Table of Contents
1.1 Simplification Drives Scientific Progress
1.2 The Human Mind is a Simplifying Machine
2.1 The Meaninglessness of Free Text
2.2 Sorting Text, the Impossible Dream
2.5 Annotation and the Simple Science of Metadata
2.6 Specifications Good, Standards Bad
3.1 How Data Scientists Use Indexes
3.2 Concordances and Indexed Lists
3.3 Term Extraction and Simple Indexes
3.4 Autoencoding and Indexing with Nomenclatures
3.5 Computational Operations on Indexes
Chapter 4: Understanding Your Data
4.2 Simple Statistical Descriptors
4.3 Retrieving Image Information
Chapter 5: Identifying and Deidentifying Data
5.2 Poor Identifiers, Horrific Consequences
5.3 Deidentifiers and Reidentifiers
5.5 Data Encryption and Authentication
5.6 Timestamps, Signatures, and Event Identifiers
Chapter 6: Giving Meaning to Data
6.2 Driving Down Complexity with Classifications
6.3 Driving Up Complexity With Ontologies
6.4 The Unreasonable Effectiveness of Classifications
6.5 Properties That Cross Multiple Classes
Chapter 7: Object-Oriented Data
7.1 The Importance of Self-Explaining Data
7.2 Introspection and Reflection
7.3 Object-Oriented Data Objects
7.4 Working with Object-Oriented Data
Chapter 8: Problem Simplification
8.3 Resampling and Permutating
8.4 Verification, Validation, and Reanalysis