MATH 2006 - Numerical Analysis for Engineers

Basic Information

Course Code:

MATH 2006

Course Name:

Numerical Analysis for Engineers

Language of Instruction:

English

Course Type:

Class

Course Level:

Bachelor

ECTS:

5.00

Instructor:

Dr. Öğr. Üyesi SÜLEYMAN CENGİZCİ

Course Objectives

To introduce fundamental numerical methods used in solving engineering problems, explain their theoretical foundations, and develop an understanding of their applications in engineering practice.

Course Content

Numerical methods for finding roots of equations, solution techniques for linear and nonlinear systems of equations, interpolation and curve fitting, numerical differentiation and integration, numerical solution of ordinary differential equations, and an introduction to numerical optimization methods.

Course Books / Materials / Recommended Resources

1) Cengizci, S., Scientific Computing Lecture Notes, Antalya Bilim University, 2026.2) Burden, R. L., & Faires, J. D., Numerical Analysis, 9th Edition, Brooks/Cole, Cengage Learning, 2011.3) Chapra, S. C., Applied Numerical Methods with MATLAB for Engineers and Scientists, 4th Edition, McGraw-Hill Education, 2018. (Recommended)4) Chapra, S. C., & Canale, R. P., Numerical Methods for Engineers, 8th Edition, McGraw-Hill Education, 2021. (Recommended)5) Mathews, J. H., & Fink, K. D., Numerical Methods Using MATLAB, 4th Edition, Prentice Hall, 2004. (Recommended)

Learning Outcomes

Code	Description
LO1	Apply fundamental mathematics and engineering science knowledge in the implementation of numerical methods.
LO2	Select and apply appropriate numerical methods for solving engineering problems.
LO3	Use Python to implement numerical methods, analyze computational results, and interpret the outcomes.
LO4	Evaluate existing numerical methods, adapt them to specific engineering problems, and develop new solution approaches when necessary.
LO5	Evaluate the accuracy, convergence, and sources of error associated with numerical methods.
LO6	Compare different numerical methods and justify the selection of the most appropriate method for a given engineering problem.

Assessment

#	Assessment Type	Contribution (%)
1	Midterm Exam	%35
2	Homework	%15
3	Final Exam	%50
TOTAL		%100

PO - LO Matrix

PO \ LO	LO1	LO2	LO3	LO4	LO5	LO6
PO-1
PO-2
PO-3
PO-4
PO-5
PO-6
PO-7
PO-8
PO-9
PO-10
PO-11

1

Low Contribution

2

Medium Contribution

3

High Contribution

Academic Integrity and Artificial Intelligence

Students are expected to adhere to the principles of academic integrity throughout the course. All assignments, reports, projects, and examinations must represent the student's own work, and all sources must be properly acknowledged. Artificial intelligence (AI) tools (e.g., ChatGPT, Copilot, Gemini) may only be used as permitted by the instructor for educational support purposes. AI-generated content must not replace the student's own understanding, analysis, or original work. Any use of AI tools should be appropriately disclosed, and students remain fully responsible for the accuracy, originality, and integrity of all submitted work. Violations of academic integrity will be handled in accordance with the University's academic regulations.

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