book

Predictive Analytics: Data Mining, Machine Learning and Data Science for Practitioners, 2nd Edition

by Dursun Delen

December 2020

Beginner to intermediate

448 pages

11h 21m

English

Pearson FT Press

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What’s in a Name?Why the Sudden Popularity of Analytics and Data Science?The Application Areas of AnalyticsThe Main Challenges of AnalyticsA Longitudinal View of AnalyticsA Simple Taxonomy for AnalyticsThe Cutting Edge of Analytics: IBM WatsonSummaryReferences
What Is Data Mining?What Data Mining Is NotThe Most Common Data Mining ApplicationsWhat Kinds of Patterns Can Data Mining Discover?Popular Data Mining ToolsThe Dark Side of Data Mining: Privacy ConcernsSummaryReferences
The Knowledge Discovery in Databases (KDD) ProcessCross-Industry Standard Process for Data Mining (CRISP-DM)SEMMASEMMA Versus CRISP-DMSix Sigma for Data MiningWhich Methodology Is Best?SummaryReferences
The Nature of Data in Data AnalyticsPreprocessing of Data for AnalyticsData Mining MethodsPredictionClassificationDecision TreesCluster Analysis for Data Miningk-Means Clustering AlgorithmAssociationApriori AlgorithmData Mining and Predictive Analytics Misconceptions and RealitiesSummaryReferences
Naive BayesNearest NeighborSimilarity Measure: The Distance MetricArtificial Neural NetworksSupport Vector MachinesLinear RegressionLogistic RegressionTime-Series ForecastingSummaryReferences
Model EnsemblesBias–Variance Trade-off in Predictive AnalyticsImbalanced Data Problems in Predictive AnalyticsExplainability of Machine Learning Models for Predictive AnalyticsSummaryReferences
Natural Language ProcessingText Mining ApplicationsThe Text Mining ProcessText Mining ToolsTopic ModelingSentiment AnalysisSummaryReferences
Where Does Big Data Come From?The Vs That Define Big DataFundamental Concepts of Big DataThe Business Problems That Big Data Analytics AddressesBig Data TechnologiesData ScientistsBig Data and Stream AnalyticsData Stream MiningSummaryReferences
Introduction to Deep LearningBasics of “Shallow” Neural NetworksElements of an Artificial Neural NetworkDeep Neural NetworksConvolutional Neural NetworksRecurrent Networks and Long Short-Term Memory NetworksComputer Frameworks for Implementation of Deep LearningCognitive ComputingSummaryReferences
Project Constraints: Time and MoneyThe Learning CurveThe KNIME CommunityCorrectness and FlexibilityExtensive Coverage of Data Science TechniquesData Science in the EnterpriseSummary and ConclusionsAcknowledgment