Lecture 1 Introduction to Data Structures and Algorithms 241 F18 Lecture 1.pdf• “The data structure requires 727 bytes.” • How to express memory requirements in a more meaningful

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Lecture1IntroductiontoDataStructures

andAlgorithms

ECE241–AdvancedProgrammingIFall2018MikeZink

ECE 241 – Data Structures Fall 2018 © 2018 Mike Zink

Overview

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• Introductionofdatastructuresandalgorithms• Informationaboutcourse

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What is an algorithm?

• Well-defined computationalprocedure• Takessetofvaluesasinputandproducessetofvaluesasoutput

• Tooltosolvewelldefinedcomputationalproblem


Algorithm example: Sorting numbers

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• Input: Asequence ofnnumbers:<a1, a2,…, an>• Output:Apermutation(reordering)<a’1,a’2,…,a’n>ofinputsequence suchthata’1 <=a’2 <=… <=a’n

• Input:<31,41,59,26,41,58>Output:<26,31,41,41,58,59>

• Sincesortingisessential, alargenumberofsortingalgorithmsexist

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A Data-centric View of the World

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• Dataareimportantinmanydisciplines• DNAsequences, images,network traffic,sensorreadings,maps,customerprofiles,etc.

• PastECEfocus:processing• Computation atcore:processors, computers,etc.

• NewECEfocus:data• Dataatcore:datacenters,datasecurity,etc.

• Needstructuredapproachtohandlingdata


What kind of problems are solved by algorithms?

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• HumanGenomeProject• Internet:Routing,searches,security• Electroniccommerce• Commercialenterprises• …

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Human Genome Project

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• Completemappingandunderstandingofallthegenesofhumanbeings

• ~20,500humangenes• ~3billionchemicalbasepairsthatmakeuphumanDNA

• Requiresalgorithmstostoreandanalyze


Internet

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• Routingalgorithmsthatfindshortestpathandadapttochanges

• Searchenginesthatfindpageswithparticularinformation

• Encryptedcommunication• Queuemanagement

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Electronic Commerce

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• Adplacements• Recommendations: Netflix,Amazon,etc.• Hashingforauthentication• Electronictrading


Manufacturing and Commercial Enterprises

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• Allocationofscarceresources:• Assigningcrewstoflights• Routesforpackagedeliveries• Storageanddistribution ofgoods

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Why Data Structures?

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• Waytostoreandorganizedata• Facilitateaccessandmodificationofdata• Differentdatastructureshavestrengthsandweaknesses

• Bettersuitedforaspecificalgorithmthanothers


Data Structures and Algorithms

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• Datastructures• Representation andorganizationofdata

• Algorithms• Methods forimplementingoperations ondatastructures

• Datastructuresandalgorithmsarecloselyrelated

• Datastructures areoftentuned forcertainalgorithms

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Big Data

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• Extremely largevolumesofdata• Canbecomputationallyanalyzed

• InternetofThings• Socialmediacontent

• Requiresalgorithmsanddatastructurestoanalyze


Artificial Intelligence

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• Often”trained”onbigdatasets• Imagerecognition• Voicerecognition

• Reliesonalargesetofalgorithm,varyingincomplexity

• Linearregression• Statistics,probability

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Complexity Problems

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• Datasetscanbeverylarge• Operationsmaytakealotoftimeormemory

• Efficientimplementations canmakeabigdifference

• Examplesoflargedatasetsandsizes?


• Example: Million Song Dataset

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• http://labrosa.ee.columbia.edu/millionsong/• Datasetofsongs: title,artist,recordingyears,etc.

• Howwouldyoufigureout• Whichartisthasrecordedmostsongs?

• Whichsonghasbeen coveredthemosttimes?• Whatarethemostcommonwords inatitle?

• Whatarepotentialproblems?

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What will you learn in the course?

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• Howtothinkabout dataandoperations ondata

• Howtodesigndatastructures forefficientuse

• Howtodeterminetheefficiencyofalgorithms• Asetofcommondatastructures andalgorithmsfortypical

operations ondata(e.g.,searching,sorting,etc.)

• Advanced programmingtechniques (e.g.,recursion,etc.)

