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

Learning outcomes

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

1. Develop Python scripts for simple data analysis and visualization tasks upon completion of the course successfully.

   Contribution to Program Outcomes

   1. Acquire knowledge on biological, chemical, physical and mathematical principles which constitute the basis of bioengineering applications
   2. Apply mathematical analysis and modeling methods for bioengineering design and production processes.
   3. Design processes for the investigation of bioengineering problems, collect data, analyze and interpret the results.
   4. Work effectively in multi-disciplinary research teams
   5. Develop an awareness of continuous learning in relation with modern technology.

   Method of assessment

   1. Written exam
2. Develop algorithms to solve an engineering problem programmatically

   Contribution to Program Outcomes

   1. Acquire knowledge on biological, chemical, physical and mathematical principles which constitute the basis of bioengineering applications
   2. Apply mathematical analysis and modeling methods for bioengineering design and production processes.
   3. Design processes for the investigation of bioengineering problems, collect data, analyze and interpret the results.
   4. Work effectively in multi-disciplinary research teams
   5. Develop an awareness of continuous learning in relation with modern technology.

   Method of assessment

   1. Written exam
3. Capable of improving their programming skill and learn a new programming language by self-study.

   Contribution to Program Outcomes

   1. Acquire knowledge on biological, chemical, physical and mathematical principles which constitute the basis of bioengineering applications
   2. Convert biological, chemical, physical and mathematical principles into novel applications for the benefit of society,
   3. Apply mathematical analysis and modeling methods for bioengineering design and production processes.
   4. Work effectively in multi-disciplinary research teams
   5. Develop an awareness of continuous learning in relation with modern technology.

   Method of assessment

   1. Written exam

Contents
Week 1:	Introduction to Computing and Python
Week 2:	Data types String and List
Week 3:	If - elif - else statement
Week 4:	While and For loops
Week 5:	Use of If-elif-else statements together with While and For loops
Week 6:	Indexing in Python Midterm exam I
Week 7:	Other data types in Python (dictionary, set, tuple)
Week 8:	Fundamentals of Object Oriented Programming
Week 9:	Functions in Python
Week 10:	Modules in Python
Week 11:	Reading and writing data in files
Week 12:	MatPlotLib module and plotting charts Midterm exam II
Week 13:	Numpy module and array data type
Week 14:	Scipy module
Week 15*:	.
Week 16*:	Final exam
Textbooks and materials:	Ders notları, örnek kodlar, Jupyter defterleri.
Recommended readings:	Python for Bioinformatics, Sebastian Bassi, 2017, Chapman and Hall/CRC Ortaöğretim Bilgisayar Bilmi - Kur 1, Yasemin Gülbahar, 2017 - 2018, MEB yayınları www.python.org www.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	50
Other in-term studies:		0
Project:		0
Homework:		0
Quiz:		0
Final exam:	16	50
	Total weight:	(%)

Workload
Activity	Duration (Hours per week)	Total number of weeks	Total hours in term
Courses (Face-to-face teaching):	2	13
Own studies outside class:	4	14
Practice, Recitation:	2	12
Homework:	0	0
Term project:	0	0
Term project presentation:	0	0
Quiz:	0	0
Own study for mid-term exam:	10	2
Mid-term:	1	2
Personal studies for final exam:	20	1
Final exam:	2	1
		Total workload:
		Total ECTS credits:	*
	* ECTS credit is calculated by dividing total workload by 25. (1 ECTS = 25 work hours)