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Course Description Course Name : Numerical Analysis Course Code : İSB301 Course Type : Optional Level of Course : First Cycle Year of Study : 3 Course Semester : Fall (16 Weeks) ECTS : 6 Name of Lecturer(s) : Prof.Dr. SADULLAH SAKALLIOĞLU Learning Outcomes of the Course : Make solutions to systems of linear equations Apply Gauss Elimination and Gauss-Jordan Methods for Solving Linear Equations Appy Gauss-Siedel Methods to solve linear equations Find Root of a function Practical knowledge of polynomial interpolation, theoretical knowledge of associated approximation properties Use to approximate definite integrals Examination of the errors in the calculations Use least squares method Mode of Delivery : Face-to-Face Prerequisites and Co-Prerequisites : None Recommended Optional Programme Components : None Aim(s) of Course : To analyze and apply well-know numerical techniques to solve problems. Course Contents : Numerical solution of linear and nonlinear systems of equations; Newton’s method, the secant method, Bairstow’s method, Gauss elemination, Gauss – Jordan elemination, inverse and determinant of any square matrix, Gaus - Siedel iteration, least squares, power iteration. Interpolation and numerical integration methods. Language of Instruction : Turkish Work Place : Faculty of Arts and Sciences Annex Classrooms   Course Outline /Schedule (Weekly) Planned Learning Activities Week Subject Student's Preliminary Work Learning Activities and Teaching Methods 1 Meaning of numerical analysis, number systems, and general information about the error, Source reading Lecture, discussion and problem-solving 2 Methods of Solving Nonlinear Equations; bisection and Newton methods, Source reading Lecture, discussion and problem-solving 3 Methods of Solving Nonlinear Equations, Regula-Falsi and Bairstow methods, Source reading Lecture, discussion and problem-solving 4 Systems of linear equations, matrix inverse and determinant Source reading Lecture, discussion and problem-solving 5 Solving Linear Equations; Gauss Elimination and Gauss-Jordan Methods Source reading Lecture, discussion and problem-solving 6 Gauss Elimination and Gauss-Jordan Methods for finding inverse of the matrix and determinant. Source reading Lecture, discussion and problem-solving 7 Gauss-Seidel Method of Solving Linear Equations Source reading Lecture, discussion and problem-solving 8 Mid-term exam Rewview the topics discussed in the lecture notes and sources Written exam 9 Interpolation, linear interpolation, Lagrange Interpolation Source reading Lecture, discussion and problem-solving 10 Central difference interpolation Source reading Lecture, discussion and problem-solving 11 Forward difference interpolation Source reading Lecture, discussion and problem-solving 12 Backward difference interpolation Source reading Lecture, discussion and problem-solving 13 Numerical integration methods Source reading Lecture, discussion and problem-solving 14 Numerical integration methods. Source reading Lecture, discussion and problem-solving 15 Curve fitting, method of least squares Source reading Lecture, discussion and problem-solving 16/17 Mid-term exam Rewview the topics discussed in the lecture notes and sources Written exam   Required Course Resources Resource Type Resource Name Recommended Course Material(s)  Behiç Çağal (1989), Sayısal Analiz, Seç Yayın Dağıtım, İstanbul.  Lee W. Johnson, R. Dean Riess (1982) Numerical Analysis, Addison-Wesley Publishing Company. Required Course Material(s)  Richard L. Burden, J. Douglas Faires (2000). Numerical Analysis, Brooks Cole; 7th edition.  thony Ralston, Philip Rabinowitz (2001). A First Course in Numerical Analysis, Dover Publications; 2nd Rev edition.   Assessment Methods and Assessment Criteria Semester/Year Assessments Number Contribution Percentage     Mid-term Exams (Written, Oral, etc.) 1 100     Homeworks/Projects/Others 10 0 Total 100 Rate of Semester/Year Assessments to Success 40   Final Assessments 100 Rate of Final Assessments to Success 60 Total 100   Contribution of the Course to Key Learning Outcomes # Key Learning Outcome Contribution* 1 Utilize computer systems and softwares 0 2 Apply the statistical analyze methods 0 3 Make statistical inference(estimation, hypothesis tests etc.) 0 4 Generate solutions for the problems in other disciplines by using statistical techniques 3 5 Discover the visual, database and web programming techniques and posses the ability of writing programme 0 6 Construct a model and analyze it by using statistical packages 0 7 Distinguish the difference between the statistical methods 0 8 Be aware of the interaction between the disciplines related to statistics 3 9 Make oral and visual presentation for the results of statistical methods 0 10 Have capability on effective and productive work in a group and individually 2 11 Develop scientific and ethical values in the fields of statistics-and scientific data collection 4 12 Explain the essence fundamentals and concepts in the field of Probability, Statistics and Mathematics 0 13 Emphasize the importance of Statistics in life 0 14 Define basic principles and concepts in the field of Law and Economics 0 15 Produce numeric and statistical solutions in order to overcome the problems 5 16 Construct the model, solve and interpret the results by using mathematical and statistical tehniques for the problems that include random events 4 17 Use proper methods and techniques to gather and/or to arrange the data 0 18 Professional development in accordance with their interests and abilities, as well as the scientific, cultural, artistic and social fields, constantly improve themselves by identifying training needs 2 * Contribution levels are between 0 (not) and 5 (maximum).   Student Workload - ECTS Works Number Time (Hour) Total Workload (Hour) Course Related Works     Class Time (Exam weeks are excluded) 14 3 42     Out of Class Study (Preliminary Work, Practice) 14 3 42 Assesment Related Works     Homeworks, Projects, Others 10 3 30     Mid-term Exams (Written, Oral, etc.) 1 12 12     Final Exam 1 20 20 Total Workload: 146 Total Workload / 25 (h): 5.84 ECTS Credit: 6