Comp. Sci. Data Structures and Algorithms Course

Data Structures and Algorithms Course Overview

Table of Contents

• Content
• Standards
• Aspects
  • 👶 Introduction
  • 💪 Workout
  • 🦑 Deep
  • ✨ New
  • 🔮 Obscura

Content

Below you can find the ordered content of the topic, in a linear progression

The linear progression of content aims to cover all content, course by course, workout by workout as follows:

• first course is the only core one, denoted by its manifest
• the next course is denoted by the first item of the next array in each course manifest
• each course has its order of workouts designated by the sections field in the same aforementioned manifest

1. intro-data-structures

Insights:

no	name	content	aspects	standards	PQ	RQ	Quiz	done
1	what-is-a-data-structure	✅	👶 introduction	❌	❌	✅	❌	❌
2	the-array-data-structure	✅	👶 introduction	❌	✅	✅	❌	❌
3	the-linked-list-data-structure	✅	👶 introduction	❌	✅	✅	❌	❌
4	the-stack-data-structure	✅	👶 introduction	❌	✅	✅	❌	❌
5	the-queue-data-structure	✅	👶 introduction	❌	✅	✅	❌	❌

Exercises:

⚠️ There are no insights in this workout.

Game:

⚠️ There is no game in this workout.

2. intro-graphs

Insights:

no	name	content	aspects	standards	PQ	RQ	Quiz	done
1	the-graph-data-structure	✅	👶 introduction 💪 workout	❌	✅	✅	❌	❌
2	the-components-of-a-graph	✅	👶 introduction 💪 workout	❌	✅	❌	❌	❌
3	the-components-of-a-graph-ii	✅	👶 introduction 💪 workout	❌	✅	✅	❌	❌
4	graph-adt	✅	👶 introduction 💪 workout	❌	✅	✅	❌	❌
5	the-tree-data-structure	✅	👶 introduction 💪 workout	❌	✅	✅	❌	❌
6	node-height-and-depth	✅	👶 introduction 💪 workout	❌	✅	✅	❌	❌

Exercises:

⚠️ There are no insights in this workout.

Game:

name	type	aspects	standards	done
what-should-it-be-stored-in	fillTheGap	👶 introduction 💪 workout	❌	❌

3. algorithms-i

Insights:

no	name	content	aspects	standards	PQ	RQ	Quiz	done
1	approximating-memory-and-time-required-by-data-types	✅	🦑 deep 💪 workout	❌	✅	✅	❌	❌
2	approximation-methods	✅	🦑 deep 💪 workout	❌	✅	✅	❌	❌
3	binary-search	✅	🦑 deep 💪 workout	❌	❌	✅	❌	❌
4	sieve-of-eratosthenes	✅	🦑 deep 🔮 obscura	❌	✅	✅	❌	❌
5	primality-test	✅	🦑 deep 💪 workout	❌	✅	✅	❌	❌

Exercises:

⚠️ There are no insights in this workout.

Game:

⚠️ There is no game in this workout.

4. binary-search-tree

Insights:

no	name	content	aspects	standards	PQ	RQ	Quiz	done
1	the-binary-search-tree-data-structure	✅	🦑 deep 💪 workout	❌	✅	✅	❌	❌
2	verifying-a-binary-search-tree	✅	🦑 deep 💪 workout	❌	✅	✅	❌	❌
3	inserting-data-into-a-binary-search-tree	✅	🦑 deep 💪 workout	❌	✅	✅	❌	❌
4	removing-keys-from-a-binary-search-tree	✅	🦑 deep 💪 workout	❌	✅	✅	❌	❌
5	balanced-vs-unbalanced-binary-trees	✅	🦑 deep 💪 workout	❌	❌	✅	❌	❌

Exercises:

⚠️ There are no insights in this workout.

