COURSE 02Beginner9 IN-DEPTH LESSONS

Data, Labels and Reality

How data, labels, distributions, and real-world annotations power models—and why messy data, leakage, and sampling skew create messy results.

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Course Syllabus & Units

Unit 1

Data

1 lessons
1.1L0

Data and labels

Most modern AI systems learn from **examples**. **Data** is those examples. **Labels** are the “answer keys” attached to some examples (spam/not spam, price, diagnosis code, next word). Messy data makes messy models. Biased labels replay...

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Unit 2

Datasets

1 lessons
2.1L0

Datasets and splits

A **dataset** is the collection of examples a model learns from. A **split** divides that collection into parts for training, checking, and final testing so you do not grade the model on the same homework it memorized.

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Unit 3

Quality

1 lessons
3.1L0

Data quality and bias in labels

**Labels** are the answers attached to examples (spam/not spam, cat/dog). **Quality** means those answers are consistent, timely, and fair enough for the job. Bias in labels becomes bias in models.

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Unit 4

Provenance

1 lessons
4.1L0

Collection, consent, and provenance

Where data came from, whether people agreed, and whether you can trace a record back to its source—**provenance**.

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Unit 5

Synthetic

1 lessons
5.1L0

Synthetic data basics

**Synthetic data** is generated by simulations or models rather than collected directly from the world. It can help privacy and coverage—or amplify junk.

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Unit 6

Leakage

1 lessons
6.1L0

Leakage case studies

**Leakage** is when information from outside the training-time-allowed view sneaks into features or splits—so scores look great and production fails.

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Unit 7

Docs

1 lessons
7.1L0

Documentation and datasheets

Structured docs for datasets and models—**datasheets**, model cards, and system cards—so others know intended use, limits, and risks.

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Unit 8

Loops

1 lessons
8.1L0

Feedback loops in data

When a model’s outputs change the world (or the logs), and those changes become tomorrow’s training data—**feedback loops**.

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Unit 9

Pii

1 lessons
9.1L0

PII in training data

Personally identifiable information accidentally (or carelessly) ending up in training sets, prompts, or logs—and how to minimize it.

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