[PDF] Data Structures Lecture 1: Introduction - Everything Computer Science PDF

Introduction to Data Structures and Algorithms

Introduction to Data Structures and Algorithms. 1.0 Objective. 1.1 Introduction: 1.2 • https://edutechlearners.com/data-structures-with-c-by-schaum-series-pdf ...

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You should find it useful for a variety of courses from an undergraduate course in data structures up through a graduate course in algorithms. PDF files for ...

Data Structures and Algorithms

To fully understand data structures and algorithms you will almost certainly need to comple- ment the introductory material in these notes with textbooks or

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only the elements stored but also their relationship to each other. Ms G Jayabharathi. Page 3. Introduction. ▫ Data structure affects the

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Determine which algorithm or data structure to use in different scenarios. 4. To improve the logical ability. UNIT-I INTRODUCTION TO ALGORITHMS AND DATA.

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for modelling the persistent identity of a persistent data structure. Page 18. 6. Introduction. The second part of the thesis (Chapters 5–8) concerns the

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19 Jan 2010 Page 1. A Practical Introduction to. Data Structures and Algorithm. Analysis. Third Edition (Java Version). Clifford A. Shaffer. Department of ...

Notes 2: Introduction to data structures - 2.1 Recursion

A binary tree is a tree data structure in which each node has at most two. Notes 2: Introduction to data structures. Page 5. MA934 Numerical Methods. Notes 2.

Introduction to Data Structures and Algorithms

Efficiency: Data structure also needs to be efficient. It should process the data at high speed without utilizing much of the computer resources such as memory

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To fully understand data structures and algorithms you will almost certainly need to comple- ment the introductory material in these notes with textbooks or

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Implementation of Stack and Queue using linked list. UNIT-IV SORTING AND SEARCHING. Classes:12. Sorting: Introduction Selection sort

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Demonstrate various tree and graph traversal algorithms. V. Analyze and choose appropriate data structure to solve problems in real world. UNIT - 1 INTRODUCTION

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Array Stacks

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Introduction to. Data Structures Main operations on these data structures are ... i.e. a certain data structure is a stack if the respective axioms hold.

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Introduction to. Data Structure A Data structure is a way of organizing all data items ... Primitive Data Structure :- The data structure that are.

A Practical Introduction to Data Structures and Algorithm Analysis

03-Jan-2011 tial introduction to data structures. Readers of this book should have programming experience typically two semesters or the equivalent of ...

[PDF] module 1: introduction data structures

1 MODULE 1: INTRODUCTION DATA STRUCTURES A data structure is a specialized format for organizing and storing data General data

[PDF] Introduction to Data Structures and Algorithms - Rutgers

Chapter: Elementary Data Structures(1) Overview on simple data structures i e a certain data structure is a stack if the respective axioms hold

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1 5 Overview These notes will cover the principal fundamental data structures and algorithms used in computer science and bring together a broad range of

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Introduction to Data Structure Introduction to Data Structure Computer is an electronic machine which is used for data processing and manipulation

[PDF] Introduction to Data Structures

Definition ? Data structure is representation of the logical relationship existing between individual elements of data ? In other words a data

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Determine which algorithm or data structure to use in different scenarios 4 To improve the logical ability UNIT-I INTRODUCTION TO ALGORITHMS AND DATA

[PDF] Data Structures Lecture 1: Introduction - Everything Computer Science

What is a Data Structure Anyway? •It's an agreement about: • how to store a collection of objects in memory • what operations we can perform on that data

[PDF] Basics of Data Structures Introduction to Data Structures

Data Structure is an arrangement of data in a computer's memory (or sometimes on a disk) Data structures include arrays linked lists stacks binary trees

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3 jan 2011 · many problems some data structure in the toolkit provides a good solution The second goal is to introduce the idea of tradeoffs and

[PDF] DATA STRUCTURES USING “C” - CET

Module-1 Lecture-01 Introduction to Data structures In computer terms a data structure is a Specific way to store and organize data in a

What is the basic introduction of data structures?
A data structure is a specialized format for organizing, processing, retrieving and storing data. There are several basic and advanced types of data structures, all designed to arrange data to suit a specific purpose. Data structures make it easy for users to access and work with the data they need in appropriate ways.
What are the 4 data structures?
The four basic data structure types are linear data structures, tree data structures, hash data structures and graph data structures.
How do I teach myself data structures?
How to start with data structures and algorithms?
1Look out for the best resources to learn the basics.2Start implementing each data structure.3Understand the internal workings of each data structure.4Practice easy, medium and hard questions.5Notice the patterns in problems and isolate the standard codes.
We define an algorithm to be the set of programs that implement or express that algorithm. The set of all programs is partitioned into equivalence classes. Two programs are equivalent if they are essentially the same program.

CMSC 420

Data Structures

Lecture 1: Introduction

Digital Data

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MoviesMusicPhotosMapsProtein

Shapes

DNA

RAM = Symbols + Pointers

16-bit words02468101214

ASCII table:

agreement for the meaning of bits

We may agree to

interpret bits as an address (pointer)

Physically, RAM is

a random accessible array of bits. => We can store and manipulate arbitrary symbols (like letters) and associations between them. (for our purposes)

Digital Data Must Be ...

