Data Science with R A Step By Step Guide With Visual Illustrations & Examples


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Posted on 2018-12-04, by voska89.


Data Science with R: A Step By Step Guide With Visual Illustrations & Examples by Andrew Oleksy
English | November 16, 2018 | ISBN: N/A | ASIN: B07KNBFGFZ | 276 pages | AZW3 | 4.46 MB

A Step By Step Guide with Visual Illustrations and Examples
The Data Science field is expected to continue growing rapidly over the next several years and Data Scientist is consistently rated as a top career.Data Science with R gives you the necessery theoretical background to start your Data Science journey and shows you how to apply the R programming language through practical examples in order to extract valuable knowledge from data. Professor Andrew Oleksy guides you through all important concepts of data science including the R programming language, Data Mining, Clustering, Classification and Prediction, Hadoop framework and more.
Table of Contents:

Introduction to Data Mining Data ScienceKnowledge Discovery in Databases (KDD)Model TypesExamples and CounterexamplesClassification of Data Mining methodsApplicationsChallengesThe R Programming LanguageBasic Concepts, Definitions and NotationsTool InstallationIntroduction to R Data TypesBasic TasksControl StructuresFunctionsScoping RulesIterated FunctionsHelp from the console and Package InstallationTypes, Quality and Data Preprocessing Categories and Types of VariablesPreprocessing processesdplyr and tidyr packagesSummary Statistics and Visualization Measures of PositionMeasures of DispersionVisualization of Qualitative DataVisualization of Quantitative DataClassification and Prediction ClassificationPredictionOverfitting and RegularizationClustering Unsupervised LearningConcept of ClusterK-means algorithmHierarchical Clustering AlgorithmsDBSCAN AlgorithmMining of Frequent Itemsets and Association Rules IntroductionTheoretical BackgroundApriori AlgorithmFrequent Itemsets TypesPositive and Negative Border of Frequent ItemsetsAssociation Rules MiningAlternative Methods for Large Itemsets generationFP-Growth AlgorithmArules PackageComputational Methods for Big Data Analysis (Hadoop and MapReduce) IntroductionAdvantages of Hadoop's Distributed File SystemHadoop UsersHadoop ArchitectureThe Hadoop Cluster ArchitectureHadoop Java APIList Loops & Generic Classes and Methods

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