Gespeichert in:
| Titel: | It's all analytics! the foundations of ai, big data, and data science landscape for professionals in healthcare, business, and government |
|---|---|
| Von: |
Scott Burk, Ph.D., Gary D. Miner, Ph.D.
|
| Person: |
Burk, Scott
ca. 20./21. Jh. Verfasser aut Miner, Gary 1942- |
| Hauptverfassende: | , |
| Format: | Buch |
| Sprache: | Englisch |
| Veröffentlicht: |
Boca Raton ; London ; New York
CRC Pres,s Taylor & Francis Group
2020
|
| Schlagworte: | |
| Online-Zugang: | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=032239172&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| Beschreibung: | XXXV, 272 Seiten Diagramme |
| ISBN: | 0367359685 9780367359683 |
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Datensatz im Suchindex
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|---|---|
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| adam_text | Contents Foreword Number One........................................................................... xv Foreword Number Two......................................................................... xvii Foreword Number Three.......................................................................xix Preface...................................................................................................... xxi Endorsements....................................................................................... xxvii Authors................................................................................................... xxxi 1 You Need This Book.......................................................................... 1 Preamble......................................................................................................... 1 The Hip, the Hype, the Fears, the Intrigue, and the Reality:.................... 2 Hype, Fear, and Intrigue No 1:................................................................ 2 Hype, Fear, and Intrigue No 2:................................................................ 2 Hype, Fear, and Intrigue No 3:................................................................ 3 Professionals Need This Book..................................................................... 6 Introduction............................................................................................... 6 Technology Keeps Raging, but We Need More Than Technology to Be Successful........................................................................................ 6 Data
and Analytics Explosion................................................................. 10 A Bright Side of the Revolution.................................................................. 14 Where Is Someone to Turn for Information?........................................ 17 The Problem, Too Many Self-Interests: The Need for an Objective View.......................................................................................................... 25 There Are Many Other Professional Stories That Are Concerned about Whether Analytics Is Important; Here Are a Few More Examples..................................................................................................29 What This Book Is Not:.............................................................................. 33 Why This Book?...........................................................................................33 Sure, Business, but Why Healthcare, Public Policy, and Business?........ 34
vi ■ Contents How This Book Is Organized.................................................................... 39 References.................................................................................................... 41 Resources for the Avid Learner...................................................................44 2 Building a Successful Program......................................................45 Preamble....................................................................................................... 45 The Hip, the Hype, the Fears, the Intrigue, and the Reality................... 45 The Hype.................................................................................................. 45 Reality....................................................................................................... 45 The Hype..................................................................................................46 Reality....................................................................................................... 46 The Hype..................................................................................................46 Reality....................................................................................................... 46 Introduction..................................................................................................46 Culture and Organization - Gaps and Limitations................................... 47 Gaps in Analytics Programs...................................................................48 Characterizing Common
Problems.........................................................51 Don’t Confuse Organizational Gaps for Project Gaps..............................55 Justifying a Data-Driven Organization.......................................................56 Motivations................................................................................................... 56 Critical Business Events.......................................................................... 57 Analytics as a Winning Strategy................................................................. 57 Part I - New Programs and Technologies............................................. 57 Part II - More Traditional Methods of Justification...............................58 Positive Return of Investment................................................................. 58 Scale.......................................................................................................... 59 Productivity.............................................................................................. 59 Reliability.................................................................................................. 59 Sustainability............................................................................................60 Designing the Organization for Program Success.................................... 61 Motivation / Communication and Commitment....................................... 62 Establish Clear Business Outcomes........................................................62 Organization Structure and
Design............................................................63 The Organization and Its Goals ֊ Alignment....................................... 63 Organizational Structure............................................................................. 64 Centralized Analytics.................................................................................. 64 Decentralized or Embedded Analytics...................................................... 66 Multidisciplinary Roles for Analytics.......................................................... 67 Data Scientists..........................................................................................68
Contents ■ vii Data Engineers........................................................................................ 68 Citizen Data Scientists.............................................................................68 Developers................................................................................................69 Business Experts..................................................................................... 69 Business Leaders..................................................................................... 69 Project Managers..................................................................................... 69 Analytics Oversight Committee (AOC) and Governance Committee (Board Report).............................................................................................71 Postscript...................................................................................................... 71 References.................................................................................................... 72 Resources for the Avid Learner...................................................................72 3 Some Fundamentals - Process, Data, and Models.......................75 Preamble....................................................................................................... 75 The Hip, the Hype, the Fears, the Intrigue, and the Reality................... 75 The Hype..................................................................................................75
