It's all analytics!: the foundations of Al, big data, and data science landscape for professionals in healthcare, business, and government
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Boca Raton ; London ; New York
CRC Press
2020
|
| Ausgabe: | First edition |
| Schlagworte: | |
| Online-Zugang: | TUM01 |
| Beschreibung: | 1 Online-Ressource (xxxv, 272 Seiten) Illustrationen, Diagramme |
| ISBN: | 9781000067200 9780429343988 |
Internformat
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| 245 | 1 | 0 | |a It's all analytics! |b the foundations of Al, big data, and data science landscape for professionals in healthcare, business, and government |c Scott Burk, Gary D. Miner |
| 250 | |a First edition | ||
| 264 | 1 | |a Boca Raton ; London ; New York |b CRC Press |c 2020 | |
| 264 | 4 | |c © 2021 | |
| 300 | |a 1 Online-Ressource (xxxv, 272 Seiten) |b Illustrationen, Diagramme | ||
| 336 | |b txt |2 rdacontent | ||
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| 505 | 8 | |a Cover -- Half Title -- Title Page -- Copyright Page -- Table of Contents -- Foreword Number One -- Foreword Number Two -- Foreword Number Three -- Preface -- Endorsements -- Authors -- Chapter 1 You Need This Book -- Preamble -- The Hip, the Hype, the Fears, the Intrigue, and the Reality: -- Hype, Fear, and Intrigue No 1: -- Hype, Fear, and Intrigue No 2: -- Hype, Fear, and Intrigue No 3: -- Professionals Need This Book -- Introduction -- Technology Keeps Raging, but We Need More Than Technology to Be Successful -- Data and Analytics Explosion -- A Bright Side of the Revolution -- Where Is Someone to Turn for Information? -- The Problem, Too Many Self-Interests: The Need for an Objective View -- There Are Many Other Professional Stories That Are Concerned about Whether Analytics Is Important -- Here Are a Few More Examples -- What This Book Is Not: -- Why This Book? -- Sure, Business, but Why Healthcare, Public Policy, and Business? -- How This Book Is Organized -- References -- Resources for the Avid Learner -- Chapter 2 Building a Successful Program -- Preamble -- The Hip, the Hype, the Fears, the Intrigue, and the Reality -- The Hype -- Reality -- The Hype -- Reality -- The Hype -- Reality -- Introduction -- Culture and Organization - Gaps and Limitations -- Gaps in Analytics Programs -- Characterizing Common Problems -- Don't Confuse Organizational Gaps for Project Gaps -- Justifying a Data-Driven Organization -- Motivations -- Critical Business Events -- Analytics as a Winning Strategy -- Part I - New Programs and Technologies -- Part II - More Traditional Methods of Justification -- Positive Return of Investment -- Scale -- Productivity -- Reliability -- Sustainability -- Designing the Organization for Program Success -- Motivation / Communication and Commitment -- Establish Clear Business Outcomes -- Organization Structure and Design | |
| 505 | 8 | |a The Organization and Its Goals - Alignment -- Organizational Structure -- Centralized Analytics -- Decentralized or Embedded Analytics -- Multidisciplinary Roles for Analytics -- Data Scientists -- Data Engineers -- Citizen Data Scientists -- Developers -- Business Experts -- Business Leaders -- Project Managers -- Analytics Oversight Committee (AOC) and Governance Committee (Board Report) -- Postscript -- References -- Resources for the Avid Learner -- Chapter 3 Some Fundamentals - Process, Data, and Models -- Preamble -- The Hip, the Hype, the Fears, the Intrigue, and the Reality -- The Hype -- Reality -- Introduction -- Framework for Analytics - Some Fundamentals -- Processes Drive Data -- Models, Methods, and Algorithms -- Models, Models, Models -- Statistical Models -- Rules of Thumb, Heuristic Models -- A Note on Cognition -- Algorithms, Algorithms, Algorithms -- Distinction between Methods That Generate Models -- There Is No Free Lunch -- A Process Methodology for Analytics -- CRISP-DM: The Six Phases: -- Last Considerations -- Data Architecture -- Analytics Architecture -- Postscript -- References -- Resources for the Avid Learner -- Chapter 4 It's All Analytics! -- Preamble -- Overview of Analytics - It's All Analytics -- Analytics of Every Form and Analytics Everywhere -- Introduction -- Analytics Mega List -- Breaking it Down, Categorizing Analytics -- Introduction -- Gartner's Classification -- Descriptive Analytics -- Diagnostic Analytics -- Predictive Analytics -- Prescriptive Analytics -- Process Optimization -- Some Additional Thoughts on Classifying Analytics -- Fundamentals of Analytics - Data Basics -- Introduction -- Four Scales of Measurement -- Data Formats -- Data Stores -- Provisioning Data for Analytics -- Data Sourcing -- Data Quality Assessment and Remediation -- Integrate and Repeat -- Exploratory Data Analysis (EDA) | |
| 505 | 8 | |a Data Transformations -- Data Reduction -- Postscript -- References -- Resources for the Avid Learner -- Chapter 5 What Are Business Intelligence (BI) and Visual BI? -- Preamble -- Introduction -- Background and Chronology -- Basic (Digital) Reporting -- A View inside the Data Warehouse and Interactive BI -- Beyond the Data Warehouse and Enhanced Interactive Visual BI and More -- Business Activity Monitoring an Alert-Based BI, Version 4.0 -- Strengths and Weaknesses of BI -- Transparency and Single Version of the Truth -- Summary -- Postscript -- References -- Resources for the Avid Learner -- Chapter 6 What Are Machine Learning and Data Mining? -- Preamble -- Overview of Machine Learning and Data Mining -- Is There a Difference? -- A (Brief) Historical Perspective of Data Mining and Machine Learning -- What Types of Analytics Are Covered by Machine Learning? -- An Overview of Problem Types and Common Ground -- The BIG Three! -- Regression -- Classification -- Natural Language Processing (NLP) -- Some (of Many) Additional Problem Classes -- Association, Rules and Recommender Systems -- Clustering -- Some Comments on Model Types -- Some Popular Machine Learning Algorithm Classes -- Trees 1.0: Classification and Regression Trees or Partition Trees -- Trees 2.0: Advanced Trees: Boosted Trees and Random Forests, for Classification and Regression -- Regression Model Trees and Cubist Models -- Logistic and Constrained/Penalized (LASSO, Ridge, Elastic Net) Regression -- Multivariate Adaptive Regression Splines -- Support Vector Machines (SVMs) -- Neural Networks in 1000 Flavors -- K-Means and Other Clustering Algorithms -- Directed Acyclic Graph Analytics (Optimization, Social Networks) -- Association Rules -- AutoML (Automated Machine Learning) -- Transparency and Processing Time of Algorithms -- Model Use and Deployment | |
