Computer Science
Grade 10
20 min
Data Types
Data Types
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1
Introduction & Learning Objectives
Learning Objectives
Define what an Abstract Data Type (ADT) is and provide two examples.
Differentiate between linear data structures (like arrays) and non-linear data structures (like trees and graphs).
Explain the core components of a Tree data structure, including nodes, edges, root, and leaves.
Describe how a Graph data structure represents relationships using vertices and edges.
Explain the purpose of a Hash Table and how it uses a key-value pair system for fast data retrieval.
Implement a basic Node class, the fundamental building block for many advanced data structures.
How does a social media app instantly find all your friends, or a GPS find the fastest route? 🤔 It's all about organizing data in clever ways!
We've used basic data types like integers and...
2
Key Concepts & Vocabulary
TermDefinitionExample
Abstract Data Type (ADT)A conceptual model for a data type defined by its behavior (the operations you can perform) rather than its implementation. It's a blueprint that separates what a data type does from how it does it.A 'List' is an ADT. We know we can add items, remove items, and get an item at an index. Whether it's implemented using an Array or a Linked List is a separate detail.
Node / VertexThe fundamental building block of trees and graphs. A node is an object that contains data and may contain links or pointers to other nodes.In a family tree, each person is a node. The node would store data like the person's name and have pointers to their children.
TreeA non-linear data structure that simulates a hierarchical structure, with a ro...
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Core Syntax & Patterns
Node Class Definition (OOP Pattern)
class Node {
constructor(data) {
this.data = data;
this.children = []; // or this.left = null, this.right = null for binary trees
}
}
This is the basic blueprint for creating the building blocks of trees and graphs. In Object-Oriented Programming, we define a class to represent a node. It holds the actual data and references (pointers) to other connected nodes.
Key-Value Pair Association
myHashTable[key] = value;
This pattern is central to Hash Tables (often called Dictionaries or Maps in programming languages). You associate a 'value' with a unique 'key'. This allows for near-instantaneous insertion, deletion, and retrieval of data by using the key, rather than searching through the entire collection.
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Challenging
You are asked to model a file system directory (folders containing files and other folders). Which data structure from the tutorial is most appropriate and why?
A.Hash Table, because you can look up any file by its name very quickly.
B.Graph, because files can be linked to each other in a complex network.
C.Tree, because the file system has a clear hierarchical structure of parent folders and child files/folders.
D.An Adjacency List, because it's the most memory-efficient structure taught.
Challenging
The tutorial's expression tree for `(3 + 5) * 2` uses a binary tree. How would you have to adapt the `TreeNode` class to represent a more complex expression like `(2 + 4 + 6) * 5`, where the '+' operator has three operands?
A.The existing `TreeNode` with `left` and `right` properties is sufficient.
B.You would need to chain the nodes, making '4' a child of '2', and '6' a child of '4'.
C.The `TreeNode` class would need to be modified to use a `children` array instead of fixed `left` and `right` properties to accommodate more than two children.
D.You would use a Graph instead of a Tree, with edges pointing from the numbers to the operator.
Challenging
For a very large social network with millions of users, why is an Adjacency List (using a Hash Table) generally more memory-efficient than an Adjacency Matrix?
A.Because hash tables use less memory than 2D arrays by default.
B.Because most users are not friends with most other users (the graph is 'sparse'), so the matrix would be mostly empty (zeros), wasting space.
C.Because an adjacency matrix cannot represent mutual friendships.
D.Because looking up a key in a hash table is faster, which means it uses less memory over time.
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