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Algorithmic Thinking with Python: Foundations

This is the repository for the LinkedIn Learning course Algorithmic Thinking with Python: Foundations. The full course is available from LinkedIn Learning.

Algorithmic Thinking with Python: Foundations

The word “algorithm,” at one time the sole province of mathematics and computer science, has entered the modern vernacular because, for better or worse, algorithms have never been more important or more impactful in daily life. If you’re a developer, you need to be familiar with a wide range of algorithmic thinking in order to be able to solve new problems as they present themselves. If you’re already familiar with Python, becoming more versed in algorithmic thinking is a great way to increase your value as a developer. In this course, Robin Andrews explains how Python, because of its clarity and expressiveness, is the ideal tool for exploring algorithmic thinking. He shows you tools to help you understand the flow of algorithms, explains the brute force approach to solving algorithms, details the concepts of time and space complexity with regard to algorithm analysis, the decrease and conquer strategy, and much more.

Instructions

This repository has branches for each of the videos in the course. You can use the branch pop up menu in github to switch to a specific branch and take a look at the course at that stage, or you can add /tree/BRANCH_NAME to the URL to go to the branch you want to access.

Branches

The branches are structured to correspond to the videos in the course. The naming convention is CHAPTER#_MOVIE#. As an example, the branch named 02_03 corresponds to the second chapter and the third video in that chapter. Some branches will have a beginning and an end state. These are marked with the letters b for "beginning" and e for "end". The b branch contains the code as it is at the beginning of the movie. The e branch contains the code as it is at the end of the movie. The main branch holds the final state of the code when in the course.

When switching from one exercise files branch to the next after making changes to the files, you may get a message like this:

error: Your local changes to the following files would be overwritten by checkout:        [files]
Please commit your changes or stash them before you switch branches.
Aborting

To resolve this issue:

Add changes to git using this command: git add .
Commit changes using this command: git commit -m "some message"

Installing

  1. To use these exercise files, you must have the following installed:
    • Python 3
  2. Clone this repository into your local machine using the terminal (Mac), CMD (Windows), or a GUI tool like SourceTree.

Instructor

Robin Andrews

Check out my other courses on LinkedIn Learning.