Course Information

Course Title	Code	Semester	L+U Hour	Credits	ECTS
Scientific Computing Techniques	805101725231		3 + 0	3.0	8.0

Prerequisites

None

Language of Instruction	Turkish
Course Level	Graduate Degree
Course Type	Elective
Mode of delivery
Course Coordinator
Instructors	Nuri ÖZALP
Assistants
Goals	To acquaint students of science and engineering with the potentialities of modern computer for solving the numerical problems arising in their professions, Understanding mathematical theory of algorithms, detecting and contolling errors in scientific computing
Course Content	Number representation and programming techniques, loss of significance. Locating roots of equations, bisection method, Newton method, secant method. Interpolation and numerical differentiation, polynomial interpolation and errors, estimating derivatives, Richardson extrapolation. Numerical integration, trapezoid rule, Romberg algorithm, Simpson and Gauss quadrature formulas.
Learning Outcomes	1) Understanding programming techniques 2) Computing Taylor series, making error analysis 3) Solution algorithms of nonlinear equations and selecting the better method 4) Solution algorithms of the systems and selecting the better method 5) Computing numerical derivative and extrapolation 6) Computing numerical integral, choosing the solution method 7) Making algorithms and programming

Week	Topics	Teaching and Learning Methods and Techniques	Study Materials
1. Week	Preliminaries	Lecture Brainstorming Project Based Learning	Homework
2. Week	.Taylor Series	Lecture Brainstorming Project Based Learning	Homework
3. Week	Difference Equations	Lecture Project Based Learning	Homework
4. Week	Computer Arithmetic	Lecture Brainstorming Project Based Learning	Homework
5. Week	.Nonlinear equations	Lecture Brainstorming Project Based Learning	Homework
6. Week	.Newton Method	Lecture Brainstorming Project Based Learning	Homework
7. Week	Fixed point	Lecture Brainstorming Project Based Learning	Homework
8. Week	Polynomial interpolation	Lecture Brainstorming Project Based Learning	Homework
9. Week	Divided difference	Lecture Brainstorming Project Based Learning	Homework
10. Week	Hermite interpolation	Lecture Brainstorming Project Based Learning	Homework
11. Week	Numerical Derivation and Integral	Lecture Brainstorming Project Based Learning	Homework
12. Week	Ekstrapolation	Lecture Brainstorming Project Based Learning	Homework
13. Week	Gauss Quadratures	Lecture Brainstorming Project Based Learning	Homework
14. Week	Romberg Algorithm	Lecture Brainstorming Project Based Learning	Homework

Recommended Sources

Cheney,W.,-Kincaid,D., Numerical Analysis Mathematics of Scientific Computing,AMS,2009.

Fogiel, M., (Director), Numerical Analysis Problem Solver,REA, 1983.

Mathews. J.H., Numerical Methods for Mathematics, Science and Engineering, 2nd Ed, 1992.

Yakowitz,S., An Introduction to Numerical Computations, Macmillan, 1989.

*DK = Course's Contrubution.

	0	1	2	3	4	5
Level of contribution	None	Very Low	Low	Fair	High	Very High

Event	Quantity	Duration (Hour)	Total Workload (Hour)
Course Duration (Total weeks*Hours per week)	14	3	42
Work Hour outside Classroom (Preparation, strengthening)	14	4	56
Homework	2	5	10
Midterm Exam	1	10	10
Time to prepare for Midterm Exam	1	20	20
Time to prepare for Final Exam	1	20	20
	1	2	2
	1	2	2
	7	10	70
Total Workload
Total Workload / 30 (s)
ECTS Credit of the Course			30