Base.cs Podcast

If you know about venn diagrams, then you basically know set theory! We explain how the two are related and connect it back to computer science with the help of some of our favorite foods.

The Brians still need their own tables! We resolve our collision with a new strategy: chaining.

What do you do when you're in a hash table, and two pieces of data get assigned the same spot?! You've got a collision, and you need a resolution! We dig into one strategy to resolve a collision, and make sure each data has a spot.

In our intro to hash tables, we use books, pizza toppings, and fridge operators to break down how hash tables work and what makes them so awesome.

How does breadth-first search actually work? And how do you know whether you should use that, or depth-first search? And what's the Big O notation for BFS anyway? Let's find out!

We are getting in line, or enqueuing, for breadth-first search! We walk through the steps and compare the process to depth-first search.

How does depth-first search perform in terms of Big O notation? And how do you actually implement it, in coding terms? Let's find out!

Let's dig into another depth-first search strategy: inorder! This time, we walk through a numerical example, traversing the tree with fresh, animated voices and a broken washing machine.

We dive into depth-first search by exploring our first of three strategies: preorder! Let's walk through an example step-by-step and get to know members of Saron's fictitious tree family along the way.

How are algorithms related to brownies? And how do we navigate through the nodes of a tree when implementing depth-first search?

We use a triangle to trace simple paths and finally get to the bottom of the seven bridges problem that helped launch graph theory.

We go all the way back to 1735 to a place called Königsberg. It had seven bridges and a tricky math problem that led to the creation of graph theory.

We explore what graphs are, how to define them, and how they're related to discrete mathematics.

What does it mean for an algorithm to be logarithmic? We revisit Big O notation, this time in the context of binary search.

If you've heard of binary trees, you've probably heard of binary search. But how does a binary search algorithm actually work? And do you have to have binary trees, or can you use it on other things?