Front matter (#96)

* preface python

* remove foreword

* added editors page

* fix appendix,references

* added py acks

* minor ed

* Update editors.md

add Lindsey bio

* Add joels bio

Co-authored-by: Lindsey Heagy <[email protected]>
Co-authored-by: Joel Ostblom <[email protected]>

Author: 3 people

source/_toc.yml

@@ -4,10 +4,12 @@ parts:
 - caption: Front Matter
   chapters:
     - file: preface-text.md
-    - file: foreword-text.md
+    #- file: foreword.md
     - file: acknowledgements.md
+    - file: acknowledgements-python.md
     - file: authors.md
-    - file: setup.md
+    - file: editors.md
+    #- file: setup.md
 - caption: Chapters
   numbered: 3
   chapters:
@@ -24,4 +26,4 @@ parts:
 - caption: Appendix
   chapters:
     - file: appendixA.md
-    - file: references.md
+    #- file: references.md

source/acknowledgements-python.md

@@ -0,0 +1,25 @@
+---
+jupytext:
+  cell_metadata_filter: -all
+  formats: py:percent,md:myst,ipynb
+  text_representation:
+    extension: .md
+    format_name: myst
+    format_version: 0.13
+    jupytext_version: 1.13.8
+kernelspec:
+  display_name: Python 3 (ipykernel)
+  language: python
+  name: python3
+---
+
+# Acknowledgments for the Python Edition
+
+We'd like to thank everyone that has contributed to the development of 
+[*Data Science: A First Introduction (Python Edition)*](https://ubc-dsci.github.io/introduction-to-datascience-python/).
+This is an open source Python translation of the original [*Data Science: A First Introduction*](https://datasciencebook.ca);
+the original focused on the R programming language. Both of these books are 
+used to teach DSCI 100, a new introductory data science course 
+at the University of British Columbia (UBC).
+
+We will finalize this acknowledgements section after the book is complete!

source/acknowledgements.md

@@ -13,7 +13,7 @@ kernelspec:
   name: python3
 ---
 
-# Acknowledgments -- TBD
+# Acknowledgments
 
 We'd like to thank everyone that has contributed to the development of 
 [*Data Science: A First Introduction*](https://datasciencebook.ca).

source/appendixA.md

@@ -13,9 +13,7 @@ kernelspec:
   name: python3
 ---
 
-# Appendix
-
-# Downloading files from JupyterHub {#appendixA}
+# Downloading files from JupyterHub 
 
 This section will help you
 save your work from a JupyterHub web-based platform to your own computer. 

source/authors.md

@@ -13,7 +13,7 @@ kernelspec:
   name: python3
 ---
 
-# About the authors -- TBD
+# About the authors
 
 **Tiffany Timbers** is an Assistant Professor of Teaching in the Department of
 Statistics and Co-Director for the Master of Data Science program (Vancouver

source/editors.md

@@ -0,0 +1,51 @@
+---
+jupytext:
+  cell_metadata_filter: -all
+  formats: py:percent,md:myst,ipynb
+  text_representation:
+    extension: .md
+    format_name: myst
+    format_version: 0.13
+    jupytext_version: 1.13.8
+kernelspec:
+  display_name: Python 3 (ipykernel)
+  language: python
+  name: python3
+---
+
+# About the editors of the Python Edition
+
+**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
+Broderick in the Computer Science and Artificial Intelligence Laboratory
+(CSAIL) and Institute for Data, Systems, and Society (IDSS) at MIT, a Ph.D.
+candidate under Jonathan How in the Laboratory for Information and Decision
+Systems (LIDS) at MIT, and before that he was in the Engineering Science
+program at the University of Toronto.
+
++++
+
+**Lindsey Heagy** is an Assistant Professor in the Department of Earth, Ocean, and Atmospheric 
+Sciences and director of the Geophysical Inversion Facility at the University of British Columbia. 
+Her research combines computational methods in numerical simulations, inversions, and machine 
+learning to answer questions about the subsurface of the Earth. Primary applications include 
+mineral exploration, carbon sequestration, groundwater and environmental studies. She 
+completed her BSc at the University of Alberta, her PhD at the University of British Columbia, 
+and held a Postdoctoral research position at the University of California Berkeley prior to 
+starting her current position at UBC. 
+
++++
+
+**Joel Ostblom** is an Assistant Professor of Teaching in the Department of
+Statistics at the University of British Columbia. 
+During his PhD, Joel developed a passion for data science and reproducibility
+through the development of quantitative image analysis pipelines for studying
+stem cell and developmental biology. He has since co-created or lead the
+development of several courses and workshops at the University of Toronto and
+is now an assistant professor of teaching in the statistics department at the
+University of British Columbia. Joel cares deeply about spreading data literacy
+and excitement over programmatic data analysis, which is reflected in his
+contributions to open source projects and data science learning resources. You
+can read more about Joel on his [personal page](https://joelostblom.com/).

