Data Structures and Algorithms Overview

This course focuses on data structures and algorithms for manipulating data.
Data structures for storing information (tables, lists, stacks, queues, trees, graphs) are covered.
Basic algorithms for creating, manipulating, and using these structures are discussed.
Different searching and sorting techniques are also introduced and compared.
Programming assignments emphasize data organization and manipulation.

Students gain familiarity with implementing elementary data structures and associated algorithms.
Students learn to implement more complex data structures and associated algorithms.
Students learn advanced sorting techniques.
Students learn to determine the time complexity of algorithms.
Students learn algorithm design techniques.

Students can understand and implement lists, stacks, queues, search trees, heaps, union-find ADTs, and graphs in programs.
Students can prove properties of trees and graphs.
Students can perform breadth-first and depth-first searches on directed and undirected graphs.
Students can use advanced sorting techniques (heapsort, mergesort, quicksort).

Basic data structures: lists, stacks, queues
Analysis of Algorithms: time complexity, upper and lower bounds
Sorting: insertion sort, merge sort, quick sort, heap sort, performance bounds
Dictionaries: binary search trees, AVL trees, B-trees
Graphs: definitions, representation, modeling tools, breadth-first search, depth-first search, directed graphs, topological sorts
Hashing: hash tables, functions, collision resolution

Basic data structures: review exercises
Time complexity: exercises in estimating time complexity of algorithms
Performance bounds: exercises in estimating performance bounds of algorithms
Sorting I: exercises in using and analyzing sorting algorithms (merge sort, quick sort)
Sorting II: additional exercises in sorting, including heap sort
Dictionaries I: exercises in using dictionaries as abstract data types (binary search trees, AVL trees)
Dictionaries II: additional exercises in using dictionaries including B-trees
Breadth-first search: exercises in using the breadth-first search algorithm
Depth-first search: exercises in using the depth-first search algorithm
Topological sort: exercises in using topological sort algorithms
Hashing I: exercises in implementing hash tables and functions
Hashing II: exercises in implementing collision resolution

Data Structures in Computer Science : design, analysis, and implementation of algorithms.
Data structures are connected to the design and implementation of efficient algorithms.
Data structure deals with methods, techniques, and tools to organize data in memory.

Data is a plural of datum (something given).
Data is facts and statistics used for reference or analysis, or the quantities, characters, or symbols operated by a computer.

Data Structure is a mathematical or logical model of organizing data items into computer memory efficiently.
Organizing data, accessing methods, degrees of associativity between data items, and processing alternatives for data are key aspects.
Programs are a set of instructions that perform calculations or algorithms.
Data structures affect program design.

Primitive data structures: manipulated directly by machine instructions (int, float, char, pointer).
Non-primitive data structures: not manipulated directly by machine instructions (arrays, structures, stacks, queues, linked lists).

Linear data structures: data items arranged in a sequence in memory (arrays, stacks, queues, linked lists).
Sequential structures: data items stored in consecutive memory locations.
Linked structures: data items stored in nodes linked together.