During this sprint, we will introduce you to some fundamental Computer Science fundamentals. First, we will introduce you to Python (the language we will be using throughout Computer Science). Second, you will learn about Lambda's Problem-Solving Framework, which we call U.P.E.R. Third, you will learn about Big O notation and analyzing an algorithm's time and space complexity. Last, you will learn about arrays and in-place and out-of-place algorithms.
All of these topics lay down a crucial base for the other three sprints in Computer Science. You will rely on your Python skills, problem-solving abilities, ability to analyze time and space complexity, and your mental model for computer memory throughout the rest of the course.
In this module, you will begin to learn the fundamentals of the Python programming language. Additionally, you will learn about the U.P.E.R. Problem-Solving framework and best practices for asking for help. After completing this module, you will have all the basics that you need to get started using Python to solve algorithmic code challenges and deepen your understanding and skill set related to programming.
In this module, you will continue to learn the fundamentals of the Python programming language and put the U.P.E.R. Problem-Solving framework into practice. These skills are crucial as you encounter harder and harder code challenges in preparation for the technical interview process.
This module will teach about mutability vs. immutability, time complexity analysis, and space complexity analysis. These topics are incredibly crucial for optimizing the algorithms that you write and ensuring that we prepare you for the technical interview process.
In this module, you will learn about static arrays, dynamic arrays, and the difference between an in-place algorithm and an out-of-place algorithm.