source/preface-text.md

@@ -13,11 +13,16 @@ kernelspec:
   name: python3
 ---
 
-# Preface -- TBD
+# Preface
+
+```{index} data science, auditable, reproducible
+```
+
+
 
 This textbook aims to be an approachable introduction to the world of data science. 
-In this book, we define **data science** \index{data science!definition} as the process of generating
-insight from data through **reproducible** \index{reproducible} and **auditable** \index{auditable} processes. 
+In this book, we define **data science** as the process of generating
+insight from data through **reproducible** and **auditable** processes. 
 If you analyze some data and give your analysis to a friend or colleague, they should
 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
@@ -29,19 +34,17 @@ At a high level, in this book, you will learn how to
 (1) identify common problems in data science, and 
 (2) solve those problems with reproducible and auditable workflows.
 
-Figure \@ref(fig:img-chapter-overview) summarizes what you will learn in each chapter
-of this book.
-Throughout, you will learn how to use the R programming language [@Rlanguage] to perform
+{numref}`preface-overview-fig` summarizes what you will learn in each chapter
+of this book. Throughout, you will learn how to use the [Python programming language](https://www.python.org/) to perform
 all the tasks associated with data analysis. You will
-spend the first four chapters learning how to use R to load, clean, wrangle
+spend the first four chapters learning how to use Python to load, clean, wrangle
 (i.e., restructure the data into a usable format) and visualize data
 while answering descriptive and exploratory data analysis questions. In the next
 six chapters, you will learn how to answer predictive, exploratory, and inferential
 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)), 
-you will learn how to combine R code, formatted text, and images
+you will learn how to combine Python 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
 on your own computer. If you are reading this book as part of a course that you are
@@ -51,20 +54,26 @@ But if you are reading this independently, you may want to jump to these last th
 early before going on to make sure your computer is set up in such a way that you can
 try out the example code that we include throughout the book. 
 
-```{r img-chapter-overview, echo = FALSE, message = FALSE, warning = FALSE, fig.cap = "Where are we going?", out.width="100%", fig.retina = 2, fig.align = "center"}
-knitr::include_graphics("img/chapter_overview.jpeg")
+```{figure} img/chapter_overview.jpeg
+---
+height: 400px
+name: preface-overview-fig
+---
+Where are we going?
 ```
 
+
+
 Each chapter in the book has an accompanying worksheet that provides exercises
 to help you practice the concepts you will learn. We strongly recommend that you
 work through the worksheet when you finish reading each chapter 
 before moving on to the next chapter. All of the worksheets
 are available at 
-[https://github.com/UBC-DSCI/data-science-a-first-intro-worksheets#readme](https://github.com/UBC-DSCI/data-science-a-first-intro-worksheets#readme);
+[https://github.com/UBC-DSCI/data-science-a-first-intro-python-worksheets#readme](https://github.com/UBC-DSCI/data-science-a-first-intro-python-worksheets#readme);
 the "Exercises" section at the end of each chapter points you to the right worksheet for that chapter.
 For each worksheet, you can either launch an interactive version of the worksheet in your browser by clicking the "launch binder" button,
 or preview a non-interactive version of the worksheet by clicking "view worksheet."
 If you instead decide to download the worksheet and run it on your own machine,
 make sure to follow the instructions for computer setup
-found in Chapter \@ref(move-to-your-own-machine). This will ensure that the automated feedback
+found in the {ref}`move-to-your-own-machine` chapter. This will ensure that the automated feedback
 and guidance that the worksheets provide will function as intended.

source/references.md

@@ -13,6 +13,6 @@ kernelspec:
   name: python3
 ---
 
-`r if (knitr:::is_html_output()) '
-# References -- TBD
-'`
+# References 
+
+