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Introduction to Data Structures

Starting with this chapter, we're going to focus on how we can store and organize data in a way that allows for even better algorithms.

Data structures are just organizational tools that allow for more advanced algorithms. Some examples:

Stacks: Last in, first out.
Queues: First in, first out.
Linked Lists: A chain of nodes, efficient for inserts and deletes.
Binary Trees: A tree where each node has up to two children.
Red Black Trees: A self-balancing binary tree using colors.
Hashmaps: A data structure that maps keys to values.
Tries: A tree used for storing and searching words efficiently.
Graphs: A collection of nodes connected by edges.

Assignment

Implement the count_marketers function. It should accept a list of strings (job titles) and return the number of users who've set their title to "marketer". LockedIn users sometimes use different casing in their titles, so make sure to account for that.

count = count_marketers(["programmer", "marketer", "doctor", "marketer"])
print(count)
# prints "2"

Tip

The .lower() method can be used to convert a string to lowercase.