Compiled Learning Objectives

At the end of the sprint, you should be able to do the following things. Make sure to check you can do them all, and if not, go back and keep practicing until you can.

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

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

Codewars Fibonacci Series

• Solve dynamic programming problems using recursion and memoisation

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

Prepare your lesson on complexity

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

Read Chapter 8 of How Computers Really Work

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

Research Complexity using the Google DSA path

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

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.

Discussion: Technical Interviews

• Identify preparation and techniques for succeeding at technical interviews.