They are sorted by difficulty, so the first project is the simplest, while the last one is the hardest. The following beginner projects help us put into practice all the things we’ve learned in the previous weeks, so consider taking at least one of them. Week 2: Variables, Lists, Tuples and Dictionaries Week 1: Introduction to Jupyter Notebook & Data Types (integer, float, boolean, string, etc) This editor allows us not only to write code but also to write equations, plot visualizations, add text, and more things that make look our Python script like a notebook. Keep in mind that the text editor you should use as an aspiring data scientist is Jupyter Notebook. They are key to doing more advanced stuff in Python. Other important things to learn are conditional statements (if/else statement) and loops (for, while, etc). This means learning the most common data types, how to use variables and how to properly use lists and dictionaries.
The first thing to do to master Python for data science is to understand the core concepts. Be sure to subscribe here to get my Python for Data Science Cheat Sheet I use in all my tutorials (Free PDF) Python Core Concepts for Data Science
