Recommended post-school resources

A curated starting point for continued learning in bioinformatics, data science, and research.

These resources are useful after the summer school for building practical skills, revisiting core concepts, and finding opportunities for further study. Start with the section closest to your current interests, then follow the links at your own pace.

Bioinformatics and computational biology

Bioconductor

The Bioconductor training and education resources include courses and teaching materials for bioinformatics in R. You may also find these useful:

Practice problems

Rosalind
programming and algorithms problems designed around biology.
Software Carpentry lessons
lessons on Git, version control, Python, the Unix shell, and related tools.

Image analysis

Microscopy image analysis course
course materials from BioInUA 2025.

Data science and machine learning

Practice and online courses

Kaggle competitions
open data science problems and competitions.
Linux Foundation training portal
courses covering Git, the Bash shell, and general IT topics.
Git Mastery playground
an interactive introduction to Git.
Hugging Face courses
courses on modern AI and machine-learning models; some may be free for students in Ukraine.

Additional courses

CS50’s Introduction to Programming with Python
Harvard’s free course - covers Python basics, problem-solving, and projects.
Introduction to Machine Learning with Python
Official scikit-learn tutorials (with Jupyter notebooks) teaching supervised and unsupervised ML.
DS100 Principles and Techniques of Data Science
Harvard’s free course - covers all basic and some advanced ML and DS topics.
TensorFlow Bootcamp
Basic NN with tensorflow and intro to computer vision.
Data Science: R Basics
Clear, free introduction to R syntax, data structures, and programming logic.
Introduction to Statistical Learning (ISLR)
Free book + video lectures; practical machine learning/statistics in R.

Books

Python books

Basic: Byte of Python(Swaroop C.H.)
A very beginner-friendly introduction to Python, covering syntax, data types, and fundamental concepts with clear examples(Ukrainian version)
Less basic: Automate the Boring Stuff with Python (Al Sweigart)
Practical guide to applying Python for everyday tasks - file handling, web scraping, Excel automation, and more.
Advanced: Fluent Python (Luciano Ramalho)
Focuses on writing idiomatic, efficient Python code, including advanced data structures, metaprogramming, and concurrency.
ML focused: Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow (Aurélien Géron)
Step-by-step introduction to machine learning and deep learning in Python with practical examples and projects.
ML focused 2: The Orange book of machine learning(Carl McBride Ellis)
The essentials of making predictions using supervised regression and classification for tabular data

R and statistics books

Basic: R for Data Science (Hadley Wickham & Garrett Grolemund)
Gentle, practical introduction to R focused on data wrangling, visualization, and tidyverse philosophy.
Less basic: Discovering Statistics using R (Andy Field)
Engaging and accessible introduction to statistics using R, full of worked examples and humor.
Advanced: Advanced R (Hadley Wickham)
Covers deep internals of R environments, functional programming, object-oriented systems—aimed at serious R programmers.
Application of R and statistics in life sciences: Modern Statistics for Modern Biology (W. Huber)
A complicated but readable treatment of modern statistics tailored for biological and life sciences applications.

Additional videos and tutorials

Internships and funding

For students looking for funded research internships abroad, the Molecular Biology School summer internship program offers funding for life-science research internships worldwide for up to three months in a laboratory of the student’s choice.