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| Title: | It's all analytics! the foundations of Al, big data, and data science landscape for professionals in healthcare, business, and government |
|---|---|
| From: |
Scott Burk, Gary D. Miner
|
| Person: |
Burk, Scott
ca. 20./21. Jh. Verfasser aut Miner, Gary 1942- |
| Main Authors: | , |
| Format: | Electronic eBook |
| Language: | English |
| Published: |
Boca Raton ; London ; New York
CRC Press
2020
|
| Edition: | First edition |
| Subjects: | |
| Online Access: | https://ebookcentral.proquest.com/lib/munchentech/detail.action?docID=6209045 |
| Physical Description: | 1 Online-Ressource (xxxv, 272 Seiten) Illustrationen, Diagramme |
| ISBN: | 9781000067200 9780429343988 |
Staff View
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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 | ||
| 337 | |b c |2 rdamedia | ||
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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 | |
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| 650 | 4 | |a Artificial intelligence | |
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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 |
| 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="943" ind1="1" 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">DE-91</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 | Illustrated |
| indexdate | 2024-12-20T19:19:40Z |
| 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 |
| 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_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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