UID:
kobvindex_INTEBC6209045
Format:
1 online resource (309 pages)
Edition:
1st ed.
ISBN:
9781000067200
Content:
This book, the first in a series of three, provides a look at the foundations of artificial intelligence and analytics and why readers need an unbiased understanding of the subject
Note:
Cover -- Half Title -- Title Page -- Copyright Page -- Table of Contents -- Foreword Number One -- Foreword Number Two -- Foreword Number Three -- Preface -- Endorsements -- Authors -- Chapter 1 You Need This Book -- Preamble -- The 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 Book -- Introduction -- Technology Keeps Raging, but We Need More Than Technology to Be Successful -- Data and Analytics Explosion -- A Bright Side of the Revolution -- Where Is Someone to Turn for Information? -- The Problem, Too Many Self-Interests: The Need for an Objective View -- There Are Many Other Professional Stories That Are Concerned about Whether Analytics Is Important -- Here Are a Few More Examples -- What This Book Is Not: -- Why This Book? -- Sure, Business, but Why Healthcare, Public Policy, and Business? -- How This Book Is Organized -- References -- Resources for the Avid Learner -- Chapter 2 Building a Successful Program -- Preamble -- The Hip, the Hype, the Fears, the Intrigue, and the Reality -- The Hype -- Reality -- The Hype -- Reality -- The Hype -- Reality -- Introduction -- Culture and Organization - Gaps and Limitations -- Gaps in Analytics Programs -- Characterizing Common Problems -- Don't Confuse Organizational Gaps for Project Gaps -- Justifying a Data-Driven Organization -- Motivations -- Critical Business Events -- Analytics as a Winning Strategy -- Part I - New Programs and Technologies -- Part II - More Traditional Methods of Justification -- Positive Return of Investment -- Scale -- Productivity -- Reliability -- Sustainability -- Designing the Organization for Program Success -- Motivation / Communication and Commitment -- Establish Clear Business Outcomes -- Organization Structure and Design
,
Data Transformations -- Data Reduction -- Postscript -- References -- Resources for the Avid Learner -- Chapter 5 What Are Business Intelligence (BI) and Visual BI? -- Preamble -- Introduction -- Background and Chronology -- Basic (Digital) Reporting -- A View inside the Data Warehouse and Interactive BI -- Beyond the Data Warehouse and Enhanced Interactive Visual BI and More -- Business Activity Monitoring an Alert-Based BI, Version 4.0 -- Strengths and Weaknesses of BI -- Transparency and Single Version of the Truth -- Summary -- Postscript -- References -- Resources for the Avid Learner -- Chapter 6 What Are Machine Learning and Data Mining? -- Preamble -- Overview of Machine Learning and Data Mining -- Is There a Difference? -- A (Brief) Historical Perspective of Data Mining and Machine Learning -- What Types of Analytics Are Covered by Machine Learning? -- An Overview of Problem Types and Common Ground -- The BIG Three! -- Regression -- Classification -- Natural Language Processing (NLP) -- Some (of Many) Additional Problem Classes -- Association, Rules and Recommender Systems -- Clustering -- Some Comments on Model Types -- Some Popular Machine Learning Algorithm Classes -- Trees 1.0: Classification and Regression Trees or Partition Trees -- Trees 2.0: Advanced Trees: Boosted Trees and Random Forests, for Classification and Regression -- Regression Model Trees and Cubist Models -- Logistic and Constrained/Penalized (LASSO, Ridge, Elastic Net) Regression -- Multivariate Adaptive Regression Splines -- Support Vector Machines (SVMs) -- Neural Networks in 1000 Flavors -- K-Means and Other Clustering Algorithms -- Directed Acyclic Graph Analytics (Optimization, Social Networks) -- Association Rules -- AutoML (Automated Machine Learning) -- Transparency and Processing Time of Algorithms -- Model Use and Deployment
,
Major Components of the Machine Learning Process -- Advantages and Limitations of Using Machine Learning -- Postscript -- References -- Resources for the Avid Learner -- Chapter 7 AI (Artificial Intelligence) and How It Differs from Machine Learning -- Preamble -- Introduction -- Let Us Outline Two Types of AI Here - Weak AI and Strong AI -- AI Background and Chronology -- Short History of Digital AI -- Resurrection in the 1980s -- Beyond the Second AI Winter -- Deep Learning, Bigger, and New Data -- Next-Generation AI -- Differences of BI, Data Mining, Machine Learning, Statistics vs AI -- Strengths and Weakness -- Some Weaknesses of AI -- AI's Future -- "How 'Rosy' is the FUTURE for AI?" -- Postscript -- References -- Resources for the Avid Learner -- Chapter 8 What Is Data Science? -- Preamble -- Introduction -- Mushing All the Terms - Same Thing? -- Today's Data Science? -- Data Science vs BI and Data Scientist -- Data Science vs Data Engineering vs Citizen Data Scientist -- Backgrounds of Data