Merge pull request #265 from UBC-DSCI/additional-resources-py

Additional resources conversion to Py

Author: trevorcampbell

source/classification2.md

@@ -2134,7 +2134,7 @@ and guidance that the worksheets provide will function as intended.
   two chapters, you'll learn about another kind of predictive modeling setting,
   so it might be worth visiting the website only after reading through those
   chapters. 
-- *An Introduction to Statistical Learning* {cite:p}`james2013introduction` provides 
+- [*An Introduction to Statistical Learning*](https://www.statlearning.com/) {cite:p}`james2013introduction` provides 
   a great next stop in the process of
   learning about classification. Chapter 4 discusses additional basic techniques
   for classification that we do not cover, such as logistic regression, linear

source/inference.md

@@ -1340,17 +1340,8 @@ and guidance that the worksheets provide will function as intended.
 
 ## Additional resources
 
-- Chapters 7 to 10 of *Modern Dive* {cite:p}`moderndive` provide a great
-  next step in learning about inference. In particular, Chapters 7 and 8 cover
-  sampling and bootstrapping using `tidyverse` and `infer` in a slightly more
-  in-depth manner than the present chapter. Chapters 9 and 10 take the next step
-  beyond the scope of this chapter and begin to provide some of the initial
-  mathematical underpinnings of inference and more advanced applications of the
-  concept of inference in testing hypotheses and performing regression. This
-  material offers a great starting point for getting more into the technical side
-  of statistics.
 - Chapters 4 to 7 of *OpenIntro Statistics* {cite:p}`openintro`
-  provide a good next step after *Modern Dive*. Although it is still certainly
+  provide a good next step in learning about inference. Although it is still certainly
   an introductory text, things get a bit more mathematical here. Depending on
   your background, you may actually want to start going through Chapters 1 to 3
   first, where you will learn some fundamental concepts in probability theory.

source/reading.md

@@ -1566,15 +1566,14 @@ and guidance that the worksheets provide will function as intended.
 ## Additional resources
 
 - The [`pandas` documentation](https://pandas.pydata.org/docs/getting_started/index.html)
-  provides the documentation for many of the reading functions we cover in this chapter.
-  It is where you should look if you want to learn more about the functions in this
-  chapter, the full set of arguments you can use, and other related functions.
-  The site also provides a very nice cheat sheet that summarizes many of the data
-  wrangling functions from this chapter.
-- Sometimes you might run into data in such poor shape that none of the reading
-  functions we cover in this chapter work. In that case, you can consult the
-  [data loading chapter](https://wesmckinney.com/book/accessing-data.html#io_flat_files) from *Python for Data Analysis* {cite:p}`mckinney2012python`, which goes into a lot more detail about how Python parses
-  text from files into data frames.
+  provides the documentation for the functions we cover in this chapter.
+  It is where you should look if you want to learn more about these functions, the
+  full set of arguments you can use, and other related functions.
+- Sometimes you might run into data in such poor shape that the reading
+  functions we cover in this chapter do not work. In that case, you can consult the
+  [data loading chapter](https://wesmckinney.com/book/accessing-data.html#io_flat_files)
+  from [*Python for Data Analysis*](https://wesmckinney.com/book/) {cite:p}`mckinney2012python`, which goes into a lot
+  more detail about how Python parses text from files into data frames.
 - A [video](https://www.youtube.com/embed/ephId3mYu9o) from the Udacity
   course *Linux Command Line Basics* provides a good explanation of absolute versus relative paths.
 - If you read the subsection on obtaining data from the web via scraping and

source/regression2.md

@@ -1357,14 +1357,6 @@ and guidance that the worksheets provide will function as intended.
   useful [tutorials](https://scikit-learn.org/stable/tutorial/index.html) and [an extensive list
   of more advanced examples](https://scikit-learn.org/stable/auto_examples/index.html#general-examples)
   that you can use to continue learning beyond the scope of this book.
-- *Modern Dive* {cite:p}`moderndive` is another textