branches and having sufficient knowledge in engineering subjects and knowledge of application skills.X
 -- NAME OF LECTURER(S)
   (Assoc.Prof.Ülkü DİNLEMEZ KANTAR , Assoc.Prof.Mediha ÖRKCÜ)
 -- WEB SITE(S) OF LECTURER(S)
   (https://websitem.gazi.edu.tr/site/ulku , https://websitem.gazi.edu.tr/site/medihaakcay)
 -- EMAIL(S) OF LECTURER(S)
   (ulku@gazi.edu.tr , medihaakcay@gazi.edu.tr)