CSE1006

Data Structures and Algorithms

Core Courses

Prerequisites:CSE1001CSE1002

A comprehensive course covering the design, analysis, and implementation of fundamental data structures and algorithms including sorting, searching, graphs, dynamic programming, and NP-completeness. Equips students with the analytical and practical skills to solve complex computational problems efficiently.

34 Chapters

Chapters

Understanding Algorithms and Their Importance in Computing

Sorting Fundamentals and Algorithm Analysis

Ch 3

Asymptotic Analysis and Algorithm Efficiency

17 min

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Ch 4

Breaking Problems into Smaller Pieces

26 min

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Ch 5

Probabilistic Analysis and Randomized Algorithms

9 min

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Ch 6

Understanding Heapsort and Heap Data Structures

9 min

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Ch 7

Quicksort Algorithm and Analysis

14 min

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Ch 9

Finding Order Statistics Efficiently

8 min

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Ch 10

Fundamental Data Organization and Structures

9 min

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Ch 11

Efficient Data Organization with Hash Tables

27 min

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Ch 12

Understanding Binary Search Trees

10 min

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Ch 13

Balanced Search Trees with Red-Black Coloring

10 min

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Ch 14

Solving Optimization Problems with Dynamic Programming

14 min

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Ch 15

Building Optimal Solutions with Greedy Strategies

15 min

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Ch 16

Analyzing Sequence Operation Costs

27 min

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Ch 17

Augmenting Data Structures

11 min

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Ch 18

B-Trees and Disk-Based Indexing

13 min

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Ch 19

Efficient Set Management with Disjoint Structures

10 min

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Ch 20

Graph Search and Structural Analysis

14 min

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Ch 21

Finding Optimal Connection Networks

10 min

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Ch 22

Finding Shortest Paths in Weighted Graphs

10 min

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Ch 23

Computing Shortest Paths Between All Vertices

12 min

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Ch 24

Computing Optimal Resource Distribution in Networks

12 min

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Ch 25

Finding Optimal Pairings in Bipartite Graphs

11 min

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Ch 26

Fundamentals of Parallel Computing

16 min

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Ch 27

Making Decisions Without Knowing the Future

10 min

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Ch 28

Matrix Computations and Linear Systems

9 min

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Ch 29

Understanding Linear Programming and Duality

10 min

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Ch 30

Polynomial Multiplication and Fast Fourier Transforms

10 min

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Ch 31

Algorithms for Computing with Large Integers

16 min

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Ch 32

Efficient Pattern Finding in Text

11 min

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Ch 33

Core Machine-Learning Techniques

11 min

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Ch 34

Understanding NP-Completeness and Hard Problems

11 min

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Ch 35

Finding Near-Optimal Solutions to Hard Problems

23 min

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