Compiled Learning Objectives

Memory consumption

• Quantify the memory used by different arrays

Big-O

• Categorise algorithms as O(lg(n)), O(n), O(n^2), O(2^n)

"Expensive" Operations

• Explain what the significant/expensive operations for a particular algorithm are likely to be
• Quantify the number of significant operations taken by a particular algorithm

Network as a bottleneck

• Explain limitations of needing to make network calls (e.g. from a backend to a database)

N+1 Query Problem

• Define the n+1 query problem
• List effective strategies to reduce database queries

1. Complete Space and Time Complexity in Data Structure and Algorithms

• Define Big O notation
• List 8 common levels of time complexity
• Identify the time complexity of given common (named) algorithms

2. Research Complexity using the Google DSA path

• Plan a course of study
• Self evaluate progress with testing

3. Analyse and Refactor Functions

• Identify areas of inefficiency that contribute to higher time or space complexity
• Refactor the code to reduce its complexity.
• Review the analyses of others

4. Prepare your lesson on complexity

• Put together a presentation on a topic you explored in prep

5. Read Chapter 8 of How Computers Really Work

• Write a program to calculate the factorial of a number in Assembly

6. Codewars Fibonacci Series

• Solve dynamic programming problems using recursion and memoisation

Sorting Cards Workshop

• Analyse the actual cost (comparisons + swaps) of particular sorting algorithms.
• Categorise different sorting algorithms by computational complexity.
• Identify when different sorting algorithms are suitable.
• Devise a specialised sorting algorithm for specific kinds of data.