It's all analytics!: the foundations of ai, big data, and data science landscape for professionals in healthcare, business, and government
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Boca Raton ; London ; New York
CRC Pres,s Taylor & Francis Group
2020
|
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | XXXV, 272 Seiten Diagramme |
| ISBN: | 0367359685 9780367359683 |
Internformat
MARC
| LEADER | 00000nam a2200000 c 4500 | ||
|---|---|---|---|
| 001 | BV046829990 | ||
| 003 | DE-604 | ||
| 005 | 20220214 | ||
| 007 | t | ||
| 008 | 200729s2020 |||| |||| 00||| eng d | ||
| 020 | |a 0367359685 |9 0-367-35968-5 | ||
| 020 | |a 9780367359683 |9 978-0-367-35968-3 | ||
| 035 | |a (OCoLC)1193293394 | ||
| 035 | |a (DE-599)BVBBV046829990 | ||
| 040 | |a DE-604 |b ger |e rda | ||
| 041 | 0 | |a eng | |
| 049 | |a DE-898 |a DE-739 | ||
| 084 | |a ST 530 |0 (DE-625)143679: |2 rvk | ||
| 100 | 1 | |a Burk, Scott |d ca. 20./21. Jh. |e Verfasser |0 (DE-588)1251791255 |4 aut | |
| 245 | 1 | 0 | |a It's all analytics! |b the foundations of ai, big data, and data science landscape for professionals in healthcare, business, and government |c Scott Burk, Ph.D., Gary D. Miner, Ph.D. |
| 264 | 1 | |a Boca Raton ; London ; New York |b CRC Pres,s Taylor & Francis Group |c 2020 | |
| 300 | |a XXXV, 272 Seiten |b Diagramme | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 650 | 0 | 7 | |a Maschinelles Lernen |0 (DE-588)4193754-5 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Künstliche Intelligenz |0 (DE-588)4033447-8 |2 gnd |9 rswk-swf |
| 650 | 0 | 7 | |a Data Science |0 (DE-588)1140936166 |2 gnd |9 rswk-swf |
| 689 | 0 | 0 | |a Künstliche Intelligenz |0 (DE-588)4033447-8 |D s |
| 689 | 0 | 1 | |a Maschinelles Lernen |0 (DE-588)4193754-5 |D s |
| 689 | 0 | 2 | |a Data Science |0 (DE-588)1140936166 |D s |
| 689 | 0 | |5 DE-604 | |
| 700 | 1 | |a Miner, Gary |d 1942- |e Verfasser |0 (DE-588)140491813 |4 aut | |
| 776 | 0 | 8 | |i Erscheint auch als |n Online-Ausgabe, EPUB |z 978-0-429-34398-8 |
| 856 | 4 | 2 | |m Digitalisierung UB Passau - ADAM Catalogue Enrichment |q application/pdf |u 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 |3 Inhaltsverzeichnis |
| 999 | |a oai:aleph.bib-bvb.de:BVB01-032239172 | ||
Datensatz im Suchindex
| _version_ | 1804181650331926528 |
|---|---|
| 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
|
| adam_txt |
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 |
| any_adam_object | 1 |
| any_adam_object_boolean | 1 |
| author | Burk, Scott ca. 20./21. Jh Miner, Gary 1942- |
| author_GND | (DE-588)1251791255 (DE-588)140491813 |
| author_facet | Burk, Scott ca. 20./21. Jh Miner, Gary 1942- |
| author_role | aut aut |
| author_sort | Burk, Scott ca. 20./21. Jh |
| author_variant | s b sb g m gm |
| building | Verbundindex |
| bvnumber | BV046829990 |
| classification_rvk | ST 530 |
| ctrlnum | (OCoLC)1193293394 (DE-599)BVBBV046829990 |
| discipline | Informatik |
| discipline_str_mv | Informatik |
| format | Book |
| fullrecord | <?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01840nam a2200397 c 4500</leader><controlfield tag="001">BV046829990</controlfield><controlfield tag="003">DE-604</controlfield><controlfield tag="005">20220214 </controlfield><controlfield tag="007">t</controlfield><controlfield tag="008">200729s2020 |||| |||| 00||| eng d</controlfield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">0367359685</subfield><subfield code="9">0-367-35968-5</subfield></datafield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">9780367359683</subfield><subfield code="9">978-0-367-35968-3</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(OCoLC)1193293394</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)BVBBV046829990</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-604</subfield><subfield code="b">ger</subfield><subfield code="e">rda</subfield></datafield><datafield tag="041" ind1="0" ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="049" ind1=" " ind2=" "><subfield code="a">DE-898</subfield><subfield code="a">DE-739</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">ST 530</subfield><subfield code="0">(DE-625)143679:</subfield><subfield code="2">rvk</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Burk, Scott</subfield><subfield code="d">ca. 20./21. Jh.