Reality....................................................................................................... 76 Introduction..................................................................................................76 Framework for Analytics - Some Fundamentals......................................76 Processes Drive Data.................................................................................. 77 Models, Methods, and Algorithms............................................................. 80 Models, Models, Models..........................................................................80 Statistical Models....... ..................................................................................81 Rules of Thumb, Heuristic Models............................................................ 82 A Note on Cognition................................................................................... 83 Algorithms, Algorithms, Algorithms.......................................................... 84 Distinction between Methods That Generate Models............................. 85 There Is No Free Lunch.............................................................................. 86 A Process Methodology for Analytics....................................................... 89 CRISP-DM: The Six Phases:....................................................................90 Last Considerations..................................................................................... 92 Data
Architecture.................................................................................... 92 Analytics Architecture............................................................................. 92 Postscript......................................................................................................93 References....................................................................................................93 Resources for the Avid Learner...................................................................94 4 Iťs All Analytics!..............................................................................95 Preamble.......................................................................................................95 Overview of Analytics ֊ It’s All Analytics................................................95
viii ■ Contents Analytics of Every Form and Analytics Everywhere................................98 Introduction..............................................................................................98 Analytics Mega List................................................................................. 98 Breaking it Down, Categorizing Analytics.............................................. 100 Introduction............................................................................................ 100 Gartner’s Classification.......................................................................... 100 Descriptive Analytics.............................................................................. 101 Diagnostic Analytics...............................................................................102 Predictive Analytics................................................................................103 Prescriptive Analytics.............................................................................104 Process Optimization.............................................................................105 Some Additional Thoughts on Classifying Analytics..........................106 Fundamentals of Analytics - Data Basics................................................107 Introduction............................................................................................ 107 Four Scales of Measurement................................................................. 107 Data
Formats.......................................................................................... 108 Data Stores.............................................................................................. 109 Provisioning Data for Analytics............................................................ 109 Data Sourcing......................................................................................... Ill Data Quality Assessment and Remediation.........................................Ill Integrate and Repeat.............................................................................. 114 Exploratory Data Analysis (EDA).......................................................... 115 Data Transformations............................................................................. 116 Data Reduction....................................................................................... 116 Postscript.....................................................................................................117 References...................................................................................................117 Resources for the Avid Learner..................................................................118 5 What Are Business Intelligence (Bí) and Visual Bí?.................. 119 Preamble......................................................................................................119 Introduction.................................................................................................119 Background and
Chronology................................................................... 122 Basic (Digital) Reporting........................................................................122 A View inside the Data Warehouse and Interactive Bí......................123 Beyond the Data Warehouse and Enhanced Interactive Visual Bí and More................................................................................................ 125 Business Activity Monitoring an Alert-Based Bí, Version 4.0............ 125 Strengths and Weaknesses of Bí.............................................................. 126 Transparency and Single Version of the Truth.................................... 126
Contents ■ ix Summary.................................................................................................... 135 Postscript.................................................................................................... 136 References.................................................................................................. 136 Resources for the Avid Learner................................................................. 137 6 What Are Machine Learning and Data Mining?........................... 139 Preamble..................................................................................................... 139 Overview of Machine Learning and Data Mining..................................139 Is There a Difference?........................................................................... 139 A (Brief) Historical Perspective of Data Mining and Machine Learning.................................................................................................. 140 What Types of Analytics Are Covered by Machine Learning?.............. 143 An Overview of Problem Types and Common Ground.....................144 The BIG Three!......................................................................................144 Regression............................................................................................... 144 Classification........................................................................................... 145 Natural Language Processing (NLP).....................................................145 Some (of Many) Additional Problem
