Nevşehir Hacı Bektaş Veli University Course Catalogue
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| Year/Semester of Study | 1 / Spring 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 operations using the Python programming language and Python libraries. | |||||
| Learning Outcomes | PO | MME | |
| The students who succeeded in this course: | |||
| LO-1 | Comprehends scientific computation methods with Python libraries. |
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 | Examines mathematical methods for data analysis and comprehends data visualization 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 to SciPy library, rank, determinant and norm calculations with SciPy, eigenvalues and eigenvector calculations with SciPy, solutions of systems of linear equations with SciPy (Gaussian elimination, Gauss-Jordan methods), solutions of systems of linear equations with SciPy (LU decomposition, Gauss-Seidel methods), Calculation of inverses of regular matrices with SciPy, Calculation of inverses of singular matrices with SciPy, Introduction to SymPy library, symbolic mathematical operations with SymPy (Derivative, Limit, Integral), Series expansions with SymPy, Introduction to Pandas library, Big data analysis with Pandas, Pandas advanced data analysis applications, data visualization with Matplotlib library. | ||
| Weekly Course Content | ||
| Week | Subject | Learning Activities and Teaching Methods |
| 1 | Introduction to SciPy library | Lecturing, Problem Solving |
| 2 | Rank,determinant and norm calculations with SciPy | Lecturing, Problem Solving |
| 3 | Eigenvalues and eigenvector calculations with SciPy | Lecturing, Problem Solving |
| 4 | Solutions of systems of linear equations with SciPy (Gaussian elimination,Gauss-Jordan methods) | Lecturing, Problem Solving |
| 5 | Solutions of systems of linear equations with SciPy (LU decomposition,Gauss-Seidel methods) | Lecturing, Problem Solving |
| 6 | Computation of inverses of regular matrices with SciPy | Lecturing, Problem Solving |
| 7 | Computation of inverses of singular matrices with SciPy | Lecturing, Problem Solving |
| 8 | mid-term exam | |
| 9 | Introduction to SymPy library | Lecturing, Problem Solving |
| 10 | Symbolic mathematical operations with SymPy (Derivative,Limit,Integral) | Lecturing, Problem Solving |
| 11 | Series expansions with SymPy | Lecturing, Problem Solving |
| 12 | Introduction to Pandas library | Lecturing, Problem Solving |
| 13 | Big data analysis with Pandas | Lecturing, Problem Solving |
| 14 | Pandas advanced data analysis applications | Lecturing, Problem Solving |
| 15 | Data visualization with Matplotlib library | 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 | ||