book

It's All Analytics!

Name: It's All Analytics!
ISBN: 9781000067224

by Scott Burk, Gary D. Miner

May 2020

Beginner to intermediate

308 pages

7h 48m

English

Productivity Press

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Cover
Half-Title
Title
Copyright
Contents
Foreword Number One
Foreword Number Two
Foreword Number Three
Preface
Endorsements

Authors
1 You Need This Book
PreambleThe Hip, the Hype, the Fears, the Intrigue, and the Reality:Hype, Fear, and Intrigue No 1:Hype, Fear, and Intrigue No 2:Hype, Fear, and Intrigue No 3:Professionals Need This BookIntroductionTechnology Keeps Raging, but We Need More Than Technology to Be SuccessfulData and Analytics ExplosionA Bright Side of the RevolutionWhere Is Someone to Turn for Information?The Problem, Too Many Self-Interests: The Need for an Objective ViewThere Are Many Other Professional Stories That Are Concerned about Whether Analytics Is Important; Here Are a Few More ExamplesWhat This Book Is Not:Why This Book?Sure, Business, but Why Healthcare, Public Policy, and Business?How This Book Is OrganizedReferencesResources for the Avid Learner
2 Building a Successful Program
PreambleThe Hip, the Hype, the Fears, the Intrigue, and the RealityThe HypeRealityThe HypeRealityThe HypeRealityIntroductionCulture and Organization – Gaps and LimitationsGaps in Analytics ProgramsCharacterizing Common ProblemsDon’t Confuse Organizational Gaps for Project GapsJustifying a Data-Driven OrganizationMotivationsCritical Business EventsAnalytics as a Winning StrategyPart I – New Programs and TechnologiesPart II – More Traditional Methods of JustificationPositive Return of InvestmentScaleProductivityReliabilitySustainabilityDesigning the Organization for Program SuccessMotivation / Communication and CommitmentEstablish Clear Business OutcomesOrganization Structure and DesignThe Organization and Its Goals – AlignmentOrganizational StructureCentralized AnalyticsDecentralized or Embedded AnalyticsMultidisciplinary Roles for AnalyticsData ScientistsData EngineersCitizen Data ScientistsDevelopersBusiness ExpertsBusiness LeadersProject ManagersAnalytics Oversight Committee (AOC) and Governance Committee (Board Report)PostscriptReferencesResources for the Avid Learner
3 Some Fundamentals – Process, Data, and Models
PreambleThe Hip, the Hype, the Fears, the Intrigue, and the RealityThe HypeRealityIntroductionFramework for Analytics – Some FundamentalsProcesses Drive DataModels, Methods, and AlgorithmsModels, Models, ModelsStatistical ModelsRules of Thumb, Heuristic ModelsA Note on CognitionAlgorithms, Algorithms, AlgorithmsDistinction between Methods That Generate ModelsThere Is No Free LunchA Process Methodology for AnalyticsCRISP-DM: The Six Phases:Last ConsiderationsData ArchitectureAnalytics ArchitecturePostscriptReferencesResources for the Avid Learner
4 It’s All Analytics!
PreambleOverview of Analytics – It’s All AnalyticsAnalytics of Every Form and Analytics EverywhereIntroductionAnalytics Mega ListBreaking it Down, Categorizing AnalyticsIntroductionGartner’s ClassificationDescriptive AnalyticsDiagnostic AnalyticsPredictive AnalyticsPrescriptive AnalyticsProcess OptimizationSome Additional Thoughts on Classifying AnalyticsFundamentals of Analytics – Data BasicsIntroductionFour Scales of MeasurementData FormatsData StoresProvisioning Data for AnalyticsData SourcingData Quality Assessment and RemediationIntegrate and RepeatExploratory Data Analysis (EDA)Data TransformationsData ReductionPostscriptReferencesResources for the Avid Learner
5 What Are Business Intelligence (BI) and Visual BI?
PreambleIntroductionBackground and ChronologyBasic (Digital) ReportingA View inside the Data Warehouse and Interactive BIBeyond the Data Warehouse and Enhanced Interactive Visual BI and MoreBusiness Activity Monitoring an Alert-Based BI, Version 4.0Strengths and Weaknesses of BITransparency and Single Version of the TruthSummaryPostscriptReferencesResources for the Avid Learner
6 What Are Machine Learning and Data Mining?
PreambleOverview of Machine Learning and Data MiningIs There a Difference?A (Brief) Historical Perspective of Data Mining and Machine LearningWhat Types of Analytics Are Covered by Machine Learning?An Overview of Problem Types and Common GroundThe BIG Three!RegressionClassificationNatural Language Processing (NLP)Some (of Many) Additional Problem ClassesAssociation, Rules and Recommender SystemsClusteringSome Comments on Model TypesSome Popular Machine Learning Algorithm ClassesTrees 1.0: Classification and Regression Trees or Partition TreesTrees 2.0: Advanced Trees: Boosted Trees and Random Forests, for Classification and RegressionRegression Model Trees and Cubist ModelsLogistic and Constrained/Penalized (LASSO, Ridge, Elastic Net) RegressionMultivariate Adaptive Regression SplinesSupport Vector Machines (SVMs)Neural Networks in 1000 FlavorsK-Means and Other Clustering AlgorithmsDirected Acyclic Graph Analytics (Optimization, Social Networks)Association RulesAutoML (Automated Machine Learning)Transparency and Processing Time of AlgorithmsModel Use and DeploymentMajor Components of the Machine Learning ProcessAdvantages and Limitations of Using Machine LearningPostscriptReferencesResources for the Avid Learner
