Advance Python Programming

Course Code:

COMP 303

Course Period:

Autumn

Course Type:

Core

P:

3

Lab:

0

Credits:

3

ECTS:

5

Course Language:

İngilizce

Course Objectives:

Objective of this course to introduce some of the advance topics where programming language is commonly used in real life applications. At the end of this course, you will learn to code python topics such as socket-programming, database operations (SQL and NOSQL), web programming, applications of data-mining technics and some methods of machine learning (at some points deep learning).

Course Content:

Overview of scripting languages. Study of Python language in depth. Discussion of supported libraries. Applications to system administration, networking, databas applcaitons, socket-programming, advance concept in python.

Course Methodology:

Lecture, 2: Question-Answer, 3: Discussion, 4: Simulation, 5: Case Study

Course Evaluation Methods:

A: Testing, B: Presentation, C: Homework, D: Project, E: Laboratory

Vertical Tabs

Course Learning Outcomes

Learning Outcomes	Program Learning Outcomes	Teaching Methods	Assessment Methods
Write, debug, and run a program given a problem description.	2,4,6	1,5	C,E
Install and use extra software libraries as needed by the task.	2,4,6	1,2	C, E
Perform system administration tasks with scripts.	2,4,6	1,2,5	A,E
Learning basic ML libraries	2,4,6	1,2,3	A, C
Do research about scripting languages and assess their relative merits.	2,4,6	1,3	B, D
Complete a programming project.	2,4,6	4, 5	B,D

Course Flow

COURSE CONTENT
Week	Topics	Study Materials
1	Introduction to Course + Data Structures and Algorithms	Lecture Notes
2	Data Encoding and Processing	Lecture Notes
3	Concurrency (Threating in Python)	Lecture Notes
4	Metaprogramming (decorators, class decorators, and metaclasses	Lecture Notes
5	Socket Programming with Python	Lecture Notes
6	. Database Programming with Python	Lecture Notes
7	Web Scraping with Python	Lecture Notes
8	Midterm Examination
9	Scientific and Numeric Applications in Python	Lecture Notes
10	. Data Science Applications in Python Part 1	Lecture Notes
11	. Data Science Applications in Python Part 2	Lecture Notes
12	Introduction to Machine Learning with Python Part 1	Lecture Notes
13	Introduction to Machine Learning with Python Part 2	Lecture Notes
14	Project Presentatons
15	Final

Recommended Sources

RECOMMENDED SOURCES

Textbook

Michał Jaworski and Tarek Ziadé, Expert Python Programming Third Edition, Packt Publishing, 2019.

Luciano Ramalho, Fluent Python: Clear, Concise, and Effective Programming, O'Reilly Media, Inc.,2015.

David Beazley, Brian K. Jones, Python Cookbook, 3rd Edition, O'Reilly Media,2013.

Additional Resources

Online reference material at python.org

Material Sharing

MATERYAL PAYLAŞIMI
Belgeler	Ders Konuları için Yönergeler ve ek örnekler
Ödevler	Ev Ödevleri
Sınavlar	Ara Sınav ve Final Sınavı

Assessment

ASSESSMENT
IN-TERM STUDIES	NUMBER	PERCENTAGE
Homework assignment	10	50
Project presentation	1	30
Midterm Examination	1	20
Total		100
CONTRIBUTION OF FINAL EXAMINATION TO OVERALL GRADE		40
CONTRIBUTION OF IN-TERM STUDIES TO OVERALL GRADE		60
Total		100

ECTS

ECTS ALLOCATED BASED ON STUDENT WORKLOAD BY THE COURSE DESCRIPTION
Activities	Quantity	Duration (Hour)	Total Workload (Hour)
Course Duration (Including the exam week: 16x Total course hours)	15	3	45
Hours for off-the-classroom study (Pre-study, practice)	15	3	45
Homework	10	2	20
Midterm Examination	1	2	2
Preparation of class presentation	1	13	13
			0
Total Work Load			125
Total Work Load / 25 (h)			5,00
ECTS Credit of the Course			5

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