Python for Biologists

Details of the curriculum

The course comprises lectures and hands-on sessions devoted to programming in Python, specifically designed for biologists who are new to programming. It focuses on Python to address applied problems in statistics and bioinformatics. We start with fundamental programming concepts, scripting, and file manipulation before moving on to the use of specialized packages for handling biological data.

All classes will be given in Ukrainian.

Lectures
Lecture 1. Basics of computer architecture: memory, processors, GPU.
Taras VASYLYSHYN

Programming languages and their applications. Advantages and disadvantages of Python. Setting up your first project. Fundamentals of writing programs and code formatting in Python.

Lecture 2. Data types in Python.
Taras VASYLYSHYN

Numeric Types and Boolean: Understanding integers, floats and the usage of Boolean values. Collections: Exploring sequences like Strings, Lists, Tuples, Dictionaries and Sets. Testing code.

Lecture 3. Functions.
Taras VASYLYSHYN

Defining and invoking functions, parameters, and return values. Control Structures. Loops: Using “for” and “while” loops for repeated execution. Logical Expressions: Implementing “if”, “else”, and “elif” statements for decision making.

Lecture 4. Basic string operations.
Taras VASYLYSHYN

String formatting. Finding substrings. Comparing strings and checking string properties. Regular expressions.

Lecture 5. Working with different types of text files in Python.
Taras VASYLYSHYN

Reading and Writing Text Files. Handling CSV Files. File Manipulation.

Lecture 6. Statistics in Python.
Viktor HUSAK

Descriptive statistics of data series. Testing sample for normal distribution.

Lecture 7. Analysis of variance (ANOVA). Correlation and regression analysis.
Viktor HUSAK
Lecture 8. Basic data visualization.
Viktor HUSAK

(Seaborn and Matplotlib). Creating plots for biological data: scatter plots, bar plots, box plots, heatmaps, volcano plots. Adding error bars and statistical annotations. Plot customization with proper labels and legends. Export of high-resolution images.

Lecture 9. Biopython application to genomic data.
Sviatoslav KHARUK

FASTA file reading/writing. Sequence object manipulation, DNA/RNA transcription and translation. Sequence statistics calculation.

Lecture 10. Pandas.
Sviatoslav KHARUK

DataFrame operations for biological data - reading CSV/TSV files, filtering, sorting, merging datasets. Handling of missing values. Basic data cleaning. Expression data normalization.