• Howto implementalargersoftwareprojectwithaprofessional IDE


Python

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• WewillusePython asprogramminglanguage• Datastructuresandalgorithmsareindependentofprogramming

language• Thisisnotaprogrammingcourse

• Wefocusonhigher levelissues• Youshouldalreadyhaveexperience withaprogramming

language(Python,Java,C/C++)• IDEusedinthiscourse:PyCharm

• Professionalenvironment

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PyCharm

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• IDEforPyhton• https://www.jetbrains.com/pycharm/• Veryimportanttobecomefamiliarwithtool!!• Seeintroslides forPyCharm (moreduringthisweek’sdiscussion)


Course Organization

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• Coursewebsite:http://www.ecs.umass.edu/ece241/

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Course Events

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• Eventstoattend• Lectures• Discussions

• Assignments• Homework• Projects

• Exams• 2examsduringsemester• 1finalexam


Course Policy

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• Readthesyllabus initsentirety!• Understandexpectations forthiscourse

• Deadlinesarepostedonwebsite• Noextensions forhomeworkorprojects

• Assignments andgradesmanagedthroughMoodle

• Nosharingofcode• Automatedcheckingofsubmissions

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Office Hours

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• TA’s:• Wednesdays4PM- 5:30PM• Fridays3PM- 4PM

• Prof.Zink:• Fridays10- 11PM


Properties of Data Structures and Algorithms

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Quality Measures for Data Structures

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• Whatdoyouexpectfromagooddatastructure?


Quality Metrics for Algorithms

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• Whatdoyouexpectfroma“good”algorithm?

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Correctness

• Insomecasesabsolute• E.g.,what isthesumofallvalues inarray?

• Somealgorithmshavemultiplesolutions• Examples?

• Somealgorithmsmayhavesolutionswherecorrectness ishardtodefine• Examples?

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Multiple solutions

• Datasetfornumberofwinsbypitcher(2011):• {(Halladay,21),(Jiminez,19),(Lester,19),(Price,19),(Sabathia,21),(Wainwright,20)}

• Whichpitcherhasmostwins?27

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Unclear “correctness”

• Makeimagesquare:

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?

Running time performance

• “Thealgorithmfinishes in12seconds.”• Howtoexpressperformancemoreaccurately?

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Memory use performance

• “Thedatastructurerequires727bytes.”• Howtoexpressmemoryrequirements inamoremeaningfulway?

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It all depends…

• Qualityofdatastructuredependsonoperation• Example:unsorted “pile”ofpapers

• Veryfastforaddingitem• Notveryfastforfindingitem

• Example:paperssortedby lastname• Slowerforaddingitem• Fasterforfindingitem(ifsearchisbylastname)• However:alsoslowifsearchisbyfirstname

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Data types

• Whataretypicaldatatypestostoreinformation?

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Data types

• Commontypes• Numerictypes: integer,floatingpoint• Boolean• Texttypes: character,string• Composite types• References(“pointer”)

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Algorithms and data types• Manyalgorithmscanbeusedwithdifferentdatatypes

• Sortingofintegers• Sortingofstrings• Sortingofcompositetypes

• Mayrequiresmalladaptation incode• Differentcomparisonfunctions• Differentcodefor“movingdataaround”

• Whyisthispoint important?• Idonotwantyoutoleavethiscoursesaying:“Useless.Weonlylearnedhowtosortnumbers!”

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Objects• Foralgorithmdiscussion,wecanthinkof“objects”inaverygeneralway

• Operationsonobjects• Creation(anddestruction)• Comparison

• Operationsonobjects indatastructures• Insertion• Deletion• Algorithm-specificoperations: search,sort,etc. 35

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Next Steps

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• Next lecture“AsymptoticNotation andMergeSort”onThursday

• Firstdiscussion alsoonThursday• Willpost firstHWonThursday

ECE 241 – Data Structures Fall 2018 © 2018 Mike Zink37

Documents

Lecture 1 Introduction to Data Structures and Algorithms 241 F18 Lecture 1.pdf• “The data structure requires 727 bytes.” • How to express memory requirements in a more meaningful