It's All Analytics! The Foundations of AI, Big Data, and Data Science Landscape for Professionals in Healthcare, Business, and Government

Description

Professionals are challenged each day by a changing landscape of technology and terminology. In recent history, especially the last 25 years there has been an explosion of terms and methods born that automate and improve decision-making and operations. One term called Analytics is an overarching description of a compilation of methodologies. But, AI (Artificial Intelligence), statistics, decision science, optimization which have been around for decades has resurged. Also, things like business intelligence, On-line Analytical Processing (OLAP) and many, many more have been born or reborn. How is someone to make sense of all this methodology, terminology? 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. The authors include the basics such as algorithms, mental concepts, models, and paradigms in addition to the benefits of machine learning. The book also includes a chapter on data and the various forms of data. The authors wrap up this book with a look at next frontiers such as applications and designing your environment for success, which segue into the topics of the next two books in the series. Cover Half Title #2,0,-32767Title Page #4,0,-32767Copyright Page #5,0,-32767Table of Contents #6,0,-32767Foreword Number One #16,0,-32767Foreword Number Two #18,0,-32767Foreword Number Three #20,0,-32767Preface #22,0,-32767Endorsements Authors #32,0,-32767Chapter 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 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) 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 References Resources for the Avid Learner Chapter 12 Looking Ahead Farewell, Until Next Time