Analytics Professionals -- Young Professionals' Input on What Makes a Great Data Scientist -- Summary -- Postscript -- References -- Resources for the Avid Learner -- Chapter 9 Big Data and Bigger Data, Little Data, Cloud, and Other Data -- Preamble -- Introduction -- Three Popular Forms and Two Divisions of Data -- What Is Big Data? -- Why the Push to Big Data? Why Is Big Data Technology Attractive? -- The Hype of Big Data -- Pivotal Changes in Big Data Technology -- Brief Notes on Cloud -- "Not Big Data" Is Alive and Well and Lessons from the Swamp -- A Brief Note on Subjective and Synthetic Data -- Other Important Data Focuses of Today and Tomorrow -- Data Virtualization (DV) -- Streaming Data -- Events (Event-Driven or Event Data) -- Geospatial -- IoT (Internet of Things) -- High-Performance In-Memory Computing Beyond Spark -- Grid and GPU Computing
,
Near-Memory Computing -- Data Fabric -- Future Careers in Data -- Postscript -- References -- For the Avid Learner -- Chapter 10 Statistics, Causation, and Prescriptive Analytics -- Preamble -- Some Statistical Foundations -- Introduction -- Two Major Divisions of Statistics - Descriptive Statistics and Inferential Statistics -- What Made Statistics Famous? -- Criminal Trials and Hypothesis Testing -- The Scientific Method -- Two Major Paradigms of Statistics -- Bayesian Statistics -- Classical or Frequentist Statistics -- Dividing It Up - Assumption Heavy and Assumption Light Statistics -- Non-Parametric and Distribution Free Statistics (Assumption Light) -- Four Domains in Statistics to Mention -- Statistics in Predictive Analytics -- Design of Experiments (DoE) -- Statistical Process Control (SPC) -- Time Series -- An Ever-Important Reminder -- Statistics Summary -- Advantages of Statistics vs BI, Machine Learning and AI -- Disadvantages of Statistics vs BI, Machine Learning and AI -- Comparison of Data-Driven Paradigms Thus Far -- Business Intelligence (BI) -- Machine Learning and Data Mining -- Artificial Intelligence (AI) -- Statistics -- Predictive Analytics vs Prescriptive Analytics - The Missing Link Is Causation -- Assuming or Establishing Causation -- Ladder of Causation -- Predicting an Increasing Trend - Structural Causal Models and Causal Inference -- Summary -- Postscript -- References -- Resources for the Avid Learner -- Chapter 11 Other Disciplines to Dive in Deeper: Computer Science, Management/Decision Science, Operations Research, Engineering (and More) -- Preamble -- Introduction -- Computer Science -- Management Science -- Decision Science -- Operations Research -- Engineering -- Finance and Econometrics -- Simulation, Sensitivity and Scenario Analysis -- Sensitivity Analysis -- Scenario Analysis -- Systems Thinking
,
Postscript
,
The Organization and Its Goals - Alignment -- Organizational Structure -- Centralized Analytics -- Decentralized or Embedded Analytics -- Multidisciplinary Roles for Analytics -- Data Scientists -- Data Engineers -- Citizen Data Scientists -- Developers -- Business Experts -- Business Leaders -- Project Managers -- Analytics Oversight Committee (AOC) and Governance Committee (Board Report) -- Postscript -- References -- Resources for the Avid Learner -- Chapter 3 Some Fundamentals - Process, Data, and Models -- Preamble -- The Hip, the Hype, the Fears, the Intrigue, and the Reality -- The Hype -- Reality -- Introduction -- Framework for Analytics - Some Fundamentals -- Processes Drive Data -- Models, Methods, and Algorithms -- Models, Models, Models -- Statistical Models -- Rules of Thumb, Heuristic Models -- A Note on Cognition -- Algorithms, Algorithms, Algorithms -- Distinction between Methods That Generate Models -- There Is No Free Lunch -- A Process Methodology for Analytics -- CRISP-DM: The Six Phases: -- Last Considerations -- Data Architecture -- Analytics Architecture -- Postscript -- References -- Resources for the Avid Learner -- Chapter 4 It's All Analytics! -- Preamble -- Overview of Analytics - It's All Analytics -- Analytics of Every Form and Analytics Everywhere -- Introduction -- Analytics Mega List -- Breaking it Down, Categorizing Analytics -- Introduction -- Gartner's Classification -- Descriptive Analytics -- Diagnostic Analytics -- Predictive Analytics -- Prescriptive Analytics -- Process Optimization -- Some Additional Thoughts on Classifying Analytics -- Fundamentals of Analytics - Data Basics -- Introduction -- Four Scales of Measurement -- Data Formats -- Data Stores -- Provisioning Data for Analytics -- Data Sourcing -- Data Quality Assessment and Remediation -- Integrate and Repeat -- Exploratory Data Analysis (EDA)
Additional Edition:
Print version Burk, Scott It's All Analytics! Oxford : Productivity Press,c2020 ISBN 9780367359683
Language:
English
Keywords:
Electronic books
;
Electronic books
URL:
FULL
((OIS Credentials Required))
URL:
FULL
((OIS Credentials Required))
Bookmarklink