| 505 | 8 | |a Major Components of the Machine Learning Process -- Advantages and Limitations of Using Machine Learning -- Postscript -- References -- Resources for the Avid Learner -- Chapter 7 AI (Artificial Intelligence) and How It Differs from Machine Learning -- Preamble -- Introduction -- Let Us Outline Two Types of AI Here - Weak AI and Strong AI -- AI Background and Chronology -- Short History of Digital AI -- Resurrection in the 1980s -- Beyond the Second AI Winter -- Deep Learning, Bigger, and New Data -- Next-Generation AI -- Differences of BI, Data Mining, Machine Learning, Statistics vs AI -- Strengths and Weakness -- Some Weaknesses of AI -- AI's Future -- "How 'Rosy' is the FUTURE for AI?" -- Postscript -- References -- Resources for the Avid Learner -- Chapter 8 What Is Data Science? -- Preamble -- Introduction -- Mushing All the Terms - Same Thing? -- Today's Data Science? -- Data Science vs BI and Data Scientist -- Data Science vs Data Engineering vs Citizen Data Scientist -- Backgrounds of Data Analytics Professionals -- Young Professionals' Input on What Makes a Great Data Scientist -- Summary -- Postscript -- References -- Resources for the Avid Learner -- Chapter 9 Big Data and Bigger Data, Little Data, Cloud, and Other Data -- Preamble -- Introduction -- Three Popular Forms and Two Divisions of Data -- What Is Big Data? -- Why the Push to Big Data? Why Is Big Data Technology Attractive? -- The Hype of Big Data -- Pivotal Changes in Big Data Technology -- Brief Notes on Cloud -- "Not Big Data" Is Alive and Well and Lessons from the Swamp -- A Brief Note on Subjective and Synthetic Data -- Other Important Data Focuses of Today and Tomorrow -- Data Virtualization (DV) -- Streaming Data -- Events (Event-Driven or Event Data) -- Geospatial -- IoT (Internet of Things) -- High-Performance In-Memory Computing Beyond Spark -- Grid and GPU Computing | |
| 505 | 8 | |a Near-Memory Computing -- Data Fabric -- Future Careers in Data -- Postscript -- References -- For the Avid Learner -- Chapter 10 Statistics, Causation, and Prescriptive Analytics -- Preamble -- Some Statistical Foundations -- Introduction -- Two Major Divisions of Statistics - Descriptive Statistics and Inferential Statistics -- What Made Statistics Famous? -- Criminal Trials and Hypothesis Testing -- The Scientific Method -- Two Major Paradigms of Statistics -- Bayesian Statistics -- Classical or Frequentist Statistics -- Dividing It Up - Assumption Heavy and Assumption Light Statistics -- Non-Parametric and Distribution Free Statistics (Assumption Light) -- Four Domains in Statistics to Mention -- Statistics in Predictive Analytics -- Design of Experiments (DoE) -- Statistical Process Control (SPC) -- Time Series -- An Ever-Important Reminder -- Statistics Summary -- Advantages of Statistics vs BI, Machine Learning and AI -- Disadvantages of Statistics vs BI, Machine Learning and AI -- Comparison of Data-Driven Paradigms Thus Far -- Business Intelligence (BI) -- Machine Learning and Data Mining -- Artificial Intelligence (AI) -- Statistics -- Predictive Analytics vs Prescriptive Analytics - The Missing Link Is Causation -- Assuming or Establishing Causation -- Ladder of Causation -- Predicting an Increasing Trend - Structural Causal Models and Causal Inference -- Summary -- Postscript -- References -- Resources for the Avid Learner -- Chapter 11 Other Disciplines to Dive in Deeper: Computer Science, Management/Decision Science, Operations Research, Engineering (and More) -- Preamble -- Introduction -- Computer Science -- Management Science -- Decision Science -- Operations Research -- Engineering -- Finance and Econometrics -- Simulation, Sensitivity and Scenario Analysis -- Sensitivity Analysis -- Scenario Analysis -- Systems Thinking | |
| 505 | 8 | |a Postscript | |
| 650 | 4 | |a Artificial intelligence | |
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Datensatz im Suchindex
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| author | Burk, Scott ca. 20./21. Jh Miner, Gary 1942- |
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| author_facet | Burk, Scott ca. 20./21. Jh Miner, Gary 1942- |
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| bvnumber | BV047441677 |
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| contents | Cover -- Half Title -- Title Page -- Copyright Page -- Table of Contents -- Foreword Number One -- Foreword Number Two -- Foreword Number Three -- Preface -- Endorsements -- Authors -- Chapter 1 You Need This Book -- Preamble -- The Hip, the Hype, the Fears, the Intrigue, and the Reality: -- Hype, Fear, and Intrigue No 1: -- Hype, Fear, and Intrigue No 2: -- Hype, Fear, and Intrigue No 3: -- Professionals Need This Book -- Introduction -- Technology Keeps Raging, but We Need More Than Technology to Be Successful -- Data and Analytics Explosion -- A Bright Side of the Revolution -- Where Is Someone to Turn for Information? -- The Problem, Too Many Self-Interests: The Need for an Objective View -- There Are Many Other Professional Stories That Are Concerned about Whether Analytics Is Important -- Here Are a Few More Examples -- What This Book Is Not: -- Why This Book? -- Sure, Business, but Why Healthcare, Public Policy, and Business? -- How This Book Is Organized -- References -- Resources for the Avid Learner -- Chapter 2 Building a Successful Program -- Preamble -- The Hip, the Hype, the Fears, the Intrigue, and the Reality -- The Hype -- Reality -- The Hype -- Reality -- The Hype -- Reality -- Introduction -- Culture and Organization - Gaps and Limitations -- Gaps in Analytics Programs -- Characterizing Common Problems -- Don't Confuse Organizational Gaps for Project Gaps -- Justifying a Data-Driven Organization -- Motivations -- Critical Business Events -- Analytics as a Winning Strategy -- Part I - New Programs and Technologies -- Part II - More Traditional Methods of Justification -- Positive Return of Investment -- Scale -- Productivity -- Reliability -- Sustainability -- Designing the Organization for Program Success -- Motivation / Communication and Commitment -- Establish Clear Business Outcomes -- Organization Structure and Design The Organization and Its Goals - Alignment -- Organizational Structure -- Centralized Analytics -- Decentralized or Embedded Analytics -- Multidisciplinary Roles for Analytics -- Data Scientists -- Data Engineers -- Citizen Data Scientists -- Developers -- Business Experts -- Business Leaders -- Project Managers -- Analytics Oversight Committee (AOC) and Governance Committee (Board Report) -- Postscript -- References -- Resources for the Avid Learner -- Chapter 3 Some Fundamentals - Process, Data, and Models -- Preamble -- The Hip, the Hype, the Fears, the Intrigue, and the Reality -- The Hype -- Reality -- Introduction -- Framework for Analytics - Some Fundamentals -- Processes Drive Data -- Models, Methods, and Algorithms -- Models, Models, Models -- Statistical Models -- Rules of Thumb, Heuristic Models -- A Note on Cognition -- Algorithms, Algorithms, Algorithms -- Distinction between Methods That Generate Models -- There Is No Free Lunch -- A Process Methodology for Analytics -- CRISP-DM: The Six Phases: -- Last Considerations -- Data Architecture -- Analytics Architecture -- Postscript -- References -- Resources for the Avid Learner -- Chapter 4 It's All Analytics! -- Preamble -- Overview of Analytics - It's All Analytics -- Analytics of Every Form and Analytics Everywhere -- Introduction -- Analytics Mega List -- Breaking it Down, Categorizing Analytics -- Introduction -- Gartner's Classification -- Descriptive Analytics -- Diagnostic Analytics -- Predictive Analytics -- Prescriptive Analytics -- Process Optimization -- Some Additional Thoughts on Classifying Analytics -- Fundamentals of Analytics - Data Basics -- Introduction -- Four Scales of Measurement -- Data Formats -- Data Stores -- Provisioning Data for Analytics -- Data Sourcing -- Data Quality Assessment and Remediation -- Integrate and Repeat -- Exploratory Data Analysis (EDA) Data Transformations -- Data Reduction -- Postscript -- References -- Resources for the Avid Learner -- Chapter 5 What Are Business Intelligence (BI) and Visual BI? -- Preamble -- Introduction -- Background and Chronology -- Basic (Digital) Reporting -- A View inside the Data Warehouse and Interactive BI -- Beyond the Data Warehouse and Enhanced Interactive Visual BI and More -- Business Activity Monitoring an Alert-Based BI, Version 4.0 -- Strengths and Weaknesses of BI -- Transparency and Single Version of the Truth -- Summary -- Postscript -- References -- Resources for the Avid Learner -- Chapter 6 What Are Machine Learning and Data Mining? -- Preamble -- Overview of Machine Learning and Data Mining -- Is There a Difference? -- A (Brief) Historical Perspective of Data Mining and Machine Learning -- What Types of Analytics Are Covered by Machine Learning? -- An Overview of Problem Types and Common Ground -- The BIG Three! -- Regression -- Classification -- Natural Language Processing (NLP) -- Some (of Many) Additional Problem Classes -- Association, Rules and Recommender Systems -- Clustering -- Some Comments on Model Types -- Some Popular Machine Learning Algorithm Classes -- Trees 1.0: Classification and Regression Trees or Partition Trees -- Trees 2.0: Advanced Trees: Boosted Trees and Random Forests, for Classification and Regression -- Regression Model Trees and Cubist Models -- Logistic and Constrained/Penalized (LASSO, Ridge, Elastic Net) Regression -- Multivariate Adaptive Regression Splines -- Support Vector Machines (SVMs) -- Neural Networks in 1000 Flavors -- K-Means and Other Clustering Algorithms -- Directed Acyclic Graph Analytics (Optimization, Social Networks) -- Association Rules -- AutoML (Automated Machine Learning) -- Transparency and Processing Time of Algorithms -- Model Use and Deployment Major Components of the Machine Learning Process -- Advantages and Limitations of Using Machine Learning -- Postscript -- References -- Resources for the Avid Learner -- Chapter 7 AI (Artificial Intelligence) and How It Differs from Machine Learning -- Preamble -- Introduction -- Let Us Outline Two Types of AI Here - Weak AI and Strong AI -- AI Background and Chronology -- Short History of Digital AI -- Resurrection in the 1980s -- Beyond the Second AI Winter -- Deep Learning, Bigger, and New Data -- Next-Generation AI -- Differences of BI, Data Mining, Machine Learning, Statistics vs AI -- Strengths and Weakness -- Some Weaknesses of AI -- AI's Future -- "How 'Rosy' is the FUTURE for AI?" -- Postscript -- References -- Resources for the Avid Learner -- Chapter 8 What Is Data Science? -- Preamble -- Introduction -- Mushing All the Terms - Same Thing? -- Today's Data Science? -- Data Science vs BI and Data Scientist -- Data Science vs Data Engineering vs Citizen Data Scientist -- Backgrounds of Data Analytics Professionals -- Young Professionals' Input on What Makes a Great Data Scientist -- Summary -- Postscript -- References -- Resources for the Avid Learner -- Chapter 9 Big Data and Bigger Data, Little Data, Cloud, and Other Data -- Preamble -- Introduction -- Three Popular Forms and Two Divisions of Data -- What Is Big Data? -- Why the Push to Big Data? Why Is Big Data Technology Attractive? -- The Hype of Big Data -- Pivotal Changes in Big Data Technology -- Brief Notes on Cloud -- "Not Big Data" Is Alive and Well and Lessons from the Swamp -- A Brief Note on Subjective and Synthetic Data -- Other Important Data Focuses of Today and Tomorrow -- Data Virtualization (DV) -- Streaming Data -- Events (Event-Driven or Event Data) -- Geospatial -- IoT (Internet of Things) -- High-Performance In-Memory Computing Beyond Spark -- Grid and GPU Computing Near-Memory Computing -- Data Fabric -- Future Careers in Data -- Postscript -- References -- For the Avid Learner -- Chapter 10 Statistics, Causation, and Prescriptive Analytics -- Preamble -- Some Statistical Foundations -- Introduction -- Two Major Divisions of Statistics - Descriptive Statistics and Inferential Statistics -- What Made Statistics Famous? -- Criminal Trials and Hypothesis Testing -- The Scientific Method -- Two Major Paradigms of Statistics -- Bayesian Statistics -- Classical or Frequentist Statistics -- Dividing It Up - Assumption Heavy and Assumption Light Statistics -- Non-Parametric and Distribution Free Statistics (Assumption Light) -- Four Domains in Statistics to Mention -- Statistics in Predictive Analytics -- Design of Experiments (DoE) -- Statistical Process Control (SPC) -- Time Series -- An Ever-Important Reminder -- Statistics Summary -- Advantages of Statistics vs BI, Machine Learning and AI -- Disadvantages of Statistics vs BI, Machine Learning and AI -- Comparison of Data-Driven Paradigms Thus Far -- Business Intelligence (BI) -- Machine Learning and Data Mining -- Artificial Intelligence (AI) -- Statistics -- Predictive Analytics vs Prescriptive Analytics - The Missing Link Is Causation -- Assuming or Establishing Causation -- Ladder of Causation -- Predicting an Increasing Trend - Structural Causal Models and Causal Inference -- Summary -- Postscript -- References -- Resources for the Avid Learner -- Chapter 11 Other Disciplines to Dive in Deeper: Computer Science, Management/Decision Science, Operations Research, Engineering (and More) -- Preamble -- Introduction -- Computer Science -- Management Science -- Decision Science -- Operations Research -- Engineering -- Finance and Econometrics -- Simulation, Sensitivity and Scenario Analysis -- Sensitivity Analysis -- Scenario Analysis -- Systems Thinking Postscript |
| ctrlnum | (ZDB-30-PQE)EBC6209045 (ZDB-30-PAD)EBC6209045 (ZDB-89-EBL)EBL6209045 (OCoLC)1156022004 (DE-599)BVBBV047441677 |
| dewey-full | 6.3 |
| dewey-hundreds | 000 - Computer science, information, general works |
