Welcome, dear reader, to this brief introduction to Python. This GitHub repository covers the fundamentals of Python, designed primarily for (prospective) master's students at the Faculty of Bioscience Engineering, Ghent University.
The material selected for this course, along with its presentation, focuses on topics that are (in our opinion) essential for your academic journey, particularly for courses that list basic programming skills as a prerequisite. This overview combines freely available online resources with dedicated content curated specifically for this repository.
Target audience:
- Bachelor students of Bioscience Engineering looking for a refresher of their Python skills
- Students with a basic programming background in another language who wish to learn Python for their studies at the Faculty of Bioscience Engineering.
Contents:
- the core concepts of Python, including variables, data types, loops and functions.
- basic scientific Python libraries: Numpy and Matplotlib.
The content of this course is arranged in 9 Lessons, each of which is a Jupyter Notebook file containing an introduction of the programming concepts covered, illustrations and a set of exercises. Click the link below to navigate to the contents-page. In case you are not familiar with running Python code, also check out the Google Colab and Jupyter Notebooks sections below before diving into the contents.
GET STARTED: Table of contents
Jupyter Notebooks
Jupyter Notebooks are an interactive environment that allows you to combine code, visualizations, and narrative text in a single document, making it ideal for exploratory data analysis and sharing results. While originally designed for Python, Jupyter Notebooks support over 40 programming languages, including R, Julia, and JavaScript, through different "kernels." The Python kernel is the most commonly used. In the resources below, Jupyter Notebooks are used extensively. You can find a demo of what a notebook is here - you can stop at 2:19 (no need to watch the installation procedure as we use Google Colab or VSCode).
Google Colab
Google Colab (short for Google Colaboratory) is a free, cloud-based platform provided by Google that allows you to write, execute, and share Python code in a web-based environment. It is particularly popular for data science, machine learning, and deep learning projects because it offers access to powerful computing resources like GPUs and TPUs, which are essential for handling large datasets and complex computations.
Google Colab is essentially a hosted Jupyter Notebook environment, so if you're familiar with Jupyter Notebooks, you'll feel right at home. You can write and run code in cells, visualize data, and document your workflow all in one place. Additionally, Colab integrates seamlessly with Google Drive, making it easy to save and share your work. It's an excellent tool for both beginners and experienced programmers who want to experiment with Python code without the need to set up a local development environment.
⚠️ Using Google Colab: Google Colab does not require any installation (it uses your internet browser) but tou do need a (free) Google account. You can access Google Colab using the urlhttps://colab.research.google.com/. More information on how to use Google Colab is shown here. All notebooks used in this introductory course can be run on Colab. Either download the notebooks and upload them to Colab (in the menu > upload ) or copy-paste the URL of the GIT-repo in Colab (in the menu > GitHub) .
A Python interpreter is a program that reads and executes your Python code. Unlike compiled languages, where code is first translated into machine language, Python is an interpreted language, meaning the interpreter processes your code line by line, translating it into actions immediately. You need a Python interpreter to run your Python scripts and interact with your code, making it essential for executing and testing your programs.
An Integrated Development Environment (IDE) is a software application that provides a comprehensive environment for coding. An IDE typically includes a code editor, a debugger, and tools for running and testing your code, all within one interface. Using an IDE can streamline your coding process, offering features like syntax highlighting, code suggestions, and version control, which help improve productivity and reduce errors. Popular Python IDEs include PyCharm, Visual Studio Code, and Jupyter Notebook.
⚠️ Installation: Guidelines on how to install the Python Interpreter and VSCode Code and how to setup a working envirment in VSCode can be found here. If you prefer to skip the installation step, you can use Google Colab (see below).
- Whirlwind Tour Of Python by Jake VanderPlas [git repo]
- VSCode [web link]
- Free book [web link]
- Slide deck for Crash course Python [right click to download -> Save link as ...]
- Exercises Numpy: [right click to download -> Save link as ...]