7 AI (Artificial Intelligence) and How It Differs from Machine Learning
PreambleIntroductionLet Us Outline Two Types of AI Here – Weak AI and Strong AIAI Background and ChronologyShort History of Digital AIResurrection in the 1980sBeyond the Second AI WinterDeep Learning, Bigger, and New DataNext-Generation AIDifferences of BI, Data Mining, Machine Learning, Statistics vs AIStrengths and WeaknessSome Weaknesses of AIAI’s Future“How ‘Rosy’ is the FUTURE for AI?”PostscriptReferencesResources for the Avid Learner
8 What Is Data Science?
PreambleIntroductionMushing All the Terms – Same Thing?Today’s Data Science?Data Science vs BI and Data ScientistData Science vs Data Engineering vs Citizen Data ScientistBackgrounds of Data Analytics ProfessionalsYoung Professionals’ Input on What Makes a Great Data ScientistSummaryPostscriptReferencesResources for the Avid Learner
9 Big Data and Bigger Data, Little Data, Cloud, and Other Data
PreambleIntroductionThree Popular Forms and Two Divisions of DataWhat Is Big Data?Why the Push to Big Data? Why Is Big Data Technology Attractive?The Hype of Big DataPivotal Changes in Big Data TechnologyBrief Notes on Cloud“Not Big Data” Is Alive and Well and Lessons from the SwampA Brief Note on Subjective and Synthetic DataOther Important Data Focuses of Today and TomorrowData Virtualization (DV)Streaming DataEvents (Event-Driven or Event Data)GeospatialIoT (Internet of Things)High-Performance In-Memory Computing Beyond SparkGrid and GPU ComputingNear-Memory ComputingData FabricFuture Careers in DataPostscriptReferencesFor the Avid Learner
10 Statistics, Causation, and Prescriptive Analytics
PreambleSome Statistical FoundationsIntroductionTwo Major Divisions of Statistics – Descriptive Statistics and Inferential StatisticsWhat Made Statistics Famous?Criminal Trials and Hypothesis TestingThe Scientific MethodTwo Major Paradigms of StatisticsBayesian StatisticsClassical or Frequentist StatisticsDividing It Up – Assumption Heavy and Assumption Light StatisticsNon-parametric and distribution free statistics (assumption light)Four Domains in Statistics to MentionStatistics in Predictive AnalyticsDesign of Experiments (DoE)Statistical Process Control (SPC)Time SeriesAn Ever-Important ReminderStatistics SummaryAdvantages of Statistics vs BI, Machine Learning and AIDisadvantages of Statistics vs BI, Machine Learning and AIComparison of Data-Driven Paradigms Thus FarBusiness Intelligence (BI)Machine Learning and Data MiningArtificial Intelligence (AI)StatisticsPredictive Analytics vs Prescriptive Analytics – The Missing Link Is CausationAssuming or Establishing CausationLadder of CausationPredicting an Increasing Trend – Structural Causal Models and Causal InferenceSummaryPostscriptReferencesResources for the Avid Learner
11 Other Disciplines to Dive in Deeper: Computer Science, Management/Decision Science, Operations Research, Engineering (and More)
PreambleIntroductionComputer ScienceManagement ScienceDecision ScienceOperations ResearchEngineeringFinance and EconometricsSimulation, Sensitivity and Scenario AnalysisSensitivity AnalysisScenario AnalysisSystems ThinkingPostscriptReferencesResources for the Avid Learner
12 Looking Ahead
Farewell, Until Next Time
Index

Content preview from It's All Analytics!

Chapter 10 Statistics, Causation, and Prescriptive Analytics

Keywords: Frequentist, Classical Statistics, Bayesian Statistics, Inference, Significance Testing, Non-parametric, Criminal Trial, Structural Causal Models

In the last chapter, we covered big data to little data. We now move to a commonly misunderstood subject, statistics. We will cover various forms and put statistics in context with analytics, data science, machine learning, and AI. A data scientist should be well versed in statistics, as we will see that statistics is the foundation for virtually all forms of analytics. We then follow with bridges between predictive analytics and prescriptive analytics, and one of these bridges is statistics.

This chapter will be a bit ...

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It's All Analytics!

by Scott Burk, Gary D. Miner

Chapter 10

Statistics, Causation, and Prescriptive Analytics

Preamble

Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
and much more.