revise introduction

01_Introduction.Rmd

@@ -28,33 +28,35 @@ The book's layout is currently in seven broad categories:
 
 ### Data Science 
 
-Combining chapters about the scope and definitions of data science (Chapter \@ref(datasciencedefined): This thing called "data science"), along with overviews of good research practice, from opinionated analysis design to script style (Chapter \@ref(practice): Statistical & Data Science Practice).
+* Combining chapters about the scope and definitions of data science **(Chapter \@ref(datasciencedefined): This thing called "data science")**, along with overviews of good research practice, from opinionated analysis design to script style **(Chapter \@ref(practice): Statistical & Data Science Practice)**, and some high-level materials on **[Using R]**.
 
 
 ### Data 
 
+* What is data science with data? This section covers topics from **[Bias]** to **[Anonymity and Confidentiality]**.
+
 
 
 ### Data Visualization
 
-* **Chapter \@ref(datavisualization): Data Visualization** points to some key resources on data visualization theory and practice, and data visualization in R, with a focus on the package `ggplot2` and its related extension packages.
+* The **Data Visualization** section points to some key resources on data visualization theory and practice, and data visualization in R, with a focus on the package **ggplot2** and its related extension packages.
 
 
 
 ### Quantitative Methods
 
 
-* **Chapter \@ref(textanalysis): Text Analysis & Text Mining** covers ways of tackling text mining, including natural language processing (NLP) and sentiment analysis.
+* This section covers a variety of methods of analyzing and modeling data, from **[Bayesian Methods]** (which, along with some other approaches, gets its own chapter) to **[Time series analysis]**.
 
 
 ### Spatial Data
 
-* Chapters on mapping and Small Area Estimation
+* Chapters on **[Spatial Data (including mapping)]** and **[Small Area Estimation]**.
 
 
 ### Other Methods
 
-Text analytics.
+* **Chapter \@ref(textanalysis): Text Analysis & Text Mining** covers ways of tackling text mining, including natural language processing (NLP) and sentiment analysis.
 
 
 
@@ -66,4 +68,6 @@ Text analytics.
 
 Style and format: see ["Software information and conventions"](https://bookdown.org/yihui/blogdown/software-info.html) in Xie, Thomas & Presmanes Hill [@Xie_etal_blogdown_2019]
 
--30-
+
+***
+

02_data_science_defined.Rmd

@@ -141,6 +141,5 @@ Danielle Navarro, [Learning Statistics with R: A tutorial for psychology student
 
 
 
-
--30-
+***
 

03_data_science_practice.Rmd

@@ -336,5 +336,5 @@ Louisa Smith , [epi quals study calendar](https://docs.google.com/spreadsheets/d
 
 
 
+***
 
--30-

04_data_science_tools.Rmd

@@ -1,4 +1,4 @@
-# Using R {#using_r}
+# Using R 
 
 
 
@@ -118,6 +118,5 @@ See the companion chapter on the topics of [Text Analysis and Text Mining].
 
 
 
+***
 
-
--30-

10_data_theory.rmd

@@ -36,4 +36,6 @@ Caroline Criado Perez (2019) _Invisible Women_ [@Perez_2019]
 * Angela Chen (2019-03-05) [A journalist explains the dangerous consequences of a world built for men](https://www.theverge.com/2019/3/5/18251570/caroline-criado-perez-invisible-women-data-bias-science-gender), [theverge.com]
 
 
+ ***
+
 

51_bayesian.Rmd

@@ -1,4 +1,4 @@
-# Bayesian Methods {#bayesian}
+# Bayesian Methods
 
 
 

60_spatial_data.Rmd

@@ -265,4 +265,5 @@ Corentin M. Barbu, 2015-11-06, ["tmap R package"](http://spatcontrol.net/Spatial
 Sarah Strochak, [How to Create State and County Maps Easily in R](https://medium.com/@urban_institute/how-to-create-state-and-county-maps-easily-in-r-577d29300bb2)
 
 
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+***
+

61_small_area_estimation.Rmd

@@ -1,4 +1,4 @@
-# Small Area Estimation (SAE)  {#sae}
+# Small Area Estimation  {#sae}
 
 ## Small area estimation (SAE)