Setting Up Your Python Data Science Environment

Never miss a tutorial! Subscribe to the Project Data Science channel: https://bit.ly/3yTMdQV Go from zero to hero with our Data Science Specialization: https://courses.projectdatascience.com/courses/becoming-a-data-science-practitioner. Or learn about all of data science in one blog post! https://www.projectdatascience.com/what-is-data-science/ ... Setting up a Python data science coding environment can be a daunting task if you're just getting started. There seem to be hundreds of different ways to do it, and you can spend hours (if not days, or weeks) just trying to sift through the technologies, the tools, etc... That's why I created this video—to show you how to set up a simple, professional data science environment with all of the pieces you need to get started right away: Python, and all of the other stuff. (By the way, if you want to just get started with data science work and don't want to set anything up on your computer yet, check out Google Colab. It's basically everything you need right in your browser! https://colab.research.google.com/ It has Python, pandas, Matplotlib, Scikit-Learn, TensorFlow, and most of the other libraries you need.) And of course you can always change the pieces of this environment later if you find something else that suits you better. But rather than spending weeks figuring out your first version, take this one and run with it! Many professional data scientists—including myself—use this setup, so it should work wonderfully for you as well. Here's what the environment will consist of: - Terminal (Zsh) - Python (installed using miniconda) - VS Code (your code editor) - Git and GitHub (version control for your code) - Virtual environments (created using conda) - Jupyter notebooks (installed in your virtual environment) I'll also show you how I like to set up my project directory to keep it nice and organized, and we'll walk through some of the first steps you can take to create files and commit them using Git and GitHub. Grab your computer and dive in! Happy learning! --- 00:00 Introduction 04:27 The terminal (bash/zsh) 09:20 Using the terminal to set up our project directory 10:52 Python and virtual environments with conda 23:02 Using VS Code (code editor) 34:58 Version control with git and GitHub 49:25 Jupyter notebooks 56:24 Cookiecutter Data Science project structure 01:02:09 Wrap-up and thank you! --- Additional Project Data Science Resources: - https://projectdatascience.com/ (The official Project Data Science site!) - https://www.youtube.com/watch?v=-Db9ecTQXUE (Pandas Mega-Tutorial - everything you need to know when you're first learning pandas, one of the main Python libraries for data science) - https://www.youtube.com/watch?v=axSTGczvYIE (Matplotlib Mega-Tutorial - data visualization in Python!) - https://www.youtube.com/watch?v=rdaG53khzv0&list=PLMAyPTgGwv2DUV6DZib9eMetsTTX87JNr (Beginner's introduction to machine learning with Python.) - https://www.youtube.com/watch?v=GNKt8TAIAVc (Beginner's introduction to neural networks in Python.) --- Additional Resources: - https://docs.conda.io/en/latest/miniconda.html (Miniconda -- this is how I recommend installing Python and conda. We use conda for virtual environments.) - https://drivendata.github.io/cookiecutter-data-science/ (Cookiecutter Data Science is the project directory structure I like to use when I'm setting up new projects!) - https://guides.github.com/introduction/git-handbook/ (Git Handbook - Introduction to Git.) - https://guides.github.com/activities/hello-world/ (Intro tutorial with Git and GitHub.) - https://realpython.com/python-virtual-environments-a-primer/ (An introduction to virtual environments in Python.) - https://ohmyz.sh/ (Oh My Zsh!) - https://ubuntu.com/tutorials/command-line-for-beginners (Intro to learning the command line / terminal.)