Base.cs Podcast

In this episode, we get into what a compiler is and does. In short, a compiler is a program that reads our code (or any code, in any programming language), and translates it into another language. You...

In this episode, we get into parse trees, an illustrated, pictorial version of the grammatical structure of a sentence, which is important to understanding how computers understand coding syntax. Base...

We continue our journey with the Traveling Salesman Problem (TSP), where this we imagine a salesperson has to travel to every single city in an area, visiting each city only once. Additionally, they n...

We start our season off with something that often pops up in technical interviews: the Traveling Salesman Problem (TSP). In this problem, a salesperson has to travel to every single city in an area, v...

In this last episode of the season we continue our discussion of dynamic programming, and show just how efficient it can be by using the Fibonacci sequence! Based on Vaidehi Joshi's blog post, "Less R...

In this episode we talk about different paradigms and approaches to algorithmic design: the Divide and Conquer Algorithm, the Greedy Algorithm, and the Dynamic Programming Algorithm, which remembers t...

We continue our talk about Dijkstra's algorithm, which can be used to determine the shortest path from one node in a graph to every other node within the same graph data structure, provided that the n...

In this episode, we talk about Dijkstra's algorithm, which can be used to determine the shortest path from one node in a graph to every other node within the same graph data structure, provided that t...

We end our section of the DFS algorithm with a discussion on DAGs (directed acyclic graphs), because most implementations of depth-first search will check to see if any cycles exist, and a large part ...

Throughout our exploration of graphs, we’ve focused mostly on representing graphs, and how to search through them. We also learned about edges, the elements that connect the nodes in a graph. In this ...

Last episode, we talked about traversing through a graph with the depth-first search (DFS) algorithm, which helps us determine one (of sometimes many) paths between two nodes in the graph by traversin...

We ended last season by starting our discussion of searching, or traversing, through a graph with breadth-first search (BFS). The breadth-first search algorithm traverses broadly into a structure, by ...

In this episode, we start our discussion of searching, or traversing, through a graph with breadth-first search (BFS). The breadth-first search algorithm traverses broadly into a structure, by visitin...

In this episode, we continue our discussion of representing graphs with adjacency lists -- a hybrid between an edge list and an adjacency matrix, which we learned about last episode! They are also the...

Graphs come from mathematics, and are nothing more than a way to formally represent a network, which is a collection of objects that are all interconnected (this is all stuff you should already know i...

In last episode, we talked about 2-3 trees, where the nodes of every tree contain data in the form of keys, as well as potential child nodes, and can contain more than one key. This takes us to b-tree...

We continue our discussion of tree data structures with 2-3 trees, where the nodes of every tree contain data in the form of keys, as well as potential child nodes. Not only that, but it can contain M...

In this episode, we are looking at a different type of self-balancing tree: red-black trees. By following four very important rules while we paint our tree red and black, we can make it not only self-...

Last episode, we learned about AVL trees, a type of self-balancing binary search tree that follows a golden rule: no single leaf in the tree should have a significantly longer path from the root node ...

When you're dealing with data structures like trees, the balance of its "leaves" (data/nodes) matters. The moment a tree becomes unbalanced, it loses its efficiency, much like a real life tree bending...

In this episode, we continue our talk on Radix Trees and introduce the Practical Algorithm To Retrieve Information Coded In Alphanumeric trees, also known as PATRICIA trees. Yeah, I think we'll just s...

In this episode, join us as we adventure into the safari that is radix trees, where parent nodes eat their offspring nodes as they chomp them down and compress. Don't worry, with all of this new added...

In this episode we continue our talk on pies and tries, and how this data structure is used to power such things as auto-complete! Based on Vaidehi Joshi's blog post, "Trying to Understand Tries"....

In this episode we go through some trie-als and tribulations to retrieve and build words using tries! Based on Vaidehi Joshi's blog post, "Trying to Understand Tries"....

