| MATHEMATICS (TURKISH, PHD) | |||||
| PhD | TR-NQF-HE: Level 8 | QF-EHEA: Third Cycle | EQF-LLL: Level 8 | ||
| Course Code | Course Name | Semester | Theoretical | Practical | Credit | ECTS |
| SEN4015 | Advanced Programming with Python | Fall | 3 | 0 | 3 | 6 |
| The course opens with the approval of the Department at the beginning of each semester |
| Language of instruction: | En |
| Type of course: | Departmental Elective |
| Course Level: | |
| Mode of Delivery: | E-Learning |
| Course Coordinator : | Instructor DUYGU ÇAKIR YENİDOĞAN |
| Course Objectives: | The aim of this course is to familiarize the student with the Python programming language and help the student gain impactful scientific computations using different libraries of Python. This course will build on the students' existing programming knowledge, incorporating further object-oriented design principles and techniques for visualization, web, game or application programming. |
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The students who have succeeded in this course; Get familiar with the Python programming language. Gain the ability to implement object oriented programs with Python. Understand the data types and structures in Python. Solve problems with scientific computations. Visualize the computational results for a better understanding. Identify the commonly used operations involving file systems and regular expressions. Articulate the Object-Oriented Programming concepts such as encapsulation, inheritance and polymorphism as used in Python. Work with JSON and XML objects |
| The contents of this course include basic Python programming as well as arrays, plotting, symbolic computation, scientific algorithms, object oriented programming, threading and random variables. The students will be introduced to popular Python packages like NumPy, Matplotlib, SciPy, and others. They will also be able to parse JSON and XML objects and convert Python to the others. |
| Week | Subject | Related Preparation | |
| 1) | Orientation -Course Schedule -Expectations | ||
| 2) | Introduction to Python - What is Python - Brief history and versions - Documentation - Setting up the environment | -Set up your environment | |
| 3) | Getting Familiar with Python - Data types - Conditionals | ||
| 4) | Getting Familiar with Python - Loops - Functions | ||
| 5) | Getting Familiar with Python - Exception handling - Debugging | ||
| 6) | Getting Familiar with Python - Built-in functions and modules | ||
| 7) | Getting Familiar with Python - List, tuples, dictionaries - File operations | ||
| 8) | Midterm | ||
| 9) | Getting Familiar with Python - Regular Expressions - Pattern and Match Objects - Regex flags | ||
| 10) | Getting Familiar with Python - Object Oriented Programming - Method overloading - Static & In-Class methods - Accessing attributes | ||
| 11) | Advanced Classes - Documenting the class - Encapsulation - Abstract classes - Class decorators | ||
| 12) | Functional Programming - Lambda function - Passing functions as parameters - Map, Reduce, Filter - Generators - Coroutines | ||
| 13) | Multi-threading & Multi-processing - Synchronizing threads - Rlocks & Semaphores - Global interpreter - The multiprocessing module | ||
| 14) | Working with XML & JSON - Parsing XML - Handling unicode - Parsing XML with element tree - the Element and ElemantTree classes - Parsing the JSON object - Converting Python to JSON | ||
| Course Notes: | 1) Kent D. Lee, ""Python Programming Fundamentals"", 2nd edition, Springer 2) Tony Gaddis, ""Starting out With Python"", 4th edition, Pearson" |
| References: | 1) Jake VanderPlas, “Python Data Science Handbook: Essential Tools for Working with Data”, 1st Edition, O'Reilly 2) Aurelien Geron, Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems”, 1st Edition,O'Reilly 3) Wesley J Chun, “Core Python Applications Programming”, 3rd Edition, Pearson 4) Miguel Grinberg, “Flask Web Development: Developing Web Applications with Python”, 2nd Edition, O'Reilly" |
| Semester Requirements | Number of Activities | Level of Contribution |
| Attendance | 10 | % 0 |
| Laboratory | % 0 | |
| Application | % 0 | |
| Field Work | % 0 | |
| Special Course Internship (Work Placement) | % 0 | |
| Quizzes | 5 | % 30 |
| Homework Assignments | % 0 | |
| Presentation | % 0 | |
| Project | % 0 | |
| Seminar | % 0 | |
| Midterms | 1 | % 30 |
| Preliminary Jury | % 0 | |
| Final | 1 | % 40 |
| Paper Submission | % 0 | |
| Jury | % 0 | |
| Bütünleme | % 0 | |
| Total | % 100 | |
| PERCENTAGE OF SEMESTER WORK | % 60 | |
| PERCENTAGE OF FINAL WORK | % 40 | |
| Total | % 100 | |
| No Effect | 1 Lowest | 2 Low | 3 Average | 4 High | 5 Highest |
| Program Outcomes | Level of Contribution |