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Syllabus ( MBG 524 )

   Basic information
Course title: Computer Programming in Biology
Course code: MBG 524
Lecturer: Assist. Prof. Pınar PİR
ECTS credits: 7.5
GTU credits: 3 (3+0+0)
Year, Semester: 1/2, Fall
Level of course: Second Cycle (Master's)
Type of course: Area Elective
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 graduate bioinformatics 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. Process biological data files in Python

    Contribution to Program Outcomes

    1. Define and manipulate advanced concepts of Biology
    2. Work effectively in multi-disciplinary research teams
    3. Understand the applications and basic principles of new instrumentation and/or software vital to his/her thesis projects.

    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 advanced concepts of Biology
    2. Acquire scientific knowledge and work independently,
    3. Work effectively in multi-disciplinary research teams
    4. Understand the applications and basic principles of new instrumentation and/or software vital to his/her thesis projects.

    Method of assessment

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

    Contribution to Program Outcomes

    1. Define and manipulate advanced concepts of Biology
    2. Acquire scientific knowledge and work independently,
    3. Understand the applications and basic principles of new instrumentation and/or software vital to his/her thesis projects.

    Method of assessment

    1. Written exam
    2. Homework assignment
   Contents Up
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*: -
Textbooks and materials: Ders notları, örnek kodlar, Jupyter defterleri.
Lecture notes, sample codes, Jupyter notebooks
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
  * 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 30
Other in-term studies: 0
Project: 0
Homework: 2,4,6,8,10,12,14 30
Quiz: 0
Final exam: 16 40
  Total weight:
(%)
   Workload Up
Activity Duration (Hours per week) Total number of weeks Total hours in term
Courses (Face-to-face teaching): 2 14
Own studies outside class: 2 14
Practice, Recitation: 2 12
Homework: 4 7
Term project: 0 0
Term project presentation: 0 0
Quiz: 0 0
Own study for mid-term exam: 20 2
Mid-term: 1 2
Personal studies for final exam: 30 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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