| dewey-ones | 006 - Special computer methods |
| dewey-raw | 6.3 |
| dewey-search | 6.3 |
| dewey-sort | 16.3 |
| dewey-tens | 000 - Computer science, information, general works |
| discipline | Informatik |
| discipline_str_mv | Informatik |
| edition | First edition |
| format | Electronic eBook |
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Miner</subfield></datafield><datafield tag="250" ind1=" " ind2=" "><subfield code="a">First edition</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="a">Boca Raton ; London ; New York</subfield><subfield code="b">CRC Press</subfield><subfield code="c">2020</subfield></datafield><datafield tag="264" ind1=" " ind2="4"><subfield code="c">© 2021</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">1 Online-Ressource (xxxv, 272 Seiten)</subfield><subfield code="b">Illustrationen, 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">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">Cover -- Half Title -- Title Page -- Copyright Page -- Table of Contents -- Foreword Number One -- Foreword Number Two -- Foreword Number Three -- Preface -- Endorsements -- Authors -- Chapter 1 You Need This Book -- Preamble -- The Hip, the Hype, the Fears, the Intrigue, and the Reality: -- Hype, Fear, and Intrigue No 1: -- Hype, Fear, and Intrigue No 2: -- Hype, Fear, and Intrigue No 3: -- Professionals Need This Book -- Introduction -- Technology Keeps Raging, but We Need More Than Technology to Be Successful -- Data and Analytics Explosion -- A Bright Side of the Revolution -- Where Is Someone to Turn for Information? -- The Problem, Too Many Self-Interests: The Need for an Objective View -- There Are Many Other Professional Stories That Are Concerned about Whether Analytics Is Important -- Here Are a Few More Examples -- What This Book Is Not: -- Why This Book? -- Sure, Business, but Why Healthcare, Public Policy, and Business? -- How This Book Is Organized -- References -- Resources for the Avid Learner -- Chapter 2 Building a Successful Program -- Preamble -- The Hip, the Hype, the Fears, the Intrigue, and the Reality -- The Hype -- Reality -- The Hype -- Reality -- The Hype -- Reality -- Introduction -- Culture and Organization - Gaps and Limitations -- Gaps in Analytics Programs -- Characterizing Common Problems -- Don't Confuse Organizational Gaps for Project Gaps -- Justifying a Data-Driven Organization -- Motivations -- Critical Business Events -- Analytics as a Winning Strategy -- Part I - New Programs and Technologies -- Part II - More Traditional Methods of Justification -- Positive Return of Investment -- Scale -- Productivity -- Reliability -- Sustainability -- Designing the Organization for Program Success -- Motivation / Communication and Commitment -- Establish Clear Business Outcomes -- Organization Structure and Design</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">The Organization and Its Goals - Alignment -- Organizational Structure -- Centralized Analytics -- Decentralized or Embedded Analytics -- Multidisciplinary Roles for Analytics -- Data Scientists -- Data Engineers -- Citizen Data Scientists -- Developers -- Business Experts -- Business Leaders -- Project Managers -- Analytics Oversight Committee (AOC) and Governance Committee (Board Report) -- Postscript -- References -- Resources for the Avid Learner -- Chapter 3 Some Fundamentals - Process, Data, and Models -- Preamble -- The Hip, the Hype, the Fears, the Intrigue, and the Reality -- The Hype -- Reality -- Introduction -- Framework for Analytics - Some Fundamentals -- Processes Drive Data -- Models, Methods, and Algorithms -- Models, Models, Models -- Statistical Models -- Rules of Thumb, Heuristic Models -- A Note on Cognition -- Algorithms, Algorithms, Algorithms -- Distinction between Methods That Generate Models -- There Is No Free Lunch -- A Process Methodology for Analytics -- CRISP-DM: The Six Phases: -- Last Considerations -- Data Architecture -- Analytics Architecture -- Postscript -- References -- Resources for the Avid Learner -- Chapter 4 It's All Analytics! -- Preamble -- Overview of Analytics - It's All Analytics -- Analytics of Every Form and Analytics Everywhere -- Introduction -- Analytics Mega List -- Breaking it Down, Categorizing Analytics -- Introduction -- Gartner's Classification -- Descriptive Analytics -- Diagnostic Analytics -- Predictive Analytics -- Prescriptive Analytics -- Process Optimization -- Some Additional Thoughts on Classifying Analytics -- Fundamentals of Analytics - Data Basics -- Introduction -- Four Scales of Measurement -- Data Formats -- Data Stores -- Provisioning Data for Analytics -- Data Sourcing -- Data Quality Assessment and Remediation -- Integrate and Repeat -- Exploratory Data Analysis (EDA)</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">Data Transformations -- Data Reduction -- Postscript -- References -- Resources for the Avid Learner -- Chapter 5 What Are Business Intelligence (BI) and Visual BI? -- Preamble -- Introduction -- Background and Chronology -- Basic (Digital) Reporting -- A View inside the Data Warehouse and Interactive BI -- Beyond the Data Warehouse and Enhanced Interactive Visual BI and More -- Business Activity Monitoring an Alert-Based BI, Version 4.0 -- Strengths and Weaknesses of BI -- Transparency and Single Version of the Truth -- Summary -- Postscript -- References -- Resources for the Avid Learner -- Chapter 6 What Are Machine Learning and Data Mining? -- Preamble -- Overview of Machine Learning and Data Mining -- Is There a Difference? -- A (Brief) Historical Perspective of Data Mining and Machine Learning -- What Types of Analytics Are Covered by Machine Learning? -- An Overview of Problem Types and Common Ground -- The BIG Three! -- Regression -- Classification -- Natural Language Processing (NLP) -- Some (of Many) Additional Problem Classes -- Association, Rules and Recommender Systems -- Clustering -- Some Comments on Model Types -- Some Popular Machine Learning Algorithm Classes -- Trees 1.0: Classification and Regression Trees or Partition Trees -- Trees 2.0: Advanced Trees: Boosted Trees and Random Forests, for Classification and Regression -- Regression Model Trees and Cubist Models -- Logistic and Constrained/Penalized (LASSO, Ridge, Elastic Net) Regression -- Multivariate Adaptive Regression Splines -- Support Vector Machines (SVMs) -- Neural Networks in 1000 Flavors -- K-Means and Other Clustering Algorithms -- Directed Acyclic Graph Analytics (Optimization, Social Networks) -- Association Rules -- AutoML (Automated Machine Learning) -- Transparency and Processing Time of Algorithms -- Model Use and Deployment</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">Major Components of the Machine Learning Process -- Advantages and Limitations of Using Machine Learning -- Postscript -- References -- Resources for the Avid Learner -- Chapter 7 AI (Artificial Intelligence) and How It Differs from Machine Learning -- Preamble -- Introduction -- Let Us Outline Two Types of AI Here - Weak AI and Strong AI -- AI Background and Chronology -- Short History of Digital AI -- Resurrection in the 1980s -- Beyond the Second AI Winter -- Deep Learning, Bigger, and New Data -- Next-Generation AI -- Differences of BI, Data Mining, Machine Learning, Statistics vs AI -- Strengths and Weakness -- Some Weaknesses of AI -- AI's Future -- "How 'Rosy' is the FUTURE for AI?" -- Postscript -- References -- Resources for the Avid Learner -- Chapter 8 What Is Data Science? -- Preamble -- Introduction -- Mushing All the Terms - Same Thing? -- Today's Data Science? -- Data Science vs BI and Data Scientist -- Data Science vs Data Engineering vs Citizen Data Scientist -- Backgrounds of Data Analytics Professionals -- Young Professionals' Input on What Makes a Great Data Scientist -- Summary -- Postscript -- References -- Resources for the Avid Learner -- Chapter 9 Big Data and Bigger Data, Little Data, Cloud, and Other Data -- Preamble -- Introduction -- Three Popular Forms and Two Divisions of Data -- What Is Big Data? -- Why the Push to Big Data? Why Is Big Data Technology Attractive? -- The Hype of Big Data -- Pivotal Changes in Big Data Technology -- Brief Notes on Cloud -- "Not Big Data" Is Alive and Well and Lessons from the Swamp -- A Brief Note on Subjective and Synthetic Data -- Other Important Data Focuses of Today and Tomorrow -- Data Virtualization (DV) -- Streaming Data -- Events (Event-Driven or Event Data) -- Geospatial -- IoT (Internet of Things) -- High-Performance In-Memory Computing Beyond Spark -- Grid and GPU Computing</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">Near-Memory Computing -- Data Fabric -- Future Careers in Data -- Postscript -- References -- For the Avid Learner -- Chapter 10 Statistics, Causation, and Prescriptive Analytics -- Preamble -- Some Statistical Foundations -- Introduction -- Two Major Divisions of Statistics - Descriptive Statistics and Inferential Statistics -- What Made Statistics Famous? -- Criminal Trials and Hypothesis Testing -- The Scientific Method -- Two Major Paradigms of Statistics -- Bayesian Statistics -- Classical or Frequentist Statistics -- Dividing It Up - Assumption Heavy and Assumption Light Statistics -- Non-Parametric and Distribution Free Statistics (Assumption Light) -- Four Domains in Statistics to Mention -- Statistics in Predictive Analytics -- Design of Experiments (DoE) -- Statistical Process Control (SPC) -- Time Series -- An Ever-Important Reminder -- Statistics Summary -- Advantages of Statistics vs BI, Machine Learning and AI -- Disadvantages of Statistics vs BI, Machine Learning and AI -- Comparison of Data-Driven Paradigms Thus Far -- Business Intelligence (BI) -- Machine Learning and Data Mining -- Artificial Intelligence (AI) -- Statistics -- Predictive Analytics vs Prescriptive Analytics - The Missing Link Is Causation -- Assuming or Establishing Causation -- Ladder of Causation -- Predicting an Increasing Trend - Structural Causal Models and Causal Inference -- Summary -- Postscript -- References -- Resources for the Avid Learner -- Chapter 11 Other Disciplines to Dive in Deeper: Computer Science, Management/Decision Science, Operations Research, Engineering (and More) -- Preamble -- Introduction -- Computer Science -- Management Science -- Decision Science -- Operations Research -- Engineering -- Finance and Econometrics -- Simulation, Sensitivity and Scenario Analysis -- Sensitivity Analysis -- Scenario Analysis -- Systems Thinking</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">Postscript</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Artificial intelligence</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="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="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="a">Burk, Scott</subfield><subfield code="t">It's All Analytics!</subfield><subfield code="d">Milton : Productivity Press,c2020</subfield><subfield code="n">Druck-Ausgabe, Hardcover</subfield><subfield code="z">978-0-367-35968-3</subfield></datafield><datafield tag="776" ind1="0" ind2="8"><subfield code="i">Erscheint auch als</subfield><subfield code="n">Druck-Ausgabe, Paperback</subfield><subfield code="z">978-0-367-49379-0</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ZDB-30-PQE</subfield></datafield><datafield tag="999" ind1=" " ind2=" "><subfield code="a">oai:aleph.bib-bvb.de:BVB01-032843829</subfield></datafield><datafield tag="966" ind1="e" ind2=" "><subfield code="u">https://ebookcentral.proquest.com/lib/munchentech/detail.action?docID=6209045</subfield><subfield code="l">TUM01</subfield><subfield code="p">ZDB-30-PQE</subfield><subfield code="q">TUM_PDA_PQE_Kauf</subfield><subfield code="x">Aggregator</subfield><subfield code="3">Volltext</subfield></datafield></record></collection> |
| id | DE-604.BV047441677 |
| illustrated | Not Illustrated |
| index_date | 2024-07-03T18:01:23Z |
| indexdate | 2024-07-10T09:12:15Z |
| institution | BVB |
| isbn | 9781000067200 9780429343988 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-032843829 |
| oclc_num | 1156022004 |
| open_access_boolean | |
| owner | DE-91 DE-BY-TUM |
| owner_facet | DE-91 DE-BY-TUM |
| physical | 1 Online-Ressource (xxxv, 272 Seiten) Illustrationen, Diagramme |
| psigel | ZDB-30-PQE ZDB-30-PQE TUM_PDA_PQE_Kauf |
| publishDate | 2020 |
| publishDateSearch | 2020 |
| publishDateSort | 2020 |
| publisher | CRC Press |
| record_format | marc |
| spelling | Burk, Scott ca. 20./21. Jh. Verfasser (DE-588)1251791255 aut It's all analytics! the foundations of Al, big data, and data science landscape for professionals in healthcare, business, and government Scott Burk, Gary D. Miner First edition Boca Raton ; London ; New York CRC Press 2020 © 2021 1 Online-Ressource (xxxv, 272 Seiten) Illustrationen, Diagramme txt rdacontent c rdamedia cr rdacarrier Cover -- Half Title -- Title Page -- Copyright Page -- Table of Contents -- Foreword Number One -- Foreword Number Two -- Foreword Number Three -- Preface -- Endorsements -- Authors -- Chapter 1 You Need This Book -- Preamble -- The Hip, the Hype, the Fears, the Intrigue, and the Reality: -- Hype, Fear, and Intrigue No 1: -- Hype, Fear, and Intrigue No 2: -- Hype, Fear, and Intrigue No 3: -- Professionals Need This Book -- Introduction -- Technology Keeps Raging, but We Need More Than Technology to Be Successful -- Data and Analytics Explosion -- A Bright Side of the Revolution -- Where Is Someone to Turn for Information? -- The Problem, Too Many Self-Interests: The Need for an Objective View -- There Are Many Other Professional Stories That Are Concerned about Whether Analytics Is Important -- Here