数据科学原理（影印版）
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作者思南？约茨德米尔
出版社东南大学出版社
ISBN9787564173647
出版时间2017-10
装帧其他
开本16开
纸张胶版纸
定价92元
货号1243047
上书时间2024-07-13
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【书 名】 数据科学原理（影印版）
【书 号】 9787564173647
【出 版 社】 东南大学出版社
【作 者】 思南？约茨德米尔
【出版日期】 2017-10-01
【版 次】 1
【开 本】 16开
【定 价】 92.00元
【内容简介】
本书旨在帮助你将数学、编程和商业分析这三者融会贯通。有了这本书，在面对复杂的问题时，无论是抽象和原始的数据统计，还是可实施的理念，你都会充满自信。我们采用了一种独特的方法来建立起数学和计算机科学之间的桥梁，你会在这次令人兴奋的学习之旅中成长为一名数据科学家。从清洗和准备数据开始，然后到给出有效的数据挖掘策略和技术，你会经历数据科学的整个流程，建立起数据科学的各个组成部分是如何相互协作的宏观概念，学习基本的数学和统计学知识以及一些目前由数据科学家和分析师用到的伪代码。除此之外，你还将掌握机器学习，了解一些有用的统计模型，这些模型能够帮助你控制和处理很密集的数据集，学会如何创建出能股表达数据意图的可视化方法。
【目录】
Preface
Chapter1：HowtoSoundLikeaDataScientist
Whatisdatascience?
Basicterminology
Whydatascience?
Example-SigmaTechnologies
ThedatascienceVenndiagram
Themath
Example-spawner-recruitmodels
Computerprogramming
WhyPython?
Pythonpractices
ExampleofbasicPython
Domainknowledge
Somemoreterminology
Datasciencecasestudies
Casestudy-automatinggovernmentpaperpushing
Fireallhumans，right?
Casestudy-marketingdollars
Casestudy-what'sinajobdescription?
Summary
Chapter2：TypesofData
Flavorsofdata
Whylookatthesedistinctions?
Structuredversusunstructureddata
Exampleofdatapreprocessing
Word/phrasecounts
Presenceofcertainspecharacters
Relativelengthoftext
Pickingouttopics
Quantitativeversusqualitativedata
Example-coffeeshopdata
Example-worldalcoholconsumptiondata
Diggingdeeper
Theroadthusfar
Thefourlevelsofdata
Thenominallevel
Mathematicaloperationsallowed
Measuresofcenter
Whatdataislikeatthenominallevel
Theordinallevel
Examples
Mathematicaloperationsallowed
Measuresofcenter
Quickrecapandcheck
Theintervallevel
Example
Mathematicaloperationsallowed
Measuresofcenter
Measuresofvariation
Theratiolevel
Examples
Measuresofcenter
Problemswiththeratiolevel
Dataisintheeyeofthebeholder
Summary
Chapter3：TheFiveStepsofDataScience
IntroductiontoDataScience
Overviewofthefivesteps
Askaninterestingquestion
Obtainthedata
Explorethedata
Modelthedata
Communicateandvisualizetheresults
Explorethedata
Basicquestionsfordataexploration
Dataset1-Yelp
Dataframes
Series
Explorationtipsforqualitativedata
Dataset2-titanic
Summary
Chapter4：BasicMathematics
Mathematicsasadiscipline
Basicsymbolsandterminology
Vectorsandmatrices
Quickexercises
Answers
Arithmeticsymbols
Summation
Proportional
Dotproduct
Graphs
Logarithms/exponents
Settheory
Linearalgebra
Matrixmultiplication
Howtomultiplymatrices
Summary
Chapter5：ImpossibleorImprobable-AGentleIntroductiontoProbability
Basicdefinitions
Probability
BayesianversusFrequentist
Frequentistapproach
Thelawoflargenumbers
Compoundevents
Conditionalprobability
Therulesofprobability
Theadditionrule
Mutualexclusivity
Themultiplicationrule
Independence
Complementaryevents
Abitdeeper
Summary
Chapter6：AdvancedProbability
Collectivelyexhaustiveevents
Bayesianideasrevisited
Bayestheorem
MoreapplicationsofBayestheorem
Example-Titanic
Example-medicalstudies
Randomvariables
Discreterandomvariables
Typesofdiscreterandomvariables
Summary
Chapter7：BasicStatistics
Whatarestatistics?
