Welcome to Proteintech's Introduction to Python for Biologists online training course.

In this extensive beginner course, we will teach you the basics of python, from loops and conditions to data analysis. You can complete this course at your own pace. Your progress will be saved after each lesson so you can easily return and continue when you're ready. Remember to mark each section as "complete" using the button in the bottom right corner once you've finished.

Let's get started!

In this chapter, you’ll learn how to set up your coding environment using an Integrated Development Environment (IDE). This course will focus on Visual Studio Code as an IDE. You will learn how to install an IDE, add Python support, and run your very first script.

Before writing complex code, it is important to understand how Python works and how to write code that is functional, readable, and reusable.

In this chapter, you’ll learn:

• How Python runs code

• Variables and how they work

• Writing clean and readable code using comments and consistent formatting

• Strings and their manipulation, how to handle DNA sequences, protein names, sample IDs, and more

In this chapter, you will practice identifying and fixing common beginner mistakes using short, intentionally faulty scripts. Debugging is a core skill in scientific coding: you will spend as much time interpreting errors and checking assumptions as you do writing new code. These exercises are designed to strengthen your grasp of the basics (variables, data types, strings, lists, dictionaries, indexing/slicing, and simple method usage) by making you spot what Python is complaining about and why.

In this chapter, you will learn about Python’s core data structures, loops and conditional statements to organise data, repeat tasks, and make decisions based on data.

This chapter also includes functions for reusability, including debugging and error handling to produce robust scripts, and data cleaning to ensure accurate results.

In this chapter, you will learn how to visualize real biological data using NumPy, pandas, and plotting libraries to generate publication-ready plots.

You will also learn how to load different biological file formats, such as FASTA and PDB, and build a foundation working with real experimental datasets, building data processing pipelines, and generating graphs for analysis and reporting.

In this chapter, you will learn how to identify and fix common coding errors by practicing with real faulty scripts. Debugging is an important skill for every scientist working with data. This section will let you work through code examples that deliberately contain errors and are designed to reinforce your understanding of the basics.

In this chapter, you will learn how to apply Python to real-world biological experiments like Western blot quantification, ELISA, qPCR analysis, and protein structure exploration. These examples will show how to write reproducible, reusable Python scripts that clean data, perform statistical tests, and generate publication-ready plots.

🧬 R or Python? You choose.

Three of the examples in this chapter are also covered in Proteintech’s Introduction to R for Life Scientists course. The same workflows are used but coded in R instead of Python, allowing you to compare how different coding languages approach the same biological problems.

Congratulations on completing the Proteintech Learning Portal Introduction to Python for Biologists course. You are now ready to start writing your own workflows in Python!

Let's recap what we have learned.