Are a Few More Examples -- What This Book Is Not: -- Why This Book? -- Sure, Business, but Why Healthcare, Public Policy, and Business? -- How This Book Is Organized -- References -- Resources for the Avid Learner -- Chapter 2 Building a Successful Program -- Preamble -- The Hip, the Hype, the Fears, the Intrigue, and the Reality -- The Hype -- Reality -- The Hype -- Reality -- The Hype -- Reality -- Introduction -- Culture and Organization - Gaps and Limitations -- Gaps in Analytics Programs -- Characterizing Common Problems -- Don't Confuse Organizational Gaps for Project Gaps -- Justifying a Data-Driven Organization -- Motivations -- Critical Business Events -- Analytics as a Winning Strategy -- Part I - New Programs and Technologies -- Part II - More Traditional Methods of Justification -- Positive Return of Investment -- Scale -- Productivity -- Reliability -- Sustainability -- Designing the Organization for Program Success -- Motivation / Communication and Commitment -- Establish Clear Business Outcomes -- Organization Structure and Design The Organization and Its Goals - Alignment -- Organizational Structure -- Centralized Analytics -- Decentralized or Embedded Analytics -- Multidisciplinary Roles for Analytics -- Data Scientists -- Data Engineers -- Citizen Data Scientists -- Developers -- Business Experts -- Business Leaders -- Project Managers -- Analytics Oversight Committee (AOC) and Governance Committee (Board Report) -- Postscript -- References -- Resources for the Avid Learner -- Chapter 3 Some Fundamentals - Process, Data, and Models -- Preamble -- The Hip, the Hype, the Fears, the Intrigue, and the Reality -- The Hype -- Reality -- Introduction -- Framework for Analytics - Some Fundamentals -- Processes Drive Data -- Models, Methods, and Algorithms -- Models, Models, Models -- Statistical Models -- Rules of Thumb, Heuristic Models -- A Note on Cognition -- Algorithms, Algorithms, Algorithms -- Distinction between Methods That Generate Models -- There Is No Free Lunch -- A Process Methodology for Analytics -- CRISP-DM: The Six Phases: -- Last Considerations -- Data Architecture -- Analytics Architecture -- Postscript -- References -- Resources for the Avid Learner -- Chapter 4 It's All Analytics! -- Preamble -- Overview of Analytics - It's All Analytics -- Analytics of Every Form and Analytics Everywhere -- Introduction -- Analytics Mega List -- Breaking it Down, Categorizing Analytics -- Introduction -- Gartner's Classification -- Descriptive Analytics -- Diagnostic Analytics -- Predictive Analytics -- Prescriptive Analytics -- Process Optimization -- Some Additional Thoughts on Classifying Analytics -- Fundamentals of Analytics - Data Basics -- Introduction -- Four Scales of Measurement -- Data Formats -- Data Stores -- Provisioning Data for Analytics -- Data Sourcing -- Data Quality Assessment and Remediation -- Integrate and Repeat -- Exploratory Data Analysis (EDA) Data Transformations -- Data Reduction -- Postscript -- References -- Resources for the Avid Learner -- Chapter 5 What Are Business Intelligence (BI) and Visual BI? -- Preamble -- Introduction -- Background and Chronology -- Basic (Digital) Reporting -- A View inside the Data Warehouse and Interactive BI -- Beyond the Data Warehouse and Enhanced Interactive Visual BI and More -- Business Activity Monitoring an Alert-Based BI, Version 4.0 -- Strengths and Weaknesses of BI -- Transparency and Single Version of the Truth -- Summary -- Postscript -- References -- Resources for the Avid Learner -- Chapter 6 What Are Machine Learning and Data Mining? -- Preamble -- Overview of Machine Learning and Data Mining -- Is There a Difference? -- A (Brief) Historical Perspective of Data Mining and Machine Learning -- What Types of Analytics Are Covered by Machine Learning? -- An Overview of Problem Types and Common Ground -- The BIG Three! -- Regression -- Classification -- Natural Language Processing (NLP) -- Some (of Many) Additional Problem Classes -- Association, Rules and Recommender Systems -- Clustering -- Some Comments on Model Types -- Some Popular Machine Learning Algorithm Classes -- Trees 1.0: Classification and Regression Trees or Partition Trees -- Trees 2.0: Advanced Trees: Boosted Trees and Random Forests, for Classification and Regression -- Regression Model Trees and Cubist Models -- Logistic and Constrained/Penalized (LASSO, Ridge, Elastic Net) Regression -- Multivariate Adaptive Regression Splines -- Support Vector Machines (SVMs) -- Neural Networks in 1000 Flavors -- K-Means and Other Clustering Algorithms -- Directed Acyclic Graph Analytics (Optimization, Social Networks) -- Association Rules -- AutoML (Automated Machine Learning) -- Transparency and Processing Time of Algorithms -- Model Use and Deployment Major Components of the Machine Learning Process -- Advantages and Limitations of Using Machine Learning -- Postscript -- References -- Resources for the Avid Learner -- Chapter 7 AI (Artificial Intelligence) and How It Differs from Machine Learning -- Preamble -- Introduction -- Let Us Outline Two Types of AI Here - Weak AI and Strong AI -- AI Background and Chronology -- Short History of Digital AI -- Resurrection in the 1980s -- Beyond the Second AI Winter -- Deep Learning, Bigger, and New Data -- Next-Generation AI -- Differences of BI, Data Mining, Machine Learning, Statistics vs AI -- Strengths and Weakness -- Some Weaknesses of AI -- AI's Future -- "How 'Rosy' is the FUTURE for AI?" -- Postscript -- References -- Resources for the Avid Learner -- Chapter 8 What Is Data Science? -- Preamble -- Introduction -- Mushing All the Terms - Same Thing? -- Today's Data Science? -- Data Science vs BI and Data Scientist -- Data Science vs Data Engineering vs Citizen Data Scientist -- Backgrounds of Data Analytics Professionals -- Young Professionals' Input on What Makes a Great Data Scientist -- Summary -- Postscript -- References -- Resources for the Avid Learner -- Chapter 9 Big Data and Bigger Data, Little Data, Cloud, and Other Data -- Preamble -- Introduction -- Three Popular Forms and Two Divisions of Data -- What Is Big Data? -- Why the Push to Big Data? Why Is Big Data Technology Attractive? -- The Hype of Big Data -- Pivotal Changes in Big Data Technology -- Brief Notes on Cloud -- "Not Big Data" Is Alive and Well and Lessons from the Swamp -- A Brief Note on Subjective and Synthetic Data -- Other Important Data Focuses of Today and Tomorrow -- Data Virtualization (DV) -- Streaming Data -- Events (Event-Driven or Event Data) -- Geospatial -- IoT (Internet of Things) -- High-Performance In-Memory Computing Beyond Spark -- Grid and GPU Computing Near-Memory Computing -- Data Fabric -- Future Careers in Data -- Postscript -- References -- For the Avid Learner -- Chapter 10 Statistics, Causation, and Prescriptive Analytics -- Preamble -- Some Statistical Foundations -- Introduction -- Two Major Divisions of Statistics - Descriptive Statistics and Inferential Statistics -- What Made Statistics Famous? -- Criminal Trials and Hypothesis Testing -- The Scientific Method -- Two Major Paradigms of Statistics -- Bayesian Statistics -- Classical or Frequentist Statistics -- Dividing It Up - Assumption Heavy and Assumption Light Statistics -- Non-Parametric and Distribution Free Statistics (Assumption Light) -- Four Domains in Statistics to Mention -- Statistics in Predictive Analytics -- Design of Experiments (DoE) -- Statistical Process Control (SPC) -- Time Series -- An Ever-Important Reminder -- Statistics Summary -- Advantages of Statistics vs BI, Machine Learning and AI -- Disadvantages of Statistics vs BI, Machine Learning and AI -- Comparison of Data-Driven Paradigms Thus Far -- Business Intelligence (BI) -- Machine Learning and Data Mining -- Artificial Intelligence (AI) -- Statistics -- Predictive Analytics vs Prescriptive Analytics - The Missing Link Is Causation -- Assuming or Establishing Causation -- Ladder of Causation -- Predicting an Increasing Trend - Structural Causal Models and Causal Inference -- Summary -- Postscript -- References -- Resources for the Avid Learner -- Chapter 11 Other Disciplines to Dive in Deeper: Computer Science, Management/Decision Science, Operations Research, Engineering (and More) -- Preamble -- Introduction -- Computer Science -- Management Science -- Decision Science -- Operations Research -- Engineering -- Finance and Econometrics -- Simulation, Sensitivity and Scenario Analysis -- Sensitivity Analysis -- Scenario Analysis -- Systems Thinking Postscript Artificial intelligence Data Science (DE-588)1140936166 gnd rswk-swf Maschinelles Lernen (DE-588)4193754-5 gnd rswk-swf Künstliche Intelligenz (DE-588)4033447-8 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 Burk, Scott It's All Analytics! Milton : Productivity Press,c2020 Druck-Ausgabe, Hardcover 978-0-367-35968-3 Erscheint auch als Druck-Ausgabe, Paperback 978-0-367-49379-0 |
| spellingShingle | Burk, Scott ca. 20./21. Jh Miner, Gary 1942- It's all analytics! the foundations of Al, big data, and data science landscape for professionals in healthcare, business, and government Cover -- Half Title -- Title Page -- Copyright Page -- Table of Contents -- Foreword Number One -- Foreword Number Two -- Foreword Number Three -- Preface -- Endorsements -- Authors -- Chapter 1 You Need This Book -- Preamble -- The Hip, the Hype, the Fears, the Intrigue, and the Reality: -- Hype, Fear, and Intrigue No 1: -- Hype, Fear, and Intrigue No 2: -- Hype, Fear, and Intrigue No 3: -- Professionals Need This Book -- Introduction -- Technology Keeps Raging, but We Need More Than Technology to Be Successful -- Data and Analytics Explosion -- A Bright Side of the Revolution -- Where Is Someone to Turn for Information? -- The Problem, Too Many Self-Interests: The Need for an Objective View -- There Are Many Other Professional Stories That Are Concerned about Whether Analytics Is Important -- Here Are a Few More Examples -- What This Book Is Not: -- Why This Book? -- Sure, Business, but Why Healthcare, Public Policy, and Business? -- How This Book Is Organized -- References -- Resources for the Avid Learner -- Chapter 2 Building a Successful Program -- Preamble -- The Hip, the Hype, the Fears, the Intrigue, and the Reality -- The Hype -- Reality -- The Hype -- Reality -- The Hype -- Reality -- Introduction -- Culture and Organization - Gaps and Limitations -- Gaps in Analytics Programs -- Characterizing Common Problems -- Don't Confuse Organizational Gaps for Project Gaps -- Justifying a Data-Driven Organization -- Motivations -- Critical Business Events -- Analytics as a Winning Strategy -- Part I - New Programs and Technologies -- Part II - More Traditional Methods of Justification -- Positive Return of Investment -- Scale -- Productivity -- Reliability -- Sustainability -- Designing the Organization for Program Success -- Motivation / Communication and Commitment -- Establish Clear Business Outcomes -- Organization Structure and Design The Organization and Its Goals - Alignment -- Organizational Structure -- Centralized Analytics -- Decentralized or Embedded Analytics -- Multidisciplinary Roles for Analytics -- Data Scientists -- Data Engineers -- Citizen Data Scientists -- Developers -- Business Experts -- Business Leaders -- Project Managers -- Analytics Oversight Committee (AOC) and Governance Committee (Board Report) -- Postscript -- References -- Resources for the Avid Learner -- Chapter 3 Some Fundamentals - Process, Data, and Models -- Preamble -- The Hip, the Hype, the Fears, the Intrigue, and the Reality -- The Hype -- Reality -- Introduction -- Framework for Analytics - Some Fundamentals -- Processes Drive Data -- Models, Methods, and Algorithms -- Models, Models, Models -- Statistical Models -- Rules of Thumb, Heuristic Models -- A Note on Cognition -- Algorithms, Algorithms, Algorithms -- Distinction between Methods That Generate Models -- There Is No Free Lunch -- A Process Methodology for Analytics -- CRISP-DM: The Six Phases: -- Last Considerations -- Data Architecture -- Analytics Architecture -- Postscript -- References -- Resources for the Avid Learner -- Chapter 4 It's All Analytics! -- Preamble -- Overview of Analytics - It's All Analytics -- Analytics of Every Form and Analytics Everywhere -- Introduction -- Analytics Mega List -- Breaking it Down, Categorizing Analytics -- Introduction -- Gartner's Classification -- Descriptive Analytics -- Diagnostic Analytics -- Predictive Analytics -- Prescriptive Analytics -- Process Optimization -- Some Additional Thoughts on Classifying Analytics -- Fundamentals of Analytics - Data Basics -- Introduction -- Four Scales of Measurement -- Data Formats -- Data Stores -- Provisioning Data for Analytics -- Data Sourcing -- Data Quality Assessment and Remediation -- Integrate and Repeat -- Exploratory Data Analysis (EDA) Data Transformations -- Data Reduction -- Postscript -- References -- Resources for the Avid Learner -- Chapter 5 What Are Business Intelligence (BI) and Visual BI? -- Preamble -- Introduction -- Background and Chronology -- Basic (Digital) Reporting -- A View inside the Data Warehouse and Interactive BI -- Beyond the Data Warehouse and Enhanced Interactive Visual BI and More -- Business Activity Monitoring an Alert-Based BI, Version 4.0 -- Strengths and Weaknesses of BI -- Transparency and Single Version of the Truth -- Summary -- Postscript -- References -- Resources for the Avid Learner -- Chapter 6 What Are Machine Learning and Data Mining? -- Preamble -- Overview of Machine Learning and Data Mining -- Is There a Difference? -- A (Brief) Historical Perspective of Data Mining and Machine