This episode we're diving into radix sort! The word has no relation to Raid, so it is definitely non-toxic and you don't have to bug out. It IS, however, a great integer sorting algorithm, and the fir...

You may have noticed that it's really hard to sort things efficiently. Well, that's where counting sort comes in! Based on Vaidehi Joshi's blog post, "Counting Linearly With Counting Sort"....

We've gotten acquainted with heaps as arrays, now we're diving into heap sort with some help from a few condiments! Based on Vaidehi Joshi's blog post, "Heapify All The Things With Heap Sort"....

So we've talked about heaps, but how do you represent heaps as arrays? And why would you want to? We break it down step by step! Based on Vaidehi Joshi's blog post, "Learning to Love Heaps"....

Now that you've got your heap, what do you do with it? Shrink and grow it of course! We talk about how to add and remove values from a heap with the help of a few cats. Based on Vaidehi Joshi's blog p...

What are heaps? How are they related to binary trees? We use losers, winners, and some cards to help us get to the bottom of heaps! Based on Vaidehi Joshi's blog post, "Learning to Love Heaps"....

How does quicksort perform? And how do variables, like the pivot number, affect it? We walk through three examples to find out! Based on Vaidehi Joshi's blog post, "Pivoting To Understand Quicksort [P...

We learn all about our second "divide and conquer" algorithm, quick sort! We walk through how it works with help from a queendom, a few pointers, and a very helpful pivot number. Based on Vaidehi Josh...

Finally, a sorting algorithm that doesn't suck! We explore how merge sort works and why it performs better than insertion, bubble, and selection sort. Based on Vaidehi Joshi's blog post, "Making Sense...

We dig into how insertion sort works, how we know where to do our inserting, and how this sorting algorithm performs, all with the help of our new boos. Based on Vaidehi Joshi's blog post, "Inching T...

We are super bubbly about bubble sort! We dig into our second sorting algorithm and break down how it works and why it's actually not a great way of sorting things. Based on Vaidehi Joshi's blog post,...

What is selection sort? How does this algorithm work? And just as importantly, how does it perform? We use broken books and cookies to tell you all about it! Based on Vaidehi Joshi's blog post, "Expo...

We're at the end of the season! And to wrap things up, we're breaking down the last two ways to classify sorting algorithms: recursive vs. non-recursive and comparison vs. non-comparison. We bring it ...

Last week, we talked about two ways of classifying sorting algorithms: time complexity and space usage. This episode, we dig into two more! We explore how algorithms can be internal or external, and w...

You probably sort things all the time -- files, clothes, dishes. But have you thought about how to categorize your sorting? How do your sorting algorithms hold up in terms of, say, time complexity? We...

Sets are everywhere! If you've worked with relational databases, made a venn diagram, maybe touched some relational algebra, then you've already worked with sets. We talk about why they're so common, ...

Set theory might sound like a scary, super-math thing, but it's not! Well, it is a math thing, but it doesn't have to be super scary. In fact, if you already know how venn diagrams work, then you basi...

We're back in our hash table classroom with our multiple Brians that need their own tables! But don't you worry, we've got a brand new collision resolution called chaining to help us out. We talk abou...

School is in session, and the teacher is directing students to their assigned seat. Each unique name gets its own unique table. But there's an unexpected student in the class. There's another Brian! W...

We're kicking off a new season with a brand new topic: hash tables! This episode is full of bookshelves, pizza toppings, and helpful fridge operators who are teaming up to give you the most gentle (an...

Let's break down how breadth-first search (BFS) actually works! We'll walk through a real example, explain the Big O notation of this algorithm, and explore how you might decide whether to use breadth...

We're going broad with breadth-first search! Well, actually, we're getting in line, or enqueuing ;) We walk through the steps of how breadth-first search (BFS) works, complete with holiday themed anal...

In our final look at depth-first search (DFS), we explore how to implement this lovely algorithm in coding terms. We also dig into Big O notation, breaking down how to determine the time and space com...

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

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? Vaidehi and Saron break it all down one chocolatey step at a time. Base...