Data Management & Engineering
- 00. Mind map
- 01. What is DM&E
- 02. Data Models Basics
- 02. Foundations - Data Models
- 02.Various Data Models
- 03. Basic metrics for data overlap
- 03. Complex Data Overlap Metrics
- 03. Data discovery
- 03. Data Lakes
- 03. Data Lakes Architecture
- 03. Largest Relational Solutions
- 03. Local-Sensitive Hashing
- 03. Relational Algebra VS SQL
- 04. Active learning
- 04. Competent Crowd Worker Systems
- 04. Data is the Bottleneck
- 04. Interpretable Models
- 04. Least confidence (in highest prediction)
- 04. Margin Sampling (margin between highest 2 prob. is closest)
- 04. Other strategies
- 04. Uncertainty sampling
- 05. DATA ISSUES
- 05. Data mining
- 05. Data quality
- 05. Data types
- 05. Different types of features
- 05. Error detection
- 05. Error repair techniques
- 05. Functional Dependencies
- 05. Integrity Constraints
- 05. Min or Max operation
- 05. Missing Values
- 05. Outliers
- 05. Simple Ratio
- 06. Atomic data type detection
- 06. Constraints using first-order logic
- 06. Data Integration
- 06. Data integration approaches
- 06. Data Integration is difficult
- 06. Data Integration Tools
- 06. Data Matching
- 06. E - Data Lakes
- 06. Element level techniques
- 06. Q - Inner, outer, left, union
- 06. Query reformulation
- 06. Schema mapping and merging
- 06. Schema matching approaches
- 06. Semantic data type detection
- 06. Structure extraction from nested documents
- 06. Structure-based techniques
- 07. Storing and retrieving Data
- 10. Data Visualisation
- 99. Enhanced Entity Relationship Recap
- 4000. Data Management & Engineering
- Data profiling
- Relational Algebra