Seminars
Seminar 1. Introduction to Python. Code formatting in Python. Input and output.
Taras VASYLYSHYN
Seminar 2. Numerical data types. Precision. Arithmetic operations. Mathematical functions.
Taras VASYLYSHYN
Seminar 3. Testing code.
Taras VASYLYSHYN
Seminar 4. Boolean data type. Logical operations.
Taras VASYLYSHYN
Seminar 5. Loops and branching.
Taras VASYLYSHYN
Seminar 6. Lists and Tuples.
Taras VASYLYSHYN
Seminar 7. Dictionaries and Sets.
Taras VASYLYSHYN
Seminar 8. String operations.
Taras VASYLYSHYN
Seminar 9. Regular expressions.
Taras VASYLYSHYN
Seminar 10. Working with text files.
Taras VASYLYSHYN
Seminar 11. Biological data visualization: Seaborn and Matplotlib.
Sviatoslav KHARUK
Seminar 12. Work with big datasets.
Sviatoslav KHARUK
Seminar 13. Applied project: software for statistical analysis (I) – interface.
Viktor HUSAK
Seminar 14. Applied project: software for statistical analysis (II) – implementation of descriptive statistics.
Viktor HUSAK
Seminar 15. Applied project: software for statistical analysis (III) – implementation of outliers.
Viktor HUSAK
Seminar 16. Applied project: software for statistical analysis (IV) – implementation of distribution normality check.
Viktor HUSAK
Seminar 17. Applied project: software for statistical analysis (V) – comparing experimental results: Student's t-Test.
Viktor HUSAK
Seminar 18. Applied project: software for statistical analysis (VI) – comparing experimental results: One-way ANOVA (ANalysis Of VAriance) with post-hoc Tukey HSD (Honestly Significant Difference). Test Calculator for comparing multiple treatments.
Viktor HUSAK
Seminar 19. Applied project: software for statistical analysis (VII) – One-way ANOVA with post-hoc Dunn Test Calculator for comparing multiple treatments.
Viktor HUSAK
Seminar 20. Applied project: software for statistical analysis (VIII) – implementation of correlation and regression.
Viktor HUSAK
Level
Bachelor and master students
Lectures
10
Practical classes
20
Duration
2 Months
Language
Ukrainian
Certificate
2 credits ECTS
Timetable
Date Time Topic Lectures
03.02.2025 Mon 18:00 Lecture 1. Basics of computer architecture: memory, processors, GPU. Taras VASYLYSHYN
04.02.2025 Tue 18:00 Seminar 1. Group 1.Introduction to Python. Code formatting in Python. Input and output. Taras VASYLYSHYN
05.02.2025 Wed 18:00 Seminar 1. Group 2. Introduction to Python. Code formatting in Python. Input and output. Taras VASYLYSHYN
10.02.2025 Mon 18:00 Lecture 2. Data types in Python. Taras VASYLYSHYN
11.02.2025 Tue 18:00 Seminar 2. Group 1. Numerical data types. Precision. Arithmetic operations. Mathematical functions. Taras VASYLYSHYN
12.02.2025 Wed 18:00 Seminar 2. Group 2Numerical data types. Precision. Arithmetic operations. Mathematical functions. Taras VASYLYSHYN
17.02.2025 Mon 18:00 Seminar 3. Group 1. Testing code. Taras VASYLYSHYN
18.02.2025 Tue 18:00 Seminar 3. Group 2. Testing code. Taras VASYLYSHYN
19.02.2025 Wed 18:00 Seminar 4. Group 1Boolean data type. Logical operations. Taras VASYLYSHYN
24.02.2025 Mon 18:00 Seminar 4. Group 2Boolean data type. Logical operations. Taras VASYLYSHYN
25.02.2025 Tue 18:00 Lecture 3. Functions. Taras VASYLYSHYN
26.02.2025 Wed 18:00 Seminar 5. Group 1Loops and branching. Taras VASYLYSHYN
03.03.2025 Понеділок 18:00 Seminar 5. Group 2Loops and branching. Taras VASYLYSHYN
04.03.2025 Mon 18:00 Seminar 6. Group 1Lists and Tuples. Taras VASYLYSHYN
05.03.2025 Wed 18:00 Seminar 6. Group 2Lists and Tuples. Taras VASYLYSHYN
10.03.2025 Mon 18:00 Seminar 7. Group 1. Dictionaries and Sets. Taras VASYLYSHYN
11.03.2025 Tue 18:00 Seminar 7. Group 2. Dictionaries and Sets. Taras VASYLYSHYN
12.03.2025 Wed 18:00 Lecture 4. Basic string operations. Taras VASYLYSHYN
17.03.2025 Mon 18:00 Seminar 8. Group 1. String operations. Taras VASYLYSHYN
18.03.2025 Tue 18:00 Seminar 8. Group 2. String operations. Taras VASYLYSHYN
19.03.2025 Wed 18:00 Seminar 9. Group 1Regular expressions. Taras VASYLYSHYN
24.03.2025 Mon 18:00 Seminar 9. Group 2Regular expressions. Taras VASYLYSHYN
25.03.2025 Tue 18:00 Lecture 5. Working with different types of text files in Python. Taras VASYLYSHYN
