Table of Contents
Preface
Part 1: Understanding Data Science and Its Foundations
1
Introducing Data Science
Data science, AI, and ML – what’s the difference?
The mathematical and statistical underpinnings of data science
Statistics and data science
What is statistics?
Descriptive and inferential statistics
Sampling strategies
Probability
Probability distribution
Conditional probability
Describing our samples
Measures of central tendency
Measures of dispersion
Degrees of freedom
Correlation, causation, and covariance
The shape of data
Probability distributions
Discrete probability distributions
Continuous probability distributions
Summary
2
Characterizing and Collecting Data
What are the key criteria to consider when evaluating datasets?
Data ...
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