COMPUTER ENGINEERING
Bachelor	TR-NQF-HE: Level 6	QF-EHEA: First Cycle	EQF-LLL: Level 6

Course Introduction and Application Information

Course Code	Course Name	Semester	Theoretical	Practical	Credit	ECTS
SEN4015	Advanced Programming with Python	Fall	3	0	3	6

This catalog is for information purposes. Course status is determined by the relevant department at the beginning of semester.

Basic information

Language of instruction:	English
Type of course:	Departmental Elective
Course Level:	Bachelor’s Degree (First Cycle)
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.

Learning Outcomes

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

Course Content

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.

Weekly Detailed Course Contents

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

Sources

Course Notes / Textbooks:

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"

Evaluation System

Semester Requirements	Number of Activities	Level of Contribution
Attendance	10	% 0
Quizzes	5	% 30
Midterms	1	% 30
Final	1	% 40
Total		% 100
PERCENTAGE OF SEMESTER WORK		% 60
PERCENTAGE OF FINAL WORK		% 40
Total		% 100

Contribution of Learning Outcomes to Programme Outcomes

No Effect	1 Lowest	2 Low	3 Average	4 High	5 Highest

	Program Outcomes	Level of Contribution
1)	Adequate knowledge in mathematics, science and computer engineering; the ability to use theoretical and practical knowledge in these areas in complex engineering problems.
2)	Ability to identify, formulate, and solve complex engineering problems; ability to select and apply appropriate analysis and modeling methods for this purpose.
3)	Ability to design a complex system, process, device or product to meet specific requirements under realistic constraints and conditions; ability to apply modern design methods for this purpose.
4)	Ability to develop, select and use modern techniques and tools necessary for the analysis and solution of complex problems encountered in computer engineering applications; ability to use information technologies effectively.
5)	Ability to design, conduct experiments, collect data, analyze and interpret results for the study of complex engineering problems or computer engineering research topics.
6)	Ability to work effectively within and multi-disciplinary teams; individual study skills.
7)	Ability to communicate effectively in verbal and written Turkish; knowledge of at least one foreign language; ability to write active reports and understand written reports, to prepare design and production reports, to make effective presentations, to give and receive clear and understandable instructions.
8)	Awareness of the necessity of lifelong learning; ability to access information, to follow developments in science and technology and to renew continuously.
9)	To act in accordance with ethical principles, professional and ethical responsibility; information on the standards used in engineering applications.
10)	Information on business practices such as project management, risk management and change management; awareness of entrepreneurship and innovation; information about sustainable development.
11)	Knowledge of the effects of engineering practices on health, environment and safety in the universal and social scale and the problems of the era reflected in engineering; awareness of the legal consequences of engineering solutions.