How deeply does Grokking AI Algorithms, Second Edition cover data?

Defines data types, distinguishes data from information, and follows collection and preparation without detailing storage internals.

Data

As explained in Grokking AI Algorithms, Second Edition

Data is recorded information, observations, measurements, or signals that can serve as inputs for processing and action.

Data is recorded facts, observations, or measurements that can be processed and acted on. Sights, sounds, temperatures, and transaction records all count. For AI, data is like fuel: an algorithm cannot make useful decisions if its inputs are missing, unsuitable, or poorly represented. section 1.1 section 1.3

Data can take different forms:

Quantitative data records numerical measurements or counts, such as temperature or age in days. section 1.3
Qualitative data records observations or judgments, such as a smell or a movie review. Its meaning may depend on the observer. section 1.3

From a reading to a decision

A patient's temperature of 38°C is data. Interpreting that reading as a fever produces information. Deciding to administer medication draws on that information and experience: knowledge. The same recorded fact can support different interpretations depending on the question and context. section 1.3 section 2.4

For machine learning, data is commonly arranged in a table: each row is an example and each column is a feature. Apartment listings, for instance, can pair location, room count, and garage access with monthly rent. Comparing listings reveals patterns that help estimate the rent of a new apartment. Larger, more complex datasets make collecting and understanding the right features especially important. section 8.1 section 8.7

Table 8.1 The diamond dataset

	Carat	Cut	Color	Clarity	Depth	Table	Price	X	Y	Z
1	0.30	Good	J	SI1	64.0	55	339	4.25	4.28	2.73
2	0.41	Ideal	I	SI1	61.7	55	561	4.77	4.80	2.95
3	0.75	Very good	D	SI1	63.2	56	2,760	5.80	5.75	3.65
4	0.91	Fair	H	SI2	65.7	60	2,763	6.03	5.99	3.95
5	1.20	Fair	F	I1	64.6	56	2,809	6.73	6.66	4.33
6	1.31	Premium	J	SI2	59.7	59	3,697	7.06	7.01	4.20
7	1.50	Premium	H	I1	62.9	60	4,022	7.31	7.22	4.57
8	1.74	Very good	H	I1	63.2	55	4,677	7.62	7.59	4.80
9	1.96	Fair	I	I1	66.8	55	6,147	7.62	7.60	5.08
10	2.21	Premium	H	I1	62.2	58	6,535	8.31	8.27	5.16

The diamond dataset makes the table structure concrete: rows describe individual diamonds, while columns record features such as carat, cut, dimensions, and sale price. section 8.7

Choosing data is part of solving the problem, not just feeding an algorithm. First decide what question matters, then collect relevant data, understand what its fields mean, clean and format it, and test whether the resulting model helps. Results may send you back to revise the data. Even accurate measurements can mislead when selected in a biased way. section 8.6 section 1.3

How Grokking AI Algorithms, Second Edition covers it

Cover of Grokking AI Algorithms, Second Edition

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Related concepts

machine learningused by · Machine learning is a field of computational methods in which systems learn patterns or relationships from data to make predictions, decisions, or other outputs.
unlabeled dataincludes · Unlabeled data consists of examples that lack target labels or task-specific annotations.
labeled dataincludes · Labeled data is a set of examples or observations paired with target values, categories, or other task-relevant annotations.
data analysisused by · Data analysis is the systematic examination, transformation, and interpretation of data to derive information, identify patterns, or support conclusions and decisions.

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