Encoded (e.g. 01001001 <-> )

Arranged

Stored in an orderly way in memory / disk

Accessed

Insert new data

Remove old data

Find data matching some condition

Processed

Algorithms: shortest path, minimum cut, FFT, ...

The focus of

this class

Data Structures -> Data StructurING

How do we organize information so that we can find, update, add, and delete portions of it efficiently?

Niklaus Wirth,

designer of Pascal

Data Structure Example Applications

1.How does Google quickly find web pages that contain a

search term?

2.What's the fastest way to broadcast a message to a network

of computers?

3.How can a subsequence of DNA be quickly found within

the genome?

4.How does your operating system track which memory

(disk or RAM) is free?

5.In the game Half-Life, how can the computer determine

which parts of the scene are visible?

What is a Data Structure Anyway?

It's an agreement about:

how to store a collection of objects in memory, what operations we can perform on that data, the algorithms for those operations, and how time and space efficient those algorithms are.

Ex. vector in C++:

Stores objects sequentially in memory

Can access, change, insert or delete objects

Algorithms for insert & delete will shift items as needed Space: O(n), Access/change = O(1), Insert/delete = O(n)

Abstract Data Types (ADT)

Data storage & operations encapsulated by an ADT.

ADT specifies permitted operations as well as time and space guarantees.

User unconcerned with how it's implemented

(but we are concerned with implementation in this class).

ADT is a concept or convention:

not something that directly appears in your code programming language may provide support for communicating ADT to users (e.g. classes in Java & C++) insert() delete() find_min() find() int main() {

D = new Dictionary()

D.insert(3,10);

cout << D.find(3); class Dictionary {

Dictionary();

void insert(int x, int y); void delete(int x);

Dictionary ADT

Most basic and most useful ADT:

insert(key, value) delete(key, value) value = find(key)

Many languages have it built in:

Insert, delete, find each either O(log n) [C++] or expected constant [perl, python]

Any guesses how dictionaries are implemented?

awk:

D["AAPL"] = 130

# associative array perl: my %D; $D["AAPL"] = 130; # hash python:

D = {}; D["AAPL"] = 130

# dictionary C++: map D = new map();

D["AAPL"] = 130;

// map

C++ STL

Data structures = "containers"

Interface specifies both operations & time guarantees

ContainerElement AccessInsert / DeleteIterator PatternsvectorconstO(n)RandomlistO(n)constBidirectionalstackconst (limited)O(n)Frontqueueconst (limited)O(n)Front, Backdequeconst

O(n), const @ ends

RandommapO(log n)O(log n)BidirectionalsetO(log n)O(log n)BidirectionalstringconstO(n)BidirectionalarrayconstO(n)RandomvalarrayconstO(n)RandombitsetconstO(n)Random

Some STL Operations

Select operations to be orthogonal: they don't significantly duplicate each other's functionality.

Choose operations to be useful building blocks.

push_back find insert erase size begin, end (iterators) operator[] front back for_each find_if count copy reverse sort set_union min max

E.g. Data Structure

Operations

E.g. Algorithms

Iterators, Sequences

Suppose You're Google Maps...

You want to store data about cities (location, elevation, population)...What kind of operations should your data structure(s) support?

Operations to support the following scenarios...

Finding addresses on map?

Lookup city by name...

Mobile iPhone user?

Find nearest point to me...

Car GPS system?

Calculate shortest-path between

cities...

Show cities within a given

window...

Political revolution?

Insert, delete, rename cities

Data Organizing Principles

Ordering:

Put keys into some order so that we know something about where each key is are relative to the other keys. Phone books are easier to search because they are alphabetized.

Linking:

Add pointers to each record so that we can find related records quickly. E.g. The index in the back of book provides links from words to the pages on which they appear.

Partitioning:

Divide the records into 2 or more groups, each group sharing a particular property.

E.g. Multi-volume encyclopedias (Aa-Be, W-Z)

E.g. Folders on your hard drive

Ordering

Pheasant,

Grouse,

Quail,

Pelican,

Partridge,

32
Duck, 18

Woodpecker,

Robin,

Cardinal,

102

Eagle,

Chicken,

Pigeon,

201
Swan, 57
Loon, 213

Turkey,

Albatross,

Ptarmigan,

Finch,

Bluejay,

Heron,

Egret,

Goose,

Albatross,

Bluejay,

Cardinal,

102

Chicken,

7 Duck, 18

Eagle,

Egret,

Finch,

Goose,

Grouse,

Heron,

70
Loon, 213

Partridge,

Pelican,

Pheasant,

Pigeon,

201

Ptarmigan,

Quail,

Robin,

89
Swan, 57

Turkey,

Woodpecker,

Sequential Search - O(n)(1)(2)(3)(4)Search for

"Goose"

Every step discards

half the remaining entries: n/2 k = 1 2 k = n k = log n

Binary Search

O(log n)

Big-O notation

O() notation focuses on the largest term and ignores constants Largest term will dominate eventually for large enough n. Constants depend on "irrelevant" things like machine speed, architecture, etc. Definition: T(n) is O(f(n)) if the limit of T(n) / f(n), is a constant as n goes to infinity.

