Compiled Learning Objectives

Memory consumption (Prep)

• Quantify the memory used by different arrays

Big-O (Prep)

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

"Expensive" Operations (Prep)

• 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 (Prep)

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

N+1 Query Problem (Prep)

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

Analyse and Refactor Functions (Backlog)

• 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 (Backlog)

• Solve dynamic programming problems using recursion and memoisation

Complete Space and Time Complexity in Data Structure and Algorithms (Backlog)

• 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 (Backlog)

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

Read Chapter 8 of How Computers Really Work (Backlog)

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

Research Complexity using the Google DSA path (Backlog)

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

Sorting Cards Workshop (Day Plan)

• 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 (Day Plan)

• Identify preparation and techniques for succeeding at technical interviews.