Data Simplification: Taming Information With Open Source Tools addressesthe simple fact that modern data is too big and complex to analyze in its native form. Data simplification is the process whereby large and complex data is rendered usable. Complex data must be simplified before it can be analyzed, but the process of data simplification is anything but simple, requiring a specialized set of skills and tools.
This book provides data scientists from every scientific discipline with the methods and tools to simplify their data for immediate analysis or long-term storage in a form that can be readily repurposed or integrated with other data.
Drawing upon years of practical experience, and using numerous examples and use cases, Jules Berman discusses the principles, methods, and tools that must be studied and mastered to achieve data simplification, open source tools, free utilities and snippets of code that can be reused and repurposed to simplify data, natural language processing and machine translation as a tool to simplify data, and data summarization and visualization and the role they play in making data useful for the end user.
- Discusses data simplification principles, methods, and tools that must be studied and mastered
- Provides open source tools, free utilities, and snippets of code that can be reused and repurposed to simplify data
- Explains how to best utilize indexes to search, retrieve, and analyze textual data
- Shows the data scientist how to apply ontologies, classifications, classes, properties, and instances to data using tried and true methods
1. The Simple Life 2. Structuring Text 3. Indexing Text 4. Understanding Your Data 5. Identifying and Deidentifying Data 6. Giving Meaning to Data 7. Object-oriented data 8. Problem simplification