Merge pull request #384 from UBC-DSCI/preamble

preamble copyedit pass

Author: trevorcampbell

acknowledgements.Rmd

@@ -7,12 +7,12 @@ for DSCI 100, a new introductory data science course
 at the University of British Columbia (UBC).
 Several faculty members in the UBC Department of Statistics 
 were pivotal in shaping the direction of that course, 
-and as such contributed greatly to the broad structure and 
+and as such, contributed greatly to the broad structure and 
 list of topics in this book. We would especially like to thank Matías
 Salibían-Barrera for his mentorship during the initial development and roll-out
 of both DSCI 100 and this book. His door was always open when
 we needed to chat about how to
-best introduce and teach data science our first year students.
+best introduce and teach data science to our first-year students.
 
 We also owe a debt of gratitude to all of the students of DSCI 100 over the past
 few years. They provided invaluable feedback on the book and worksheets; 

authors.Rmd

@@ -1,6 +1,6 @@
 # About the authors {-}
 
-Tiffany Timbers is an Assistant Professor of Teaching in the Department of
+**Tiffany Timbers** is an Assistant Professor of Teaching in the Department of
 Statistics and Co-Director for the Master of Data Science program (Vancouver
 Option) at the University of British Columbia. In these roles she teaches and
 develops curriculum around the responsible application of Data Science to solve
@@ -9,7 +9,7 @@ course on collaborative software development, which focuses on teaching how to
 create R and Python packages using modern tools and workflows.
 
 
-Trevor Campbell is an Assistant Professor in the Department of Statistics at
+**Trevor Campbell** is an Assistant Professor in the Department of Statistics at
 the University of British Columbia. His research focuses on automated, scalable
 Bayesian inference algorithms, Bayesian nonparametrics, streaming data, and
 Bayesian theory. He was previously a postdoctoral associate advised by Tamara
@@ -20,7 +20,7 @@ Systems (LIDS) at MIT, and before that he was in the Engineering Science
 program at the University of Toronto.
 
 
-Melissa Lee is an Assistant Professor of Teaching in the Department of
+**Melissa Lee** is an Assistant Professor of Teaching in the Department of
 Statistics at the University of British Columbia. She teaches and develops
 curriculum for undergraduate statistics and data science courses. Her work
 focuses on student-centered approaches to teaching, developing and assessing

preface-text.Rmd

@@ -15,7 +15,7 @@ If you analyze some data and give your analysis to a friend or colleague, they s
 be able to re-run the analysis from start to finish and get the same result you did (*reproducibility*).
 They should also be able to see and understand all the steps in the analysis, as well as the history of how
 the analysis developed (*auditability*). Creating reproducible and auditable 
-analyses allows both yourself and others to easily double-check and validate your work.
+analyses allows both you and others to easily double-check and validate your work.
 
 At a high level, in this book, you will learn how to 
 
@@ -33,7 +33,7 @@ six chapters, you will learn how to answer predictive, exploratory, and inferent
 data analysis questions with common methods in data science, including
 classification, regression, clustering, and estimation.
 In the final chapters 
-(\@ref(getting-started-with-jupyter) - \@ref(move-to-your-own-machine)), 
+(\@ref(getting-started-with-jupyter)–\@ref(move-to-your-own-machine)), 
 you will learn how to combine R code, formatted text, and images
 in a single coherent document with Jupyter, use version control for
 collaboration, and install and configure the software needed for data science