Game:

name	type	aspects	standards	done
oh-that-was-the-answer	tetris	🦑 deep 💪 workout	❌	❌

5. heap-and-trie

Insights:

no	name	content	aspects	standards	PQ	RQ	Quiz	done
1	the-heap-data-structure	✅	🦑 deep 💪 workout	❌	✅	✅	❌	❌
2	inserting-data-into-a-heap-with-the-upheap-operation	✅	🦑 deep 💪 workout	❌	✅	✅	❌	❌
3	removing-data-from-a-heap-with-the-downheap-operation	✅	🦑 deep 💪 workout	❌	✅	✅	❌	❌
4	o-logn-operations-for-heaps	✅	🦑 deep 💪 workout	❌	❌	✅	❌	❌
5	trie-data-structure	✅	🦑 deep 💪 workout	❌	❌	✅	❌	❌

Exercises:

⚠️ There are no insights in this workout.

Game:

⚠️ There is no game in this workout.

6. tree-traversals

Insights:

no	name	content	aspects	standards	PQ	RQ	Quiz	done
1	tree-traversals	✅	🦑 deep 💪 workout	❌	✅	✅	❌	❌
2	depth-first-traversal	✅	🦑 deep 💪 workout	❌	❌	✅	❌	❌
3	pre-order-traversal	✅	🦑 deep 💪 workout	❌	✅	✅	❌	❌
4	in-order-traversal	✅	🦑 deep 💪 workout	❌	✅	✅	✅	❌
5	post-order-traversal	✅	🦑 deep 💪 workout	❌	✅	✅	❌	❌

Exercises:

⚠️ There are no insights in this workout.

Game:

⚠️ There is no game in this workout.

7. algorithms-ii

Insights:

no	name	content	aspects	standards	PQ	RQ	Quiz	done
1	reverse-polish-notation	✅	🦑 deep 💪 workout 🔮 obscura	❌	✅	✅	✅	❌
2	parsing-reverse-polish-notation	✅	🦑 deep 💪 workout 🔮 obscura	❌	✅	✅	❌	❌
3	binary-expression-tree	✅	🦑 deep 🔮 obscura	❌	❌	✅	❌	❌
4	exponentiation-by-squaring	✅	🦑 deep 🔮 obscura	❌	✅	✅	❌	❌
5	levenshtein-distance	✅	🦑 deep 🔮 obscura	❌	✅	✅	❌	❌

Exercises:

⚠️ There are no insights in this workout.

Game:

⚠️ There is no game in this workout.

8. graph-algorithms

Insights:

no	name	content	aspects	standards	PQ	RQ	Quiz	done
1	dijkstras-algorithm	✅	🦑 deep 💪 workout	❌	❌	✅	❌	❌
2	dijkstras-iteration	✅	🦑 deep 💪 workout	❌	❌	✅	❌	❌
3	bellman-ford-algorithm	✅	🦑 deep 💪 workout	❌	❌	✅	✅	❌
4	bellman-ford-iteration	✅	🦑 deep 💪 workout	❌	✅	✅	❌	❌
5	traveling-salesman-problem	✅	🦑 deep 🔮 obscura	❌	✅	✅	❌	❌

Exercises:

⚠️ There are no insights in this workout.

Game:

⚠️ There is no game in this workout.

9. mst-algorithms

Insights:

no	name	content	aspects	standards	PQ	RQ	Quiz	done
1	kruskals-algorithm	✅	🦑 deep 🔮 obscura 💪 workout	❌	❌	✅	❌	❌
2	kruskals-iteration	✅	🦑 deep 🔮 obscura 💪 workout	❌	✅	✅	❌	❌
3	prims-algorithm	✅	🦑 deep 🔮 obscura 💪 workout	❌	❌	✅	✅	❌
4	prims-iteration	✅	🦑 deep 🔮 obscura 💪 workout	❌	✅	✅	❌	❌
5	0-1-knapsack-problem	✅	🦑 deep 🔮 obscura	❌	✅	✅	❌	❌

Exercises:

⚠️ There are no insights in this workout.

Game:

⚠️ There is no game in this workout.

