Course code:
065B2		 Course name:
Basics of Programming for Biochemists

Academic year:

2024/2025.

Attendance requirements:

There are no requirements.

ECTS:

6

Study level:

graduate academic studies

Study program:

Biochemistry: 1. year, winter semester, elective (E51B2) course

Teacher:

Jovana T. Kovačević, Ph.D.
assistant professor, Faculty of Mathematics, Studentski trg 16, Beograd

Assistant:

Nevena . Ćirić

Hours of instruction:

Weekly: two hours of lectures + two hours of exercises (2+2+0)

Goals:

Acquisition of general and specific programming knowledge in Python programming language.

Outcome:

Upon completion of the course, the student has adopted the basic concepts of procedural and object programming, such as data types, control structures, functions, arrays, methods and classes, as well as basic procedures of writing, executing, testing and debugging the programs.

Teaching methods:

Lectures, labwork.

Extracurricular activities:

—

Coursebooks:
  • Mitchell L. Model: Bioinformatics Programming Using Python, 2009, O'Reilly, ISBN 9781449382902

Additional material:

—

  Course activities and grading method

Lectures:

0 points (2 hours a week)

Syllabus:

  1. Setting up the work environment (Python installation, installation and use of Jupyter environment).
  2. Variables and data types.
  3. Lists and strings.
  4. Input / output and error processing (reading data from standard input, writing data to standard output, reading data from files, writing data to files, error processing).
  5. Branches (if, elif, else commands).
  6. Loops (for, while commands).
  7. Functions (calling functions, returning results, passing arguments, variable number of arguments).
  8. Introduction to Object Oriented Programming (classes, inheritance, hierarchical polymorphism).
  9. Use of publicly available biological databases and tools for their processing (PDB, NCBI, Uniprot, Disprot, BLAST, Clustal...).
  10. Biopython Library (installation, reading of different data formats (FASTA, GeneBank...), sequence alignment, text search, KNN, K-means).
  11. Introduction to libraries Numpy, Pandas and Scikit-learn.
  12. Data visualization (different types of diagrams).

Exercises:

0 points (2 hours a week)

Syllabus:

The lab program follows the lecture program. Each of the topics presented in the lectures is practically practiced on examples of different complexity. The focus is on the bioinformatics applications.

Colloquia:

20 points

Homework:

20 points

Written exam:

45 points

Oral exam:

15 points