Howdoweobtainandsampledata?
Obtainingdata
Observational
Experimental
Samplingdata
Probabilitysampling
Randomsampling
Unequalprobabilitysampling
Howdowemeasurestatistics?
Measuresofcenter
Measuresofvariation
Definition
Example-employeesalaries
Measuresofrelativestanding
Theinsightfulpart-correlationsindata
TheEmpiricalrule
Summary
Chapter8：AdvancedStatistics
Pointestimates
Samplingdistributions
Confidenceintervals
Hypothesistests
Conductingahypothesistest
Onesamplet-tests
Exampleofaonesamplet-tests
Assumptionsoftheonesamplet-tests
TypeIandtypeIIerrors
Hypothesistestforcategoricalvariables
Chi-squaregoodnessoffittest
Chi-squaretestforassociation/independence
Summary
Chapter9：CommunicatingData
Whydoescommunicationmatter?
Identifyingeffectiveandineffectivevisualizations
Scatterplots
Linegraphs
Barcharts
Histograms
Boxplots
Whengraphsandstatisticslie
Correlationversuscausation
Simpson'sparadox
Ifcorrelationdoesn'timplycausation，thenwhatdoes?
Verbalcommunication
It'sabouttellingastory
Onthemoreformalsideofthings
Thewhylhowlwhatstrategyofpresenting
Summary
Chapter10：HowtoTellIfYourToasterIsLearning-MachineLearningEssentials
Whatismachinelearning?
Machinelearningisn'tperfect
Howdoesmachinelearningwork?
Typesofmachinelearning
Supervisedlearning
It'snotonlyaboutpredictions
Typesofsupervisedlearning
Dataisintheeyesofthebeholder
Unsupervisedlearning
Reinforcementlearning
Overviewofthetypesofmachinelearning
Howdoesstatisticalmodelingfitintoallofthis?
Linearregression
Addingmorepredictors
Regressionmetrics
Logisticregression
Probability，odds，andlogodds
Themathoflogisticregression
Dummyvariables
Summary
Chapter11：PredictionsDon'tGrowonTrees-orDoThey?
Na'fveBayesclassification
Decisiontrees
Howdoesacomputerbuildaregressiontree?
Howdoesacomputerfitaclassificationtree?
Unsupervisedlearning
Whentouseunsupervisedlearning
K-meansclustering
Illustrativeexample-datapoints
Illustrativeexample-beer！
ChoosinganoptimalnumberforKandclustervalidation
TheSilhouetteCoefficient
Featureextractionandprincipalcomponentanalysis
Summary
Chapter12：BeyondtheEssentials
Thebiasvariancetradeoff
Errorduetobias
Errorduetovariance
Twoextremecasesofbias/variancetradeoff
Underfitting
Overfitting
Howbias/varianceplayintoerrorfunctions
Kfoldscross-validation
Gridsearching
Visualizingtrainingerrorversuscross-validationerror
Ensemblingtechniques
Randomforests
ComparingRandomforestswithdecisiontrees
Neuralnetworks
Basicstructure
Summary
Chapter13：CaseStudies
Casestudy1-predictingstockpricesbasedonsomedia
Textsentimentanalysis
Exploratorydataanalysis
Regressionroute
Classificationroute
Goingbeyondwiththisexample
Casestudy2-whydosomepeoplecheatontheirspouses?
Casestudy3-usingtensorflow
Tensorflowandneuralnetworks
Summary
Index
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