Python(VM 513)
| Course Code | Course Name | Semester | Theory | Practice | Lab | Credit | ECTS |
|---|---|---|---|---|---|---|---|
| VM 513 | Python | 1 | 4 | 0 | 0 | 3 | 8 |
| Prerequisites | |
| Admission Requirements |
| Language of Instruction | English |
| Course Type | Compulsory |
| Course Level | Masters Degree |
| Course Instructor(s) | Ayberk ZEYTİN azeytin@gsu.edu.tr (Email) |
| Assistant | |
| Objective | This course aims to introduce students with limited or no programming experience to programming tools and methods using the Python programming language and to teach the basic syntactic and semantic structure of the language. Additionally, it aims to teach the analysis and design of algorithms and to introduce data processing and visualization packages that are accepted in the literature. |
| Content | Python data types Syntactic and semantic structure of the Python programming language Data and code flow management Code analysis and design. Object-oriented programming. Data processing and visualization packages |
| Course Learning Outcomes | A student who successfully graduates from this course will be proficient in the Python programming language. They will be able to read code in this programming language and interpret its flow, operation, and purpose. They will be able to write Python code that calculates the solution to a given problem or output, or generates a generalized form of the output. They will be familiar with basic data processing and visualization techniques. |
| Teaching and Learning Methods |
The course will consist of the following: Example code: The instructor will provide an example code for students to learn from. Related output: The instructor will provide related output for students to compare to their own code. Analysis of the workflow: The instructor will lead students in an analysis of the workflow of the example code. Individual programming: Students will be assigned individual programming exercises to practice what they have learned. |
| References |
Python - How to Program - Deitel Algorithms, R. Sedgewick and K. Wayne Data Structures and Algorithms Using Python - Rance D. Necaise |
Theory Topics
| Week | Weekly Contents |
|---|---|
| 1 | Python data types I : integer, float, complex numbers, strings |
| 2 | Python data types II : tuple, list, set, dictionary |
| 3 | Basic programming I : code block, code flow, conditional statements |
| 4 | Basic programming II : loops, intertwined loops |
| 5 | Functions an recursion |
| 6 | Writing and using Python modules |
| 7 | Object oriented programming I : theoretical foundations and examples |
| 8 | Object oriented programming II : classes, inheritance and hierarchy |
| 9 | Object oriented programming III : designing user interfaces |
| 10 | Data manipulation and visualization with Python I : pandas, numpy ve matplotlib |
| 11 | Data manipulation and visualization with Python II : pandas, numpy ve matplotlib |
Practice Topics
| Week | Weekly Contents |
|---|---|
| 1 | |
| 2 | |
| 3 | |
| 4 | |
| 5 | |
| 6 | |
| 7 | |
| 8 | |
| 9 | |
| 10 | |
| 11 |
Contribution to Overall Grade
| Number | Contribution | |
|---|---|---|
| Contribution of in-term studies to overall grade | 3 | 60 |
| Contribution of final exam to overall grade | 1 | 40 |
| Toplam | 4 | 100 |
In-Term Studies
| Number | Contribution | |
|---|---|---|
| Assignments | 3 | 20 |
| Presentation | 0 | 0 |
| Midterm Examinations (including preparation) | 0 | 0 |
| Project | 0 | 0 |
| Laboratory | 0 | 0 |
| Other Applications | 0 | 0 |
| Quiz | 0 | 0 |
| Term Paper/ Project | 0 | 0 |
| Portfolio Study | 0 | 0 |
| Reports | 0 | 0 |
| Learning Diary | 0 | 0 |
| Thesis/ Project | 1 | 40 |
| Seminar | 0 | 0 |
| Other | 0 | 0 |
| Toplam | 4 | 60 |
| No | Program Learning Outcomes | Contribution | ||||
|---|---|---|---|---|---|---|
| 1 | 2 | 3 | 4 | 5 | ||
| Activities | Number | Period | Total Workload |
|---|---|---|---|
| Class Hours | 11 | 4 | 44 |
| Working Hours out of Class | 11 | 8 | 88 |
| Presentation | 1 | 20 | 20 |
| Quiz | 4 | 6 | 24 |
| Term Paper/ Project | 1 | 30 | 30 |
| Total Workload | 206 | ||
| Total Workload / 25 | 8.24 | ||
| Credits ECTS | 8 | ||