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Syllabus ( BENG 215 )


   Basic information
Course title: Python for Bioengineers
Course code: BENG 215
Lecturer: Assist. Prof. Pınar PİR
ECTS credits: 6
GTU credits: 3 ()
Year, Semester: 2, Fall
Level of course: First Cycle (Undergraduate)
Type of course: Compulsory
Language of instruction: English
Mode of delivery: Face to face
Pre- and co-requisites: none
Professional practice: No
Purpose of the course: This course aims to train undergraduate bioengineering students on basic computer programming techniques via Python programming language. The course is composed of complementary lectures and laboratory sessions.
   Learning outcomes Up

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. Homework assignment
  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
    2. Oral 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
    2. Homework assignment
   Contents Up
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
Homework1
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
Homework2
Week 9: Functions in Python
Week 10: Modules in Python
Week 11: Reading and writing data in files
Homework3
Week 12: MatPlotLib module and plotting charts
Midterm exam II
Week 13: Numpy module and array data type
Week 14: Scipy module
Homework4
Week 15*: General review
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 Up
Method of assessment Week number Weight (%)
Mid-terms: 6,11 40
Other in-term studies: 0
Project: 0
Homework: 4,8,11,14 30
Quiz: 0
Final exam: 16 30
  Total weight:
(%)
   Workload Up
Activity Duration (Hours per week) Total number of weeks Total hours in term
Courses (Face-to-face teaching): 2 13
Own studies outside class: 2 14
Practice, Recitation: 2 12
Homework: 6 4
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)
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