Nevşehir Hacı Bektaş Veli University Course Catalogue
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| Year/Semester of Study | 1 / Fall Semester | ||||
| Level of Course | 2nd Cycle Degree Programme | ||||
| Type of Course | Optional | ||||
| Department | MATHEMATICS | ||||
| Pre-requisities and Co-requisites | None | ||||
| Mode of Delivery | Face to Face | ||||
| Teaching Period | 14 Weeks | ||||
| Name of Lecturer | CAHİT KÖME (cahit@nevsehir.edu.tr) | ||||
| Name of Lecturer(s) | |||||
| Language of Instruction | Turkish | ||||
| Work Placement(s) | None | ||||
| Objectives of the Course | |||||
| The aim of this course is to gain the ability to perform advanced numerical and symbolic mathematical computations with the Python programming language. | |||||
| Learning Outcomes | PO | MME | |
| The students who succeeded in this course: | |||
| LO-1 | Examines solutions to numerical and symbolic problems with Python. |
PO-1 Fundamental theorems of about some sub-theories of Analysis, Applied Mathematics, Geometry, and Algebra can apply to new problems. PO-6 Following the developments in science and technology and gain self-renewing ability. PO-13 Ability to use mathematical knowledge in technology. |
Examination |
| LO-2 | Comprehend algorithmic thinking, mathematical programming and scientific computing techniques. |
PO-1 Fundamental theorems of about some sub-theories of Analysis, Applied Mathematics, Geometry, and Algebra can apply to new problems. PO-6 Following the developments in science and technology and gain self-renewing ability. PO-13 Ability to use mathematical knowledge in technology. |
Examination |
| PO: Programme Outcomes MME:Method of measurement & Evaluation |
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| Course Contents | ||
| Introduction of the course, What is Python?, What can be done with Python?, Setup of the open source Python development environment, The concept of algorithm and introduction to algorithm development with Python, Python variable structures and data types, Python arrays and logical structures, Python loops and conditional expressions, Python class and function structures, Introduction to Python math libraries (Numpy, Sympy), Introduction to Numpy data types and array structures, Introduction to Numpy linalg library, Vector and matrix analysis with Numpy, matrix decompositions with Numpy (Cholesky Decomposition), matrix decompositions with Numpy (QR Decomposition ), matrix decompositions with Numpy (Singular Value Decomposition) | ||
| Weekly Course Content | ||
| Week | Subject | Learning Activities and Teaching Methods |
| 1 | Introduction of the course, what is Python? What can be done with Python? | Lecturing, Problem solving |
| 2 | Setup of the open-source Python development environment | Lecturing, Problem solving |
| 3 | The concept of algorithm and introduction to algorithm development with Python | Lecturing, Problem solving |
| 4 | Python variable structures and data types | Lecturing, Problem solving |
| 5 | Python arrays and logical structures | Lecturing, Problem solving |
| 6 | Python loops and conditional expressions | Lecturing, Problem solving |
| 7 | Python class and function structures | Lecturing, Problem solving |
| 8 | mid-term exam | |
| 9 | Introduction to Python math libraries (Numpy, Sympy) | Lecturing, Problem solving |
| 10 | Introduction to Numpy data types and array structures | Lecturing, Problem solving |
| 11 | Introduction to Numpy linalg library | Lecturing, Problem solving |
| 12 | Vector and matrix analysis with Numpy | Lecturing, Problem solving |
| 13 | Matrix decompositions with Numpy (Cholesky Decomposition) | Lecturing, Problem solving |
| 14 | Matrix decompositions with Numpy (QR Decomposition) | Lecturing, Problem solving |
| 15 | Matrix decompositions with Numpy (Singular Value Decomposition) | Lecturing, Problem solving |
| 16 | final exam | |
| Recommend Course Book / Supplementary Book/Reading | ||
| Required Course instruments and materials | ||
| Assessment Methods | |||
| Type of Assessment | Week | Hours | Weight(%) |
| mid-term exam | 8 | 2 | 40 |
| Other assessment methods | |||
| 1.Oral Examination | |||
| 2.Quiz | |||
| 3.Laboratory exam | |||
| 4.Presentation | |||
| 5.Report | |||
| 6.Workshop | |||
| 7.Performance Project | |||
| 8.Term Paper | |||
| 9.Project | |||
| final exam | 16 | 2 | 60 |
| Student Work Load | |||
| Type of Work | Weekly Hours | Number of Weeks | Work Load |
| Weekly Course Hours (Theoretical+Practice) | 3 | 14 | 42 |
| Outside Class | |||
| a) Reading | 4 | 14 | 56 |
| b) Search in internet/Library | 3 | 14 | 42 |
| c) Performance Project | 0 | ||
| d) Prepare a workshop/Presentation/Report | 0 | ||
| e) Term paper/Project | 0 | ||
| Oral Examination | 0 | ||
| Quiz | 0 | ||
| Laboratory exam | 0 | ||
| Own study for mid-term exam | 5 | 4 | 20 |
| mid-term exam | 2 | 1 | 2 |
| Own study for final exam | 4 | 4 | 16 |
| final exam | 2 | 1 | 2 |
| 0 | |||
| 0 | |||
| Total work load; | 180 | ||