Syllabus

The complete module-and-topic map of the book. This is a living syllabus — new modules and topics are added over time as the book grows from fundamentals toward advanced techniques. Click any module to open its overview, or jump straight to a numbered topic.

An Expanding Path · 10 Topics

IFundamentals of Data Analytics4 topics

Understanding Data

1Data and Its Types 2Classification and Tabulation of Data

Organizing Data

3Class Intervals and Grouping 4Frequency Distribution

IIDescriptive Statistics4 topics

Summarizing Data

5Measures of Central Tendency (incl. Weighted & Geometric Mean) 6Measures of Dispersion

Describing Distribution Shape

7Measures of Skewness 8Measures of Kurtosis

IIIProbability2 topics

Foundations of Probability

9Basic Probability Concepts 10Conditional Probability and Bayes' Theorem

More on the way. The next modules planned for this book pick up right where this one leaves off — probability distributions (Binomial, Poisson, Normal) and inferential statistics — followed by data visualization and hands-on work in R and Python. Since this book is never really “finished,” check back here for the latest topic map.