About this module
Before statistics, define the records.
We will inspect what one row represents, how keys and timestamps preserve ownership, and why missing information cannot silently become zero.
A correct formula cannot rescue an ambiguous data grain or a historical record that was not available at decision time.
Lessons
- Lesson 21
Observations, Entities, Variables, and Datasets
Three payment rows are not three customers
2:38 - Lesson 22
Numeric, Categorical, Ordinal, and Binary Variables
A risk grade is ordered, but not a measured distance
2:27 - Lesson 23
Population, Sample, Census, and Sampling Frame
A sample of approved loans cannot represent all applications
2:34 - Lesson 24
Cross-Sectional, Time-Series, Panel, and Event Data
Customer histories and payment events have different grains
2:43 - Lesson 25
Identifiers, Keys, Joins, and Data Grain
A duplicate join can double the apparent money
2:37 - Lesson 26
Timestamps, Time Zones, Calendars, and Observation Time
Different clock strings can describe the same instant
2:38 - Lesson 27
Missing, Nonfinite, Censored, and Truncated Values
Unknown, nonfinite, censored, and absent from the sample
2:40 - Lesson 28
Measurement Error, Resolution, Accuracy, and Precision
A clock can be consistent and still wrong
2:38 - Lesson 29
Revisions, Vintages, and Point-in-Time Availability
Backtesting with the version that actually existed
2:35 - Lesson 30
Data Provenance, Lineage, Ownership, and Licensing
A useful feature needs a traceable origin
2:36