It’s All Analytics The Foundations of AI Big Data and Data scape for Professionals in Healthcare Business and Government 1st edition By Scott Burk, Gary Miner ISBN 0367493790 9780367493790

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ISBN 10: 0367493790
ISBN 13: 9780367493790
Author: Scott Burk, Gary Miner

Professionals are challenged each day by a changing landscape of technology and terminology. In recent history, especially in the last 25 years, there has been an explosion of terms and methods that automate and improve decision-making and operations. One term, “analytics,” is an overarching description of a compilation of methodologies. But AI (artificial intelligence), statistics, decision science, and optimization, which have been around for decades, have resurged. Also, things like business intelligence, online analytical processing (OLAP) and many, many more have been born or reborn. How is someone to make sense of all this methodology and 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 the next frontiers such as applications and designing your environment for success, which segue into the topics of the next two books in the series.

It’s All Analytics! The Foundations of AI Big Data and Data scape for Professionals in Healthcare Business and Government 1st Table of contents:

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

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

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

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

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

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

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

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

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

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

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

12 Looking Ahead

Farewell, Until Next Time

Index

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Tags: Scott Burk, Gary Miner, The Foundations, Healthcare Business