Written by Adithya Solai
This is a self-serve, self-paced Python curriculum. Read Adithya's Python Curriculum.pdf to get a high-level breakdown of the curriculum.
I originally used this curriculum to do private 1-on-1 Python lessons. I have open-sourced this material for everyone to use. I have tested this curriculum as a self-serve educational tool by giving it to my Univ. of Maryland CS Undergrad mentees. They found it useful, and I hope you do as well.
Lessons 1-4 cover basic Python concepts that are found in most other programming languages (variables, operators, logic flow, for-loops & while-loops, lists, sets, dictionaries, Objects, etc.). Make sure to do the Problem Sets associated with each Lesson (example: Problem Set 1 should be done after completing Lesson 1). Each Problem Set also has an answer key (/problemSets/answerKeys directory). The exercises inside of Lessons also have a separate answer key (/lessons/answerKeys directory). There are also Projects (read Adithya's Python Curriculum.pdf to learn the appropriate time to do each Project).
The main value of this curriculum comes from the data analysis content in Lessons 5-7, Problem Set 5, Projects 3 & 4, and the Final Project. It's harder to make educational content on data analysis tools than on the vanilla Python concepts covered in Lessons 1-4. The goal of this curriculum is to make you comfortable with common Data Analysis tools (NumPy, Pandas, matplotlib, seaborn, sklearn, etc.) and have 3 personal projects that you can display on their GitHub & Resume (Project 3, Project 4, and Final Project).
My approach to this content is to make you learn by doing. There is some example code given in Lessons 5-7, but the associated Problem Sets and Projects emphasize using Google to learn what needs to be learned to solve the task at hand.
If you spot something that could be improved, please email me at adithyasolai7@gmail.com.