26.03.2025 Wed 18:00 Семінар 10. Group 1Working with text files. Taras VASYLYSHYN
31.03.2025 Mon 18:00 Семінар 10. Group 2Working with text files. Taras VASYLYSHYN
02.04.2025 Wed 18:00 Lecture 6. Statistics in Python. Viktor HUSAK
03.04.2025 Thu 18:00 Lecture 7. Analysis of variance (ANOVA). Correlation and regression analysis. Viktor HUSAK
07.04.2025 Mon 18:00 Lecture 8. Basic data visualization. Viktor HUSAK
08.04.2025 Tue 18:00 Lecture 9. Biopython application to genomic data. Sviatoslav KHARUK
09.04.2025 Wed 18:00 Seminar 11. Group 1Biological data visualization: Seaborn and Matplotlib. Viktor HUSAK
10.04.2025 Thu 18:00 Seminar 11. Group 2Biological data visualization: Seaborn and Matplotlib. Viktor HUSAK
11.04.2025 Fri 18:00 Lecture 10. Pandas. Sviatoslav KHARUK
14.04.2025 Mon  18:00 Seminar 12. Group 1Work with big datasets. Sviatoslav KHARUK
15.04.2025 Tue 18:00 Seminar 12. Group 2Work with big datasets. Sviatoslav KHARUK
16.04.2025 Wed 18:00 Seminar 13. Group 1Applied project: software for statistical analysis (I) – interface. Viktor HUSAK
17.04.2025 Thu 18:00 Seminar 13. Group 2Applied project: software for statistical analysis (I) – interface. Viktor HUSAK
21.04.2025 Mon  18:00 Seminar 14. Group 1Applied project: software for statistical analysis (II) – implementation of descriptive statistics. Sviatoslav KHARUK
22.04.2025 Tue 18:00 Seminar 14. Group 2Applied project: software for statistical analysis (II) – implementation of descriptive statistics. Sviatoslav KHARUK
23.04.2025 Wed 18:00 Seminar 15. Group 1Applied project: software for statistical analysis (III) – implementation of outliers. Viktor HUSAK
24.04.2025 Thu 18:00 Seminar 15. Group 2Applied project: software for statistical analysis (III) – implementation of outliers. Viktor HUSAK
28.04.2025 Mon 18:00 Seminar 16. Group 1Applied project: software for statistical analysis (IV) – implementation of distribution normality check. Viktor HUSAK
29.04.2025 Tue 18:00 Seminar 16. Group 2Applied project: software for statistical analysis (IV) – implementation of distribution normality check. Viktor HUSAK
30.04.2025 Wed 18:00 Seminar 17. Group 1. Applied project: software for statistical analysis (V) – comparing experimental results: Student's t-Test. Viktor HUSAK
01.05.2025 Thu 18:00 Seminar 17. Group 2. Applied project: software for statistical analysis (V) – comparing experimental results: Student's t-Test. Viktor HUSAK
05.05.2025 Mon 18:00 Seminar 18. Group 1. Applied project: software for statistical analysis (VI) – comparing experimental results: One-way ANOVA (ANalysis Of VAriance) with post-hoc Tukey HSD (Honestly Significant Difference). Test Calculator for comparing multiple treatments. Viktor HUSAK
06.05.2025 Tue 18:00 Seminar 18. Group 2. Applied project: software for statistical analysis (VI) – comparing experimental results: One-way ANOVA (ANalysis Of VAriance) with post-hoc Tukey HSD (Honestly Significant Difference). Test Calculator for comparing multiple treatments. Viktor HUSAK
07.05.2025 Wed 18:00 Seminar 19. Group 1. Applied project: software for statistical analysis (VII) – One-way ANOVA with post-hoc Dunn Test Calculator for comparing multiple treatments. Viktor HUSAK
08.05.2025 Tyu 18:00 Seminar 19. Group 2. Applied project: software for statistical analysis (VII) – One-way ANOVA with post-hoc Dunn Test Calculator for comparing multiple treatments. Viktor HUSAK
12.05.2025 Mon 18:00 Seminar 20. Group 1. Applied project: software for statistical analysis (VIII) – implementation of correlation and regression. Viktor HUSAK
13.05.2025 Tue 18:00 Seminar 20. Group 2. Applied project: software for statistical analysis (VIII) – implementation of correlation and regression. Viktor HUSAK
    TEST  
Lecturers

Leading specialist at the Department of Biochemistry and Biotechnology at Vasyl Stefanyk Precarpathian National University, Ivano-Frankivsk.

Doctor of Science in Physics and Mathematics, Professor of the Department of Mathematical and Functional Analysis of PNU where he teaches several courses including "Statistics and Python"

Associate Professor of the Department of Biochemistry and Biotechnology at the Vasyl Stefanyk Precarpathian National University.