Welcome to A Python Tutorial for Data Scientists!#

Created by EricJohnson

The purpose of this tutorial#

This tutorial is intended to be a brief introduction to computer programming for working with quantitative data in the Python programming language.

The tutorial will be conducted via Jupyter Notebooks, which provide a free and easy-to-use platform for Python programming.

This tutorial does not presume any previous coding experience - it will start from “what even is programming?” and will cover installation, setup, and the very basics. At the end of the tutorial, a student will be able to write simple Python programs, load and visualize data sets, and read other people’s Python code.

How to use this tutorial#

  1. Type everything yourself

    Writing code is a creative enterprise. You don’t learn to paint by reading about painting, and you similarly won’t learn to code just by reading this guide. Instead, each part of this tutorial comes with a notebook with which you can follow along by typing for yourself every Python command you see. This will allow you to practice as you learn and will help you get accustomed to Python’s syntax.

     

  2. Sections 1-7 are the baseline

    Once you have finished the sections up to functions you’ll have enough tools to start to parse other Python code. However, if you are taking What Do Your Data Say?, you should also complete the Plotting and Reading/Writing Data sections. These first sections are cumulative and build on each other, so going in order is advised.

     

  3. The internet is your friend!

    I have tried very hard to make this tutorial thorough, self-contained, and accessible, but is is very possible that my presentation of a topic will not connect with you. In that case, use the immense resources of the internet to see if there’s something out there with more details. Another similarly-leveled tutorial can be found here and I can recommend the tutorials at DataCamp as well. That being said, part of learning to code is learning to search for your issue or bug, and you should leverage the vast resources of the internet to your advantage!

Get started!#

With all this in mind, continue to the introduction to get started on your Python journey!

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