(Ebook PDF) Smarter data science succeeding with enterprise grade data and ai projects 1st edition Cole Stryker, Neal Fishman ISBN 9781119693420 111969342X full chapters
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Product details:
ISBN 10: 111969342X
ISBN 13: 9781119693420
Author: Neal Fishman; Cole Stryker
Enterprise data and AI projects are often scattershot, underbaked, siloed, and not adaptable to predictable business changes. As a result, the vast majority fail. These expensive quagmires can be avoided, and this book explains precisely how.
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. Smarter Data Science 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.
Table of contents:
- CHAPTER 1: Climbing the AI Ladder
- Readying Data for AI
- Technology Focus Areas
- Taking the Ladder Rung by Rung
- Constantly Adapt to Retain Organizational Relevance
- Data-Based Reasoning Is Part and Parcel in the Modern Business
- Toward the AI-Centric Organization
- Summary
- CHAPTER 2: Framing Part I: Considerations for Organizations Using AI
- Data-Driven Decision-Making
- Democratizing Data and Data Science
- Aye, a Prerequisite: Organizing Data Must Be a Forethought
- Facilitating the Winds of Change: How Organized Data Facilitates Reaction Time
- Quae Quaestio (Question Everything)
- Summary
- CHAPTER 3: Framing Part II: Considerations for Working with Data and AI
- Personalizing the Data Experience for Every User
- Context Counts: Choosing the Right Way to Display Data
- Ethnography: Improving Understanding Through Specialized Data
- Data Governance and Data Quality
- Ontologies: A Means for Encapsulating Knowledge
- Fairness, Trust, and Transparency in AI Outcomes
- Accessible, Accurate, Curated, and Organized
- Summary
- CHAPTER 4: A Look Back on Analytics: More Than One Hammer
- Been Here Before: Reviewing the Enterprise Data Warehouse
- Drawbacks of the Traditional Data Warehouse
- Paradigm Shift
- Modern Analytical Environments: The Data Lake
- Elements of the Data Lake
- The New Normal: Big Data Is Now Normal Data
- Schema-on-Read vs. Schema-on-Write
- Summary
- CHAPTER 5: A Look Forward on Analytics: Not Everything Can Be a Nail
- A Need for Organization
- Data Topologies
- Expanding, Adding, Moving, and Removing Zones
- Enabling the Zones
- Summary
- CHAPTER 6: Addressing Operational Disciplines on the AI Ladder
- A Passage of Time
- Create
- Execute
- Operate
- The xOps Trifecta: DevOps/MLOps, DataOps, and AIOps
- Summary
- CHAPTER 7: Maximizing the Use of Your Data: Being Value Driven
- Toward a Value Chain
- Curation
- Data Governance
- Integrated Data Management
- Summary
- 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
- 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
- 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
- Appendix: Glossary of Terms
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Smarter Data Science,Enterprise Grade Data,AI Projects
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