Standards

✅ - At least one insight covers this

❌ - Nothing covers this

🛠️ - This standard has no objectives yet

❌ Identify linear collection data structures and their usage

0. ❌ Identify Linked Lists
1. ❌ Identify a Stack
2. ❌ Identify a Queue
3. ❌ Identify Indexable Ordered Sets (arrays, lists)
4. ❌ Identify a use case for a Linked List
5. ❌ Identify a use case for a Stack
6. ❌ Identify a use case for a Queue
7. ❌ Identify a use case for a Indexable Ordered Set

❌ Identify unordered collection data structures and their usage

0. ❌ Identify a Hash Map
1. ❌ Identify a Set
2. ❌ Identify a use case for a Hash Map
3. ❌ Identify a use case for a Set

❌ Identify graph data structures and their usage

0. ❌ Identify a linked list
1. ❌ Identify an arboreal graph
2. ❌ Identify a binary tree
3. ❌ Identify a red-black tree
4. ❌ Identify a graph
5. ❌ Define directionality, cyclical, connected, complete, and weighted in reference to properties of graphs
6. ❌ Classify diagrams of graphs as having directionality or not, being cyclic or acyclic, connected or not, complete or incomplete, weighted or not

❌ Implement and use linear collection data structures

0. ❌ Implement a Linked List and use it to solve a problem
1. ❌ Implement a Stack and use it to solve a problem
2. ❌ Implement a Queue and use it to solve a problem
3. ❌ Implement a Indexable Ordered Sets (arrays, skip lists, etc) to solve a problem

❌ Implement and use unordered collection data structures

0. ❌ Implement a Hash Map and use it to solve a problem
1. ❌ Implement a Set and use it to solve a problem

❌ Implement and use graph data structures

0. ❌ Implement a Tree
1. ❌ Use a tree appropriately to solve a problem
2. ❌ Implement a binary tree
3. ❌ Use a binary tree appropriately to solve a problem
4. ❌ Implement a red-black tree
5. ❌ Use a red-black tree appropriately to solve a problem
6. ❌ Implement a graph data structure
7. ❌ Write an algorithm to identify if a graph is cyclic
8. ❌ Write an algorithm to identify if a graph is complete
9. ❌ Implement and Use a directed graph appropriately to solve a problem
10. ❌ Implement and Use an undirected graph appropriately to solve a problem
11. ❌ Implement and Use a weighted graph appropriately to solve a problem

❌ Analyze the complexity of common data structure access operations

0. ❌ Accurately describe the complexity of Array access, search, insertion, and deletion
1. ❌ Accurately describe the complexity of Stack access, search, insertion, and deletion
2. ❌ Accurately describe the complexity of Queue access, search, insertion, and deletion
3. ❌ Accurately describe the complexity of Doubly and Singly Linked List access, search, insertion, and deletion, and contrast the differences
4. ❌ Accurately describe the complexity of Hash Table access, search, insertion, and deletion
5. ❌ Accurately describe the complexity of Skip List access, search, insertion, and deletion
6. ❌ Accurately describe the complexity of Binary Search Tree access, search, insertion, and deletion
7. ❌ Accurately describe the complexity of Cartesian Tree access, search, insertion, and deletion
8. ❌ Accurately describe the complexity of B-Tree access, search, insertion, and deletion
9. ❌ Accurately describe the complexity of Red-Black Tree access, search, insertion, and deletion
10. ❌ Accurately describe the complexity of Splay Tree access, search, insertion, and deletion
11. ❌ Accurately describe the complexity of AVL Tree access, search, insertion, and deletion
12. ❌ Accurately describe the complexity of KD Tree access, search, insertion, and deletion

❌ Analyze the complexity of common search and sort algorithms

0. ❌ Analyze and accurately predict the complexity of linear searches
1. ❌ Analyze and accurately predict the complexity of other searching algorithms
2. ❌ Analyze and accurately predict the complexity of sorting algorithms

❌ Analyze the complexity of common pathfinding algorithms

0. ❌ Accurately describe the complexity of Djkistra's
1. ❌ Accurately describe the complexity of A*
2. ❌ Accurately describe the complexity of Breadth-First-Search
3. ❌ Accurately describe the complexity of Depth-First-Search in an arboreal graph

Aspects

Given the insights are tagged with aspects, we can filter over the linear content progression and create learning sub-paths.

These sub-path progressions will most likely not cover all content, but they will ensure and enforce an unified learning experience, tailor for the user wish.

