Data Structures: An Active Learning Approach

Name: Data Structures: An Active Learning Approach
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Author: The University of California, San Diego

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English

60 h

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Self-paced
Free Access
Fee-based Certificate

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10 Sequences
Intermediate Level

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Course details

Syllabus

Module 1: Introduction and Review

1.1 Welcome to Data Structures!
1.2 Tick Tock, Tick Tock
1.3 Classes of Computational Complexity
1.4 The Fuss of C++
1.5 Random Numbers
1.6 Bit-by-Bit
1.7 The Terminal-ator
1.8 Git: the "Undo" Button of Software Development

Module 2: Introductory Data Structures

2.1 Array Lists
2.2 Linked Lists
2.3 Skip Lists
2.4 Circular Arrays
2.5 Abstract Data Types
2.6 Deques
2.7 Queues
2.8 Stacks
2.9 And the Iterators Gonna Iterate-ate-ate

Module 3: Tree Structures

3.1 Lost in a Forest of Trees
3.2 Heaps
3.3 Binary Search Trees
3.4 BST Average-Case Time Complexity
3.5 Randomized Search Trees
3.6 AVL Trees
3.7 Red-Black Trees
3.8 B- Trees
3.9 B+ Trees

Module 4: Introduction to Graphs

4.1 Introduction to Graphs
4.2 Graph Representations
4.3 Algorithms on Graphs: Breadth-First Search
4.4 Algorithms on Graphs: Depth-First Search
4.5 Dijkstra's Algorithm
4.6 Minimum Spanning Trees: Prim's and Kruskal's Algorithms
4.7 Disjoint Sets

Module 5: Hashing

5.1 The Unquenched Need for Speed
5.2 Hash Functions
5.3 Introduction to Hash Tables
5.4 Probability of Collisions
5.5 Collision Resolution: Open Addressing
5.6 Collision Resolution: Closed Addressing (Separate Chaining)
5.7 Collision Resolution: Cuckoo Hashing
5.8 Hash Maps

Module 6: Implementing a Lexicon

6.1 Creating a Lexicon
6.2 Using Linked Lists
6.3 Using Arrays
6.4 Using Binary Search Trees
6.5 Using Hash Tables and Hash Maps
6.6 Using Multiway Tries
6.7 Using Ternary Search Trees

Module 7: Coding and Information Compression

7.1 Return of the (Coding) Trees
7.2 Entropy and Information Theory
7.3 Honey, I Shrunk the File
7.4 Bitwise I/O

Module 8: Conclusions

8.1 Summaries of Data Structures

Prerequisite

Reading and understanding pseudocode
Performing time-complexity analysis using Big-O notation
Working with basic probabilities
Following formal mathematical proofs
Programming in either C++ or Python

Instructors

Niema Moshiri
Ph. D. Student
The University of California, San Diego

Christine Alvarado
Associate Teaching Professor, Computer Science and Engineering
The University of California, San Diego

Liz Izhikevich
M.S. Student, Computer Science and Engineering
The University of California, San Diego

Editor

The University of California, San Diego

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