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JMP Connections: The Art of Utilizing Connections In Your Data - ISBN 9781119447757

JMP Connections: The Art of Utilizing Connections In Your Data

ISBN 9781119447757

Autor: John Wubbel

Wydawca: Wiley

Dostępność: 3-6 tygodni

Cena: 253,05 zł

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ISBN13:      

9781119447757

ISBN10:      

1119447755

Autor:      

John Wubbel

Oprawa:      

Hardback

Rok Wydania:      

2017-12-08

Ilość stron:      

240

Wymiary:      

235x161

Tematy:      

UX

UNLOCK THE FULL CAPABILITIES OF JMP® PRO SOFTWARE

JMP® Connections is your turnkey solution for quickly producing the actionable business intelligence from your organization′s raw data that results in high–performance decision making.

Interpreting data is the key to optimizing the everyday mechanics of business and unearthing the profitable opportunities unseen in the siloed areas of your company. This pioneering resource puts corporate leaders and data analysts on the same page with a transformative model for sophisticated analysis, including predictive modeling and cross–validation techniques. See your business operations with fresh eyes by:

Gaining proficiency in the most profitable business–oriented data discovery tool available from an industry leader in data analytics Easily making the leap from page to practice with ready–to–use formulas, up–to–date screenshots, and sample code Preparing yourself for what′s ahead with practical guidance from a case model illustrating the seven–section framework behind the JMP® Connections platform

The data of your business shows what needs to happen to run more efficiently, and JMP® Connections gives you everything you need to see it.



Preface xv

Chapter 1 Generalized Context for Decision Process Improvement 1

1.1 Situational Assessment (Current State) 3

1.2 Problem Statement 11

1.3 Visualizing State Transition 15

1.4 Metrics On–Demand 20

Chapter 2 Real–Time Metrics Business Case 25

2.1 Project Description and Objectives A Case Study 27

2.2 Solution Description 31

2.3 Cost and Benefit Analysis 34

2.4 Financial Assessment 37

2.5 Implementation Timeline 42

2.5.1 Contemplating Startup 42

2.5.2 Skills Dependencies and Timeline Consideration 44

2.5.3 Implementation Starting Point 46

2.5.4 Implementation to Deployment 49

2.6 Critical Assumptions and Risk Assessment 50

2.6.1 Critical Assumptions 50

2.6.2 Risk Assessment 51

2.7 Recommendations: Transmigrate the Enterprise 58

Chapter 3 Technical Details and Practical Implementation 63

3.1 Hardware Foundations 69

3.2 Solution Stack 70

3.3 Integration of Hardware and Software Infrastructure 72

3.4 Build Out 72

3.5 The Construction of a Metric 79

3.6 Metric Case Study 80

Chapter 4 Harvesting Benefits and Extensibility 99

4.1 Benefits Example 100

4.2 Extensibility 101

4.3 Configuration Management Version Control 102

Chapter 5 So What About a Bad Economy? 107

5.1 Overachievement Data Virtualization 110

5.2 JMP Connection as the Universal Server 114

5.3 Well–Formed Data 117

5.4 Linked Data 120

Chapter 6 Decision Streams 133

Chapter 7 Delivery and Presentations 139

7.1 Push Versus Pull Delivery 140

7.2 Presentation 143

7.3 DIY, But Leave the Poor Bi Person Alone! 156

7.4 Advanced Presentation Method 157

Chapter 8 In Closing (As–Built) 163

Glossary 169

Appendix A Server–Side PHP Code 173

Appendix B JMP JSL Time Constant Learning Curve Script 175

Appendix C JMP GUI User Interface Code Example 181

Appendix D Resource Description Framework File Example 185

Appendix E Sample Hardware Requirements 191

Appendix F Early Warning Deliverable 193

Appendix G JMP PRO Connections: The Transversality of the Capability Maturity Model 203

G.1 Tangential Concept 204

G.2 Transversal Concept 205

G.3 Univariate to Multivariate Process Control 206

G.4 JMP Process Screening 208

G.5 Transversal Maturity Space in Relation to JMP PRO Features 210

G.6 Summary 212

References 213

Suggested Reading 217

Index 219



JOHN WUBBEL is a data science software engineer with a decade of experience using JMP® to manufacture products in the life science pharmaceutical industry. His most recent work includes using structured data in graph database systems for advanced analytics, as well as modeling and structuring data for artificial intelligence and machine learning.

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