For example, a user might be interested in new additions and updates of a language, rather than introduction lessions. Note that these sub-paths don't take games into consideration

👶 Introduction

If you are being introduced to the topic for the first time

Insights:

1. what-is-a-data-structure
2. the-array-data-structure
3. the-linked-list-data-structure
4. the-stack-data-structure
5. the-queue-data-structure
6. the-graph-data-structure
7. the-components-of-a-graph
8. the-components-of-a-graph-ii
9. graph-adt
10. the-tree-data-structure
11. node-height-and-depth

⚠️ There are no exercises tagged with this aspect in data-structures-and-algorithms. Games:

1. what-should-it-be-stored-in

💪 Workout

Theory put into practice/that’s how you achieve X points

Insights:

1. the-graph-data-structure
2. the-components-of-a-graph
3. the-components-of-a-graph-ii
4. graph-adt
5. the-tree-data-structure
6. node-height-and-depth
7. approximating-memory-and-time-required-by-data-types
8. approximation-methods
9. binary-search
10. primality-test
11. the-binary-search-tree-data-structure
12. verifying-a-binary-search-tree
13. inserting-data-into-a-binary-search-tree
14. removing-keys-from-a-binary-search-tree
15. balanced-vs-unbalanced-binary-trees
16. the-heap-data-structure
17. inserting-data-into-a-heap-with-the-upheap-operation
18. removing-data-from-a-heap-with-the-downheap-operation
19. o-logn-operations-for-heaps
20. trie-data-structure
21. tree-traversals
22. depth-first-traversal
23. pre-order-traversal
24. in-order-traversal
25. post-order-traversal
26. reverse-polish-notation
27. parsing-reverse-polish-notation
28. dijkstras-algorithm
29. dijkstras-iteration
30. bellman-ford-algorithm
31. bellman-ford-iteration
32. kruskals-algorithm
33. kruskals-iteration
34. prims-algorithm
35. prims-iteration

⚠️ There are no exercises tagged with this aspect in data-structures-and-algorithms. Games:

1. what-should-it-be-stored-in
2. oh-that-was-the-answer

🦑 Deep

Prerequisite knowledge consisting of 2 or more 👶/💪 workouts

Insights:

1. approximating-memory-and-time-required-by-data-types
2. approximation-methods
3. binary-search
4. sieve-of-eratosthenes
5. primality-test
6. the-binary-search-tree-data-structure
7. verifying-a-binary-search-tree
8. inserting-data-into-a-binary-search-tree
9. removing-keys-from-a-binary-search-tree
10. balanced-vs-unbalanced-binary-trees
11. the-heap-data-structure
12. inserting-data-into-a-heap-with-the-upheap-operation
13. removing-data-from-a-heap-with-the-downheap-operation
14. o-logn-operations-for-heaps
15. trie-data-structure
16. tree-traversals
17. depth-first-traversal
18. pre-order-traversal
19. in-order-traversal
20. post-order-traversal
21. reverse-polish-notation
22. parsing-reverse-polish-notation
23. binary-expression-tree
24. exponentiation-by-squaring
25. levenshtein-distance
26. dijkstras-algorithm
27. dijkstras-iteration
28. bellman-ford-algorithm
29. bellman-ford-iteration
30. traveling-salesman-problem
31. kruskals-algorithm
32. kruskals-iteration
33. prims-algorithm
34. prims-iteration
35. 0-1-knapsack-problem

⚠️ There are no exercises tagged with this aspect in data-structures-and-algorithms. Games:

1. oh-that-was-the-answer

✨ New

Recently added/gained traction feature

⚠️ There are no insights tagged with this aspect in data-structures-and-algorithms. :warning: There are no exercises tagged with this aspect in data-structures-and-algorithms. :warning: There are no games tagged with this aspect in data-structures-and-algorithms.

🔮 Obscura

Stories, obscure details that don’t specifically relate to a learning objective

Insights:

1. sieve-of-eratosthenes
2. reverse-polish-notation
3. parsing-reverse-polish-notation
4. binary-expression-tree
5. exponentiation-by-squaring
6. levenshtein-distance
7. traveling-salesman-problem
8. kruskals-algorithm
9. kruskals-iteration
10. prims-algorithm
11. prims-iteration
12. 0-1-knapsack-problem

⚠️ There are no exercises tagged with this aspect in data-structures-and-algorithms. :warning: There are no games tagged with this aspect in data-structures-and-algorithms.

✅ All content has been tagged with aspects.