Classes...................................... 146 Association, Rules and Recommender Systems.................................. 147 Clustering................................................................................................ 148 Some Comments on Model Types........................................................148 Some Popular Machine Learning Algorithm Classes..........................149 Trees 1.0: Classification and Regression Trees or Partition Trees....150 Trees 2.0: Advanced Trees: Boosted Trees and Random Forests, for Classification and Regression...................................................... 151 Regression Model Trees and Cubist Models.................................... 151 Logistic and Constrained/Penalized (LASSO, Ridge, Elastic Net) Regression........................................................................................... 152 Multivariate Adaptive Regression Splines........................................ 153 Support Vector Machines (SVMs)......................................................153 Neural Networks in 1000 Flavors.....................................................153 К-Means and Other Clustering Algorithms..................................... 154 Directed Acyclic Graph Analytics (Optimization, Social Networks)........................................................................................... 154 Association Rules................................................................................155 AutoML (Automated Machine Learning)..........................................155 Transparency and Processing Time
of Algorithms................................. 156 Model Use and Deployment..................................................................... 156 Major Components of the Machine Learning Process............................ 156
x ■ Contents Advantages and Limitations of Using Machine Learning....................... 157 Postscript.....................................................................................................158 References...................................................................................................158 Resources for the Avid Learner................................................................. 160 7 AI (Artificial Intelligence) and HowIt Differs from Machine Learning............................................................................................161 Preamble......................................................................................................161 Introduction.................................................................................................161 Let Us Outline Two Types of AI Here ֊ Weak AI and Strong AI.... 1б2 AI Background and Chronology.............................................................. 164 Short History of Digital AI.....................................................................165 Resurrection in the 1980s.................................................................. 165 Beyond the Second AI Winter.......................................................... 166 Deep Learning, Bigger, and New Data.................................................... 167 Next-Generation AI.................................................................................... 1б9 Differences of Bí, Data Mining, MachineLearning, Statistics vs AI..... 171 Strengths and
Weakness............................................................................172 Some Weaknesses of AI.........................................................................172 Aľs Future.................................................................................................. 177 “How ‘Rosy’ is the FUTURE for AI?”.................................................... 177 Postscript.....................................................................................................179 References...................................................................................................179 Resources for the Avid Learner................................................................. 181 8 What Is Data Science?.................................................................... 183 Preamble......................................................................................................183 Introduction................................................................................................ 183 Mushing All the Terms ֊ Same Thing?.....................................................186 Today’s Data Science?.............................................................................189 Data Science vs Bí and Data Scientist..................................................189 Data Science vs Data Engineering vs Citizen Data Scientist............. 189 Backgrounds of Data Analytics Professionals..................................... 194 Young Professionals’ Input on What Makes a Great Data Scientist....196
Summary.................................................................................................... 200 Postscript.................................................................................................... 200 References.................................................................................................. 200 Resources for the Avid Learner................................................................. 202
Contents M xi 9 Big Data and Bigger Data, Little Data, Cloud, and Other Data..................................................................................................203 Preamble..................................................................................................... 203 Introduction................................................................................................203 Three Popular Forms and Two Divisions of Data..................................204 What Is Big Data?...................................................................................... 205 Why the Push to Big Data? Why Is Big Data Technology Attractive?.... 207 The Hype of Big Data............................................................................... 208 Pivotal Changes in Big Data Technology................................................ 210 Brief Notes on Cloud.................................................................................211 “Not Big Data” Is Alive and Well and Lessons from the Swamp.......... 213 A Brief Note on Subjective and Synthetic Data...................................... 215 Other Important Data Focuses of Today and Tomorrow.......................216 Data Virtualization (DV)....................................................................... 216 Streaming Data....................................................................................... 217 Events (Event-Driven or Event Data)....................................................217
Geospatial............................................................................................... 218 IoT (Internet of Things)......................................................................... 218 High-Performance In-Memory ComputingBeyond Spark..................219 Grid and GPU Computing.................................................................... 219 Near-Memory Computing.....................................................................220 Data Fabric.............................................................................................220 Future Careers in Data.............................................................................. 221 Postscript.................................................................................................... 222 References.................................................................................................. 222 For the Avid Learner................................................................................. 224 10 Statistics, Causation, and Prescriptive Analytics.......................225 Preamble..................................................................................................... 225 Some Statistical Foundations....................................................................226 Introduction............................................................................................226 Two Major Divisions of Statistics ֊ Descriptive Statistics and Inferential Statistics............................................................................... 227 What Made