</subfield><subfield code="e">Verfasser</subfield><subfield code="0">(DE-588)1251791255</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">It's all analytics!</subfield><subfield code="b">the foundations of ai, big data, and data science landscape for professionals in healthcare, business, and government</subfield><subfield code="c">Scott Burk, Ph.D., Gary D. Miner, Ph.D.</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="a">Boca Raton ; London ; New York</subfield><subfield code="b">CRC Pres,s Taylor & Francis Group</subfield><subfield code="c">2020</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">XXXV, 272 Seiten</subfield><subfield code="b">Diagramme</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">Maschinelles Lernen</subfield><subfield code="0">(DE-588)4193754-5</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">Künstliche Intelligenz</subfield><subfield code="0">(DE-588)4033447-8</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">Data Science</subfield><subfield code="0">(DE-588)1140936166</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="689" ind1="0" ind2="0"><subfield code="a">Künstliche Intelligenz</subfield><subfield code="0">(DE-588)4033447-8</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="0" ind2="1"><subfield code="a">Maschinelles Lernen</subfield><subfield code="0">(DE-588)4193754-5</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="0" ind2="2"><subfield code="a">Data Science</subfield><subfield code="0">(DE-588)1140936166</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="0" ind2=" "><subfield code="5">DE-604</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Miner, Gary</subfield><subfield code="d">1942-</subfield><subfield code="e">Verfasser</subfield><subfield code="0">(DE-588)140491813</subfield><subfield code="4">aut</subfield></datafield><datafield tag="776" ind1="0" ind2="8"><subfield code="i">Erscheint auch als</subfield><subfield code="n">Online-Ausgabe, EPUB</subfield><subfield code="z">978-0-429-34398-8</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="m">Digitalisierung UB Passau - ADAM Catalogue Enrichment</subfield><subfield code="q">application/pdf</subfield><subfield code="u">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</subfield><subfield code="3">Inhaltsverzeichnis</subfield></datafield><datafield tag="999" ind1=" " ind2=" "><subfield code="a">oai:aleph.bib-bvb.de:BVB01-032239172</subfield></datafield></record></collection> |
| id | DE-604.BV046829990 |
| illustrated | Not Illustrated |
| index_date | 2024-07-03T15:04:33Z |
| indexdate | 2024-07-10T08:55:02Z |
| institution | BVB |
| isbn | 0367359685 9780367359683 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-032239172 |
| oclc_num | 1193293394 |
| open_access_boolean | |
| owner | DE-898 DE-BY-UBR DE-739 |
| owner_facet | DE-898 DE-BY-UBR DE-739 |
| physical | XXXV, 272 Seiten Diagramme |
| publishDate | 2020 |
| publishDateSearch | 2020 |
| publishDateSort | 2020 |
| publisher | CRC Pres,s Taylor & Francis Group |
| record_format | marc |
| spelling | Burk, Scott ca. 20./21. Jh. Verfasser (DE-588)1251791255 aut 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. Boca Raton ; London ; New York CRC Pres,s Taylor & Francis Group 2020 XXXV, 272 Seiten Diagramme txt rdacontent n rdamedia nc rdacarrier Maschinelles Lernen (DE-588)4193754-5 gnd rswk-swf Künstliche Intelligenz (DE-588)4033447-8 gnd rswk-swf Data Science (DE-588)1140936166 gnd rswk-swf Künstliche Intelligenz (DE-588)4033447-8 s Maschinelles Lernen (DE-588)4193754-5 s Data Science (DE-588)1140936166 s DE-604 Miner, Gary 1942- Verfasser (DE-588)140491813 aut Erscheint auch als Online-Ausgabe, EPUB 978-0-429-34398-8 Digitalisierung UB Passau - ADAM Catalogue Enrichment application/pdf 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 Inhaltsverzeichnis |
| 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 |
| subject_GND | (DE-588)4193754-5 (DE-588)4033447-8 (DE-588)1140936166 |
| 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_exact_search_txtP | 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 |
| url | 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 |
| work_keys_str_mv | AT burkscott itsallanalyticsthefoundationsofaibigdataanddatasciencelandscapeforprofessionalsinhealthcarebusinessandgovernment AT minergary itsallanalyticsthefoundationsofaibigdataanddatasciencelandscapeforprofessionalsinhealthcarebusinessandgovernment |