Cole Stryker - Smarter Data Science
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- Название:Smarter Data Science
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Data science is emerging as a hands-on tool for not just data scientists, but business professionals as well. Managers, directors, IT leaders, and analysts must expand their use of data science capabilities for the organization to stay competitive.
helps them achieve their enterprise-grade data projects and AI goals. It serves as a guide to building a robust and comprehensive information architecture program that enables sustainable and scalable AI deployments.
When an organization manages its data effectively, its data science program becomes a fully scalable function that’s both prescriptive and repeatable. With an understanding of data science principles, practitioners are also empowered to lead their organizations in establishing and deploying viable AI. They employ the tools of machine learning, deep learning, and AI to extract greater value from data for the benefit of the enterprise.
By following a ladder framework that promotes prescriptive capabilities, organizations can make data science accessible to a range of team members, democratizing data science throughout the organization. Companies that collect, organize, and analyze data can move forward to additional data science achievements:
Improving time-to-value with infused AI models for common use cases Optimizing knowledge work and business processes Utilizing AI-based business intelligence and data visualization Establishing a data topology to support general or highly specialized needs Successfully completing AI projects in a predictable manner Coordinating the use of AI from any compute node. From inner edges to outer edges: cloud, fog, and mist computing When they climb the ladder presented in this book, businesspeople and data scientists alike will be able to improve and foster repeatable capabilities. They will have the knowledge to maximize their AI and data assets for the benefit of their organizations.
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14 CHAPTER 8: Valuing Data with Statistical Analysis and Enabling Meaningful Access Deriving Value: Managing Data as an Asset Accessibility to Data: Not All Users Are Equal Providing Self-Service to Data Access: The Importance of Adding Controls Ranking Datasets Using a Bottom-Up Approach for Data Governance How Various Industries Use Data and AI Benefiting from Statistics Summary
15 CHAPTER 9: Constructing for the Long-Term The Need to Change Habits: Avoiding Hard-Coding Extending the Value of Data Through AI Polyglot Persistence Benefiting from Data Literacy Summary
16 CHAPTER 10: A Journey's End: An IA for AI Development Efforts for AI Essential Elements: Cloud-Based Computing, Data, and Analytics Driving Action: Context, Content, and Decision-Makers Keep It Simple The Silo Is Dead; Long Live the Silo Taxonomy: Organizing Data Zones Capabilities for an Open Platform Summary
17 Appendix: Glossary of Terms
18 Index
19 End User License Agreement
List of Illustrations
1 Chapter 1Figure 1-1: The AI Ladder to achieve a full complement of data and analytics...Figure 1-2: The ladder is part of a repetitive climb to continual improvemen...Figure 1-3: Current state ⇦ future state ⇦ current stateFigure 1-4: Ends and means model
2 Chapter 2Figure 2-1: Trust matrixFigure 2-2: Breadth and depth sliversFigure 2-3: GradingFigure 2-4: Data and AI democratizationFigure 2-5: Recognizing that the ability to skillfully ask questions is the ...
3 Chapter 3Figure 3-1: Monitors at Mission Control Center for the International Space S...Figure 3-2: A closer viewFigure 3-3: Monitors in a hospital emergency roomFigure 3-4: An electrocardiogram pattern showing normal and abnormal heartbe...Figure 3-5: Data governanceFigure 3-6: An ontological modelFigure 3-7: InferenceFigure 3-8: Blood test results showing normalcy, part AFigure 3-9: Blood test results showing normalcy, part BFigure 3-10: Recognizing preconditions
4 Chapter 4Figure 4-1: Reviewing atomic dataFigure 4-2: Simplified information architecture for an EDWFigure 4-3: YARN architecture
5 Chapter 5Figure 5-1: Starter set zonesFigure 5-2: Consistent to ActiveFigure 5-3: Consistent to InactiveFigure 5-4: Consistent to CISCO_INACTIVE_INDFigure 5-5: Consistent to StatusFigure 5-6: Consistent to WorksheetsFigure 5-7: Consistent to Implied MeaningFigure 5-8: Consistent to MetadataFigure 5-9: Misrepresenting the nature of data governanceFigure 5-10: Core elements of a data topologyFigure 5-11: Primitive zone typesFigure 5-12: Data lakes, data ponds, and data puddles
6 Chapter 6Figure 6-1: ChallengesFigure 6-2: Seven practicesFigure 6-3: Building to an MVPFigure 6-4: DevOps shift-left approachFigure 6-5: Core capabilities for DevOps and MLOpsFigure 6-6: Identifying DataOps stakeholdersFigure 6-7: Building blocks for AIOps
7 Chapter 7Figure 7-1: Data to wisdomFigure 7-2: Dialing data governance
8 Chapter 8Figure 8-1: Data value chainFigure 8-2: SkewnessFigure 8-3: KurtosisFigure 8-4: Identifying outliersFigure 8-5: Gaussian distributionFigure 8-6: Gaussian histogram plotFigure 8-7: Gaussian distribution with low and high variance
9 Chapter 9Figure 9-1: Caesar's Entertainment operating company asset valuesFigure 9-2: What color are clouds?Figure 9-3: Digitization misses more data than is actually collected.
10 Chapter 10Figure 10-1: Cloud topographyFigure 10-2: Compute and storage capabilitiesFigure 10-3: Analytic intensityFigure 10-4: Communication flowsFigure 10-5: Flight paths for model executionFigure 10-6: Driving prediction, automation, and optimizationFigure 10-7: Transitive closure and access privilegesFigure 10-8: A proliferation of lines serves to highlight the need for line ...Figure 10-9: Taxonomic representationFigure 10-10: Virtualized data zones
Guide
1 Cover
2 Table of Contents
3 Begin Reading
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Praise For This Book
The authors have obviously explored the paths toward an efficient information architecture. There is value in learning from their experience. If you have responsibility for or influence over how your organization uses artificial intelligence you will find Smarter Data Science an invaluable read. It is noteworthy that the book is written with a sense of scope that lends to its credibility. So much written about AI technologies today seems to assume a technical vacuum. We are not all working in startups! We have legacy technology that needs to be considered. The authors have created an excellent resource that acknowledges that enterprise context is a nuanced and important problem. The ideas are presented in a logical and clear format that is suitable to the technologist as well as the businessperson.
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