• Basic information
• Learning outcomes
• Contents
• Assessment
• Workload

Learning outcomes

Upon successful completion of this course, students will be able to:

1. Process biological data files in Python.

   Contribution to Program Outcomes

   1. Define and manipulate basic and advanced concepts in the field of Bioinformatics and Systems Biology
   2. Process and analyze genome-scale biological data using statistical methods and data mining methods.
   3. Acquire scientific knowledge and work independently

   Method of assessment

   1. Written exam
   2. Homework assignment
2. Use Python for the basic statistical and optimization-based analyses of biological data

   Contribution to Program Outcomes

   1. Define and manipulate basic and advanced concepts in the field of Bioinformatics and Systems Biology
   2. Process and analyze genome-scale biological data using statistical methods and data mining methods.
   3. Acquire scientific knowledge and work independently
   4. Grasp the importance of bioinformatics and systems biology based viewpoint in the analysis and interpretation of working principles of the cell.

   Method of assessment

   1. Written exam
   2. Homework assignment
3. Integrate different softwares by using programming interfaces

   Contribution to Program Outcomes

   1. Process and analyze genome-scale biological data using statistical methods and data mining methods.
   2. Find out new methods to improve his/her knowledge.

   Method of assessment

   1. Written exam
   2. Homework assignment

Contents
Week 1:	Introduction to computer programming and Python
Week 2:	String and List variable types HW1
Week 3:	If - elif - else structure
Week 4:	While and For loops HW2
Week 5:	Indexing techniques in Python HW3 Midterm exam
Week 6:	Integrating softwares with interfaces: libSBML/Python API, COPASI/Python API Midterm I
Week 7:	Dictionary, tuple and set variable types
Week 8:	Fundamentals of Object Oriented Programming HW4
Week 9:	Writing and using functions
Week 10:	Writing and using modules HW5
Week 11:	Data input and output using text files, pandas package Midterm Exam
Week 12:	Plotting charts with MatPlotLib package HW6
Week 13:	Numpy package and array data type
Week 14:	SciPy Package HW7
Week 15*:	-
Week 16*:	Final Exam
Textbooks and materials:	1. Ders notları ve Jupyter defterleri 2. Amos D., Bader D., Jablonski J. ve Heisler F., "Python Basics: A Practical Introduction to Python 3", Real Python Yayınevi, 2021.
Recommended readings:	https://pythonforbiologists.com/
	* Between 15th and 16th weeks is there a free week for students to prepare for final exam.

Assessment
Method of assessment	Week number	Weight (%)
Mid-terms:	6,11	40
Other in-term studies:		0
Project:		0
Homework:	2,4,7,9,11	30
Quiz:		0
Final exam:	16	30
	Total weight:	(%)

Workload
Activity	Duration (Hours per week)	Total number of weeks	Total hours in term
Courses (Face-to-face teaching):	3	14
Own studies outside class:	4	14
Practice, Recitation:	0	0
Homework:	8	6
Term project:	0	0
Term project presentation:	0	0
Quiz:	0	0
Own study for mid-term exam:	20	2
Mid-term:	2	2
Personal studies for final exam:	0	0
Final exam:	2	1
		Total workload:
		Total ECTS credits:	*
	* ECTS credit is calculated by dividing total workload by 25. (1 ECTS = 25 work hours)