1	Course Title:	PYTHON APPLICATIONS
2	Course Code:	EKO3310
3	Type of Course:	Optional
4	Level of Course:	First Cycle
5	Year of Study:	3
6	Semester:	6
7	ECTS Credits Allocated:	5
8	Theoretical (hour/week):	3
9	Practice (hour/week) :	0
10	Laboratory (hour/week) :	0
11	Prerequisites:	None
12	Recommended optional programme components:	None
13	Language:	Turkish
14	Mode of Delivery:	Face to face
15	Course Coordinator:	Dr. ESMA BİRİŞÇİ
16	Course Lecturers:	Dr. Öğr. Üyesi Esma Birişçi
17	Contactinformation of the Course Coordinator:	esmabirisci@uludag.edu.tr Telefon:0224 2941016 Bursa Uludağ Üniversitesi İİBF A blok
18	Website:
19	Objective of the Course:	Python is a versatile programming language suitable for projects ranging from small scripts to large systems. To create the general solution program of the different algorithms covered in the lessons by applying the basic programming algorithms through the Python programming language. Afterward, it is aimed to visualize the obtained results
20	Contribution of the Course to Professional Development	It develops students' ability to use small code structures in a real-life system. So they can write code that actually works and produces the desired functional results.

21

Learning Outcomes:

1 Gain a basic understanding of programming in Python by creating various scripts and applications for systems development.; 2 Understands how basic programming algorithms can be applied to real-life problems.; 3 It can easily reach the solution of the problem by using different Python libraries.; 4 Analyzes, designs and codes with the Python programming language with an object-oriented perspective.; 5 Develop a whole application using object-oriented programming concepts.; 6 Can develop machine learning and artificial intelligence applications.; 7 Visualizes the program with its Graphical User interface.;

22	Course Content:

Week	Theoretical	Practical
1	Module, Libraries and key features
2	Inputs and Outputs in Python
3	Error Handling
4	Data Visualization
5	Desktop GUI
6	Optimization
7	Graph algorithms
8	Nonlinear systems
9	Prediction with scikit-learn
10	Object-oriented programming I
11	Object-oriented programming II
12	Machine Learning and Artificial Intelligence
13	Component analysis (PCA) from scratch with Numpy
14	Real world applications with Python

23	Textbooks, References and/or Other Materials:	1. Introduction to Computation and Programming Using Python with Application to Understanding Data, John V. Guttag, The MIT Press (2016) 2. Richard L. Halterman 2016. Fundamentals of Python Programming. Southern Adventist University, USA. 2. https://pythont-textbok.readthedocs.io/en/1.0/Object_Oriented
24	Assesment

TERM LEARNING ACTIVITIES	NUMBER	PERCENT
Midterm Exam	1	20
Quiz	0	0
Homeworks, Performances	1	20
Final Exam	1	60
Total	3	100
Contribution of Term (Year) Learning Activities to Success Grade		40
Contribution of Final Exam to Success Grade		60
Total		100
Measurement and Evaluation Techniques Used in the Course	Written and practice questions.
Information	Assignments given during the semester affect the final score.

25

ECTS / WORK LOAD TABLE

Activites	NUMBER	TIME [Hour]	Total WorkLoad [Hour]
Theoretical	14	3	42
Practicals/Labs	0	0	0
Self Study and Preparation	0	0	0
Homeworks, Performances	1	10	10
Projects	0	0	0
Field Studies	0	0	0
Midtermexams	1	50	50
Others	0	0	0
Final Exams	1	50	50
Total WorkLoad			152
Total workload/ 30 hr			5,07
ECTS Credit of the Course			5

26	CONTRIBUTION OF LEARNING OUTCOMES TO PROGRAMME QUALIFICATIONS
PQ1 PQ2 PQ3 PQ4 PQ5 PQ6 PQ7 PQ8 PQ9 PQ10 PQ11 PQ12 LO1 4 3 3 4 4 4 5 4 4 5 5 4 LO2 4 3 3 4 4 4 4 4 4 5 5 5 LO3 4 3 3 3 4 4 5 5 4 4 5 4 LO4 5 5 4 4 3 4 4 5 4 4 5 5 LO5 5 4 4 4 5 5 4 4 4 3 5 4 LO6 4 3 4 5 4 4 2 4 4 3 3 3 LO7 3 4 4 5 5 3 4 3 5 5 3 2

LO: Learning Objectives

PQ: Program Qualifications

Contribution Level:

1 Very Low

2 Low

3 Medium

4 High

5 Very High