Statistics Famous?..............................................................228 Criminal Trials and Hypothesis Testing...........................................228 The Scientific Method....................................................................... 229 Two Major Paradigms of Statistics........................................................231 Bayesian Statistics.............................................................................. 231 Classical or Frequentisi Statistics...................................................... 232
xii ■ Contents Dividing It Up - Assumption Heavy and Assumption Light Statistics.................................................................................................. 233 Non-Parametric and Distribution Free Statistics (Assumption Light)................................................................................................... 235 Four Domains in Statistics to Mention................................................ 236 Statistics in Predictive Analytics........................................................236 Design of Experiments (DoE)...........................................................237 Statistical Process Control (SPC)........................................................237 Time Series.........................................................................................238 An Ever-Important Reminder................................................................ 239 Statistics Summary................................................................................ 240 Advantages of Statistics vs Bí, Machine Learning and AI.............. 240 Disadvantages of Statistics vs Bí, Machine Learning and AI........ 241 Comparison of Data-Driven Paradigms Thus Far................................... 242 Business Intelligence (Bí)..................................................................... 242 Machine Learning and Data Mining.....................................................243 Artificial Intelligence (AI)...................................................................... 243
Statistics.................................................................................................. 243 Predictive Analytics vs Prescriptive Analytics - The Missing Link Is Causation........................................................................................244 Assuming or Establishing Causation........................................................246 Ladder of Causation...................................................................................248 Predicting an Increasing Trend - Structural Causal Models and Causal Inference.........................................................................................249 Summary.................................................................................................... 251 Postscript.................................................................................................... 251 References.................................................................................................. 252 Resources for the Avid Learner................................................................. 253 11 Other Disciplines to Dive in Deeper: Computer Science, Management/Decision Science, Operations Research, Engineering (and More).................................................................255 Preamble..................................................................................................... 255 Introduction................................................................................................ 255 Computer
Science......................................................................................256 Management Science..................................................................................257 Decision Science........................................................................................258 Operations Research..................................................................................259 Engineering................................................................................................ 260
Contents ■ xiii Finance and Econometrics........................................................................ 260 Simulation, Sensitivity and Scenario Analysis.........................................260 Sensitivity Analysis................................................................................ 260 Scenario Analysis...................................................................................261 Systems Thinking...................................................................................26l Postscript.................................................................................................... 261 References.................................................................................................. 262 Resources for the Avid Learner.................................................................262 12 Looking Ahead..................................................................................... 263 Farewell, Until Next Time......................................................................... 263 Index............................................................................................................... 265
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| id | DE-604.BV046829990 |
| illustrated | Not Illustrated |
| indexdate | 2024-12-20T19:02:12Z |
| institution | BVB |
| isbn | 0367359685 9780367359683 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-032239172 |
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| spellingShingle | Burk, Scott ca. 20./21. Jh Miner, Gary 1942- It's all analytics! the foundations of ai, big data, and data science landscape for professionals in healthcare, business, and government Maschinelles Lernen (DE-588)4193754-5 gnd Künstliche Intelligenz (DE-588)4033447-8 gnd Data Science (DE-588)1140936166 gnd |
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| title | It's all analytics! the foundations of ai, big data, and data science landscape for professionals in healthcare, business, and government |
| title_auth | It's all analytics! the foundations of ai, big data, and data science landscape for professionals in healthcare, business, and government |
| title_exact_search | It's all analytics! the foundations of ai, big data, and data science landscape for professionals in healthcare, business, and government |
| title_full | It's all analytics! the foundations of ai, big data, and data science landscape for professionals in healthcare, business, and government Scott Burk, Ph.D., Gary D. Miner, Ph.D. |
| title_fullStr | It's all analytics! the foundations of ai, big data, and data science landscape for professionals in healthcare, business, and government Scott Burk, Ph.D., Gary D. Miner, Ph.D. |
| title_full_unstemmed | It's all analytics! the foundations of ai, big data, and data science landscape for professionals in healthcare, business, and government Scott Burk, Ph.D., Gary D. Miner, Ph.D. |
| title_short | It's all analytics! |
| title_sort | it s all analytics the foundations of ai big data and data science landscape for professionals in healthcare business and government |
| title_sub | the foundations of ai, big data, and data science landscape for professionals in healthcare, business, and government |
| topic | Maschinelles Lernen (DE-588)4193754-5 gnd Künstliche Intelligenz (DE-588)4033447-8 gnd Data Science (DE-588)1140936166 gnd |
| topic_facet | Maschinelles Lernen Künstliche Intelligenz Data Science |
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Inhaltsverzeichnis
Ausleihbestand Seybothstrasse
| Signatur: | F 03 ST 530 B959 Lageplan |
|---|---|
| Exemplar 1 | uneingeschränkt entleihbar Am Standort |