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Overview

Welcome to the Python programming in biology and chemistry course repository! This repository contains exercises, solutions, and notebooks for learning the Python programming language tailored specifically for applications in biology and chemistry.

Python is a versatile and powerful programming language known for its simplicity and readability. Its extensive libraries and frameworks make it particularly relevant in scientific fields such as biology and chemistry. Python enables researchers to automate tasks, analyze data, simulate biological processes, and visualize complex datasets efficiently.

💫 Outline

In this course, we will cover all the basics of Python programming, starting from procedural programming concepts to advanced topics like object-oriented programming. You will learn the following:

  • Basic Python syntax and data types
  • Control structures (if-else, loops)
  • Functions and modules
  • File handling
  • Data handling with NumPy and Pandas
  • Data visualization with Matplotlib and Seaborn
  • Data analysis with Scikit-learn
  • Object-oriented programming
  • Inheritance and polymorphism

This course is designed for beginners with no prior programming experience. By the end of this course, you will have a solid foundation in Python programming and be able to apply your skills to solve problems in biology and chemistry.

📚 Material

Jupyter notebooks are an excellent way to learn Python programming interactively. The live code course material and exercises are organized into several Jupyter notebooks, each covering a specific topic. You can view and run these notebooks directly in your browser using Google Colab by visiting the dedicated repository on Git.

Notebooks

The notebooks directory contains on-the-fly code snippets and examples that have been created during the course to demonstrate concepts and answer questions. These notebooks serve as additional resources to enhance your understanding of Python programming and to recapitulate concepts that were discussed.

Exercises

Inside the exercises directory in the repository, you will find a collection of Jupyter notebooks (.ipynb files) covering various topics in Python. Each notebook contains a set of exercises designed to help you practice and solidify your understanding of Python concepts. Solutions to the exercises will be provided in the solutions directory.

Using Google Colab

Google Colab is a free cloud-based Jupyter notebook environment provided by Google. It allows you to write and execute Python code directly in your browser without any setup required. You can use Google Colab to run the course material and exercises interactively. Here's how you can use the exercises in Google Colab:

  1. Open Google Colab in your web browser: Google Colab.

  2. In the Colab interface, go to File > Open Notebook.

  3. Select the option GitHub and paste the URL of this repository: GitHub Repository.

  4. You will see a list of notebooks available in the repository. Click on the notebook you want to work on.

  5. Once the notebook is opened, you can run the code cells, edit them, and interact with the exercises just like you would in any Jupyter environment.

  6. If you want to save your changes, you can either save the notebook to your Google Drive or download it to your local machine.

Each notebook contains a "Open in Colab" button at the top, making it easy for you to directly open and work with the exercises in Google Colab.