Example:

Suppose T(n) = 12n

2 + n + 2 log n.

Consider f(n) = n

2 Then lim [T(n) / f(n)] = lim [12 + (1/n) + (2 log n) / n 2 ] = 12

Example:

Is T(n) = n log n in O(n)?

Check: lim [(n log n) / n ] = lim log n = infinity! So no!

Alternative Definition of Big-O

T(n) is in O(f(n)) if there are some constants n

0 and c such that

T(n) < c f(n) for all n ≥ n

0 In other words, once the input size n gets big enough (bigger than n 0 then T(n) is always less than some constant multiple of f(n). (c allows us to shift f(n) up by a constant amount to account for machine speed, etc.) [Introduced in 1894 by Paul Bachmann, popularized by Don Knuth.] Typically, in this class, we'll want things to be sub-linear: we don't want to look at every data item.

Big-O Taxonomy

Growth RateNameNotesO(1)constantBest, independent of input sizeO(log log n)very fastO(log n)logarithmicoften for tree-based data structuresO(log

k n)polylogarithmicO(n p 2 )quadraticOk if n is small enough;O(n k )polynomialTractableO(2 n ), O(n!)exponential, factorialbad

0255075100125150175200225

2.5!10

4 5!10 4

7.5!10

4 1!10 5

1.25!10

Big-O Examples

y = 3000y = 50ny = 2n 2 n

Linking

Records located any where in memory

Green pointers give "next" element

Red pointers give "previous" element

Insertion & deletion easy if you have a pointer to the middle of the list

Don't have to know size of data at start

Pointers let us express relationships between pieces of information.

224437897

Partitioning

Ordering implicitly gives a partitioning based on the "<" relation. Partitioning usually combined with linking to point to the two halves.

Prototypical example is the Binary Search Tree:

9 18 3598

31195816

Find 18All keys in the left subtree are < the root

All keys in the right subtree are

the root

Where's the FBI?

J. Edgar Hoover Building

935 Pennsylvania Avenue, NW

Washington, DC, 20535-0001

NWNESESW

Why is the DC partitioning bad?

Everything interesting is in the northwest quadrant.

Want a balanced partition!

Another example: an unbalanced binary search tree: (becomes sequential search)

Much of the first part of this class will be

techniques for guaranteeing balance of some form.quotesdbs_dbs20.pdfusesText_26

[PDF] [PDF] Data Structures Lecture 1: Introduction - Everything Computer Science

What is the basic introduction of data structures?

What are the 4 data structures?

How do I teach myself data structures?

How to start with data structures and algorithms?

CMSC 420

Data Structures

Lecture 1: Introduction

Digital Data

MoviesMusicPhotosMapsProtein

Shapes

RAM = Symbols + Pointers

16-bit words02468101214

ASCII table:

We may agree to

Physically, RAM is

Digital Data Must Be ...

Encoded (e.g. 01001001 <-> )

Arranged

Stored in an orderly way in memory / disk

Accessed

Insert new data

Remove old data

Find data matching some condition

Processed

Algorithms: shortest path, minimum cut, FFT, ...

The focus of

Data Structures -> Data StructurING

Niklaus Wirth,

Data Structure Example Applications

1.How does Google quickly find web pages that contain a

2.What's the fastest way to broadcast a message to a network

3.How can a subsequence of DNA be quickly found within

4.How does your operating system track which memory

5.In the game Half-Life, how can the computer determine

What is a Data Structure Anyway?

It's an agreement about:

Ex. vector in C++:

Stores objects sequentially in memory

Can access, change, insert or delete objects

Abstract Data Types (ADT)

Data storage & operations encapsulated by an ADT.

User unconcerned with how it's implemented

ADT is a concept or convention:

D = new Dictionary()

D.insert(3,10);

Dictionary();

Dictionary ADT

Most basic and most useful ADT:

Many languages have it built in:

Any guesses how dictionaries are implemented?

D["AAPL"] = 130

D = {}; D["AAPL"] = 130

D["AAPL"] = 130;

C++ STL

Data structures = "containers"

O(n), const @ ends

Some STL Operations

Choose operations to be useful building blocks.

E.g. Data Structure

Operations

E.g. Algorithms

Iterators, Sequences

Suppose You're Google Maps...

Operations to support the following scenarios...

Finding addresses on map?

Lookup city by name...

Mobile iPhone user?

Find nearest point to me...

Car GPS system?

Calculate shortest-path between

Show cities within a given

Political revolution?

Insert, delete, rename cities

Data Organizing Principles

Ordering:

Linking:

Partitioning:

E.g. Multi-volume encyclopedias (Aa-Be, W-Z)

E.g. Folders on your hard drive