Learning -- What Types of Analytics Are Covered by Machine Learning? -- An Overview of Problem Types and Common Ground -- The BIG Three! -- Regression -- Classification -- Natural Language Processing (NLP) -- Some (of Many) Additional Problem Classes -- Association, Rules and Recommender Systems -- Clustering -- Some Comments on Model Types -- Some Popular Machine Learning Algorithm Classes -- Trees 1.0: Classification and Regression Trees or Partition Trees -- Trees 2.0: Advanced Trees: Boosted Trees and Random Forests, for Classification and Regression -- Regression Model Trees and Cubist Models -- Logistic and Constrained/Penalized (LASSO, Ridge, Elastic Net) Regression -- Multivariate Adaptive Regression Splines -- Support Vector Machines (SVMs) -- Neural Networks in 1000 Flavors -- K-Means and Other Clustering Algorithms -- Directed Acyclic Graph Analytics (Optimization, Social Networks) -- Association Rules -- AutoML (Automated Machine Learning) -- Transparency and Processing Time of Algorithms -- Model Use and Deployment Major Components of the Machine Learning Process -- Advantages and Limitations of Using Machine Learning -- Postscript -- References -- Resources for the Avid Learner -- Chapter 7 AI (Artificial Intelligence) and How It Differs from Machine Learning -- Preamble -- Introduction -- Let Us Outline Two Types of AI Here - Weak AI and Strong AI -- AI Background and Chronology -- Short History of Digital AI -- Resurrection in the 1980s -- Beyond the Second AI Winter -- Deep Learning, Bigger, and New Data -- Next-Generation AI -- Differences of BI, Data Mining, Machine Learning, Statistics vs AI -- Strengths and Weakness -- Some Weaknesses of AI -- AI's Future -- "How 'Rosy' is the FUTURE for AI?" -- Postscript -- References -- Resources for the Avid Learner -- Chapter 8 What Is Data Science? -- Preamble -- Introduction -- Mushing All the Terms - Same Thing? -- Today's Data Science? -- Data Science vs BI and Data Scientist -- Data Science vs Data Engineering vs Citizen Data Scientist -- Backgrounds of Data Analytics Professionals -- Young Professionals' Input on What Makes a Great Data Scientist -- Summary -- Postscript -- References -- Resources for the Avid Learner -- Chapter 9 Big Data and Bigger Data, Little Data, Cloud, and Other Data -- Preamble -- Introduction -- Three Popular Forms and Two Divisions of Data -- What Is Big Data? -- Why the Push to Big Data? Why Is Big Data Technology Attractive? -- The Hype of Big Data -- Pivotal Changes in Big Data Technology -- Brief Notes on Cloud -- "Not Big Data" Is Alive and Well and Lessons from the Swamp -- A Brief Note on Subjective and Synthetic Data -- Other Important Data Focuses of Today and Tomorrow -- Data Virtualization (DV) -- Streaming Data -- Events (Event-Driven or Event Data) -- Geospatial -- IoT (Internet of Things) -- High-Performance In-Memory Computing Beyond Spark -- Grid and GPU Computing Near-Memory Computing -- Data Fabric -- Future Careers in Data -- Postscript -- References -- For the Avid Learner -- Chapter 10 Statistics, Causation, and Prescriptive Analytics -- Preamble -- Some Statistical Foundations -- Introduction -- Two Major Divisions of Statistics - Descriptive Statistics and Inferential Statistics -- What Made Statistics Famous? -- Criminal Trials and Hypothesis Testing -- The Scientific Method -- Two Major Paradigms of Statistics -- Bayesian Statistics -- Classical or Frequentist Statistics -- Dividing It Up - Assumption Heavy and Assumption Light Statistics -- Non-Parametric and Distribution Free Statistics (Assumption Light) -- Four Domains in Statistics to Mention -- Statistics in Predictive Analytics -- Design of Experiments (DoE) -- Statistical Process Control (SPC) -- Time Series -- An Ever-Important Reminder -- Statistics Summary -- Advantages of Statistics vs BI, Machine Learning and AI -- Disadvantages of Statistics vs BI, Machine Learning and AI -- Comparison of Data-Driven Paradigms Thus Far -- Business Intelligence (BI) -- Machine Learning and Data Mining -- Artificial Intelligence (AI) -- Statistics -- Predictive Analytics vs Prescriptive Analytics - The Missing Link Is Causation -- Assuming or Establishing Causation -- Ladder of Causation -- Predicting an Increasing Trend - Structural Causal Models and Causal Inference -- Summary -- Postscript -- References -- Resources for the Avid Learner -- Chapter 11 Other Disciplines to Dive in Deeper: Computer Science, Management/Decision Science, Operations Research, Engineering (and More) -- Preamble -- Introduction -- Computer Science -- Management Science -- Decision Science -- Operations Research -- Engineering -- Finance and Econometrics -- Simulation, Sensitivity and Scenario Analysis -- Sensitivity Analysis -- Scenario Analysis -- Systems Thinking Postscript Artificial intelligence Data Science (DE-588)1140936166 gnd Maschinelles Lernen (DE-588)4193754-5 gnd Künstliche Intelligenz (DE-588)4033447-8 gnd |
| subject_GND | (DE-588)1140936166 (DE-588)4193754-5 (DE-588)4033447-8 |
| title | It's all analytics! the foundations of Al, big data, and data science landscape for professionals in healthcare, business, and government |
| title_auth | It's all analytics! the foundations of Al, big data, and data science landscape for professionals in healthcare, business, and government |
| title_exact_search | It's all analytics! the foundations of Al, big data, and data science landscape for professionals in healthcare, business, and government |
| title_exact_search_txtP | It's all analytics! the foundations of Al, big data, and data science landscape for professionals in healthcare, business, and government |
| title_full | It's all analytics! the foundations of Al, big data, and data science landscape for professionals in healthcare, business, and government Scott Burk, Gary D. Miner |
| title_fullStr | It's all analytics! the foundations of Al, big data, and data science landscape for professionals in healthcare, business, and government Scott Burk, Gary D. Miner |
| title_full_unstemmed | It's all analytics! the foundations of Al, big data, and data science landscape for professionals in healthcare, business, and government Scott Burk, Gary D. Miner |
| title_short | It's all analytics! |
| title_sort | it s all analytics the foundations of al big data and data science landscape for professionals in healthcare business and government |
| title_sub | the foundations of Al, big data, and data science landscape for professionals in healthcare, business, and government |
| topic | Artificial intelligence Data Science (DE-588)1140936166 gnd Maschinelles Lernen (DE-588)4193754-5 gnd Künstliche Intelligenz (DE-588)4033447-8 gnd |
| topic_facet | Artificial intelligence Data Science Maschinelles Lernen Künstliche Intelligenz |
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