How to Use This Book

Chapter Structure

Every chapter follows this structure:

Element	Purpose
Learning Objectives	What you will be able to do after this chapter
Theory sections	Intuitive explanations with key formulae in highlighted boxes
Section Review Questions	3–5 questions at the end of each section to consolidate understanding
Worked Examples	R and Python code with real data, explained line by line
Case Study	A full applied example using Nigerian or African data
Chapter Exercises	Up to 10 graduated exercises: recall → application → analysis
Further Reading	Pointers to deeper treatments for those who want more
Chapter Appendix	Full mathematical derivations for those who want the rigour

Reading Paths

If you are new to data analysis: Read every chapter in order, starting from Chapter 1. Do not skip Part 0 or Part I — they build the language and thinking patterns that everything else depends on.

If you have some statistics background: Skim Chapters 2–3 for notation and vocabulary, then start at Chapter 4.

If you are an experienced analyst: Use the Coverage Map in Appendix C to jump directly to the technique or application domain you need. Each applied chapter cross-references the methods chapters it depends on.

Using the Search Feature

Search requires a local server

The search box in the top navigation bar works when you view the book through a local web server. If you open _book/index.html directly as a file (i.e., the browser address bar shows file://...), search will be disabled — this is a browser security restriction.

To enable search, launch the book this way:

# In the project folder, run:
quarto preview

This starts a local server and opens the book in your browser. Search will work immediately.

Alternatively, within R:

servr::httd("_book")

Interactive vs. Print Versions

HTML and PDF Differences

This book is published in two formats. Some features differ:

Interactive plots (built with plotly) appear only in the HTML version. In the PDF, equivalent static figures are shown instead. Wherever an interactive plot exists, you will see a note: “Interactive version available in the HTML edition.”
Code fold/unfold buttons exist only in HTML. In the PDF, all code is shown in full.
Dark mode is available in the HTML version via the toggle in the top navigation bar.

Code Conventions

All code appears in tabbed panels:

# R code appears here
library(tidyverse)
df <- read_csv("data/example.csv")

# Python code appears here
import pandas as pd
df = pd.read_csv("data/example.csv")

Both implementations produce the same output. You do not need to know both languages to use this book — but working through both is strongly encouraged.

Callout Box Types

Throughout the book you will encounter several types of highlighted boxes:

📘 Theory

Blue boxes contain core theoretical concepts and key formulae.

🔑 Key Formula

Bordered formula boxes contain the most important equations, presented in a form ready to apply.

⚠️ Watch Out

Yellow boxes flag common errors, misconceptions, or pitfalls.

🌍 African Context

Green badges indicate case studies and datasets from Nigerian or African markets.

🧪 Review Questions

Purple boxes contain section-level review questions.

Exercises

End-of-chapter exercises are graduated:

Questions 1–3: Recall and comprehension — define, explain, describe
Questions 4–6: Application — use the technique on provided data
Questions 7–10: Analysis and extension — interpret results, compare methods, adapt to new contexts, or write working code

Answers to odd-numbered exercises will be made available on the companion website.

Mathematical Derivations

If you see a reference like “see the Chapter Appendix for the full derivation”, that means the complete algebraic proof is at the end of the chapter. You do not need to read it to use the technique. It is there for curious readers who want to understand not just what the formula does but where it comes from.

--- title: "How to Use This Book" number-sections: false --- # How to Use This Book {.unnumbered} ## Chapter Structure Every chapter follows this structure: | Element | Purpose | |---|---| | **Learning Objectives** | What you will be able to do after this chapter | | **Theory sections** | Intuitive explanations with key formulae in highlighted boxes | | **Section Review Questions** | 3–5 questions at the end of each section to consolidate understanding | | **Worked Examples** | R and Python code with real data, explained line by line | | **Case Study** | A full applied example using Nigerian or African data | | **Chapter Exercises** | Up to 10 graduated exercises: recall → application → analysis | | **Further Reading** | Pointers to deeper treatments for those who want more | | **Chapter Appendix** | Full mathematical derivations for those who want the rigour | ## Reading Paths **If you are new to data analysis:** Read every chapter in order, starting from Chapter 1. Do not skip Part 0 or Part I — they build the language and thinking patterns that everything else depends on. **If you have some statistics background:** Skim Chapters 2–3 for notation and vocabulary, then start at Chapter 4. **If you are an experienced analyst:** Use the Coverage Map in Appendix C to jump directly to the technique or application domain you need. Each applied chapter cross-references the methods chapters it depends on. ## Using the Search Feature ::: {.callout-important} ## Search requires a local server The **search box** in the top navigation bar works when you view the book through a local web server. If you open `_book/index.html` directly as a file (i.e., the browser address bar shows `file://...`), search will be disabled — this is a browser security restriction. **To enable search, launch the book this way:** ```bash # In the project folder, run: quarto preview ``` This starts a local server and opens the book in your browser. Search will work immediately. Alternatively, within R: ```r servr::httd("_book") ``` ::: ## Interactive vs. Print Versions ::: {.callout-note} ## HTML and PDF Differences This book is published in two formats. Some features differ: - **Interactive plots** (built with `plotly`) appear only in the HTML version. In the PDF, equivalent static figures are shown instead. Wherever an interactive plot exists, you will see a note: *"Interactive version available in the HTML edition."* - **Code fold/unfold buttons** exist only in HTML. In the PDF, all code is shown in full. - **Dark mode** is available in the HTML version via the toggle in the top navigation bar. ::: ## Code Conventions All code appears in tabbed panels: ::: {.panel-tabset} ## R ```r # R code appears here library(tidyverse) df <- read_csv("data/example.csv") ``` ## Python ```python # Python code appears here import pandas as pd df = pd.read_csv("data/example.csv") ``` ::: Both implementations produce the same output. You do not need to know both languages to use this book — but working through both is strongly encouraged. ## Callout Box Types Throughout the book you will encounter several types of highlighted boxes: ::: {.callout-note icon="false"} ## 📘 Theory Blue boxes contain core theoretical concepts and key formulae. ::: ::: {.callout-tip icon="false"} ## 🔑 Key Formula Bordered formula boxes contain the most important equations, presented in a form ready to apply. ::: ::: {.callout-important icon="false"} ## ⚠️ Watch Out Yellow boxes flag common errors, misconceptions, or pitfalls. ::: ::: {.callout-tip icon="false"} ## 🌍 African Context Green badges indicate case studies and datasets from Nigerian or African markets. ::: ::: {.callout-caution icon="false"} ## 🧪 Review Questions Purple boxes contain section-level review questions. ::: ## Exercises End-of-chapter exercises are graduated: - **Questions 1–3:** Recall and comprehension — define, explain, describe - **Questions 4–6:** Application — use the technique on provided data - **Questions 7–10:** Analysis and extension — interpret results, compare methods, adapt to new contexts, or write working code Answers to odd-numbered exercises will be made available on the companion website. ## Mathematical Derivations If you see a reference like *"see the Chapter Appendix for the full derivation"*, that means the complete algebraic proof is at the end of the chapter. You do not need to read it to use the technique. It is there for curious readers who want to understand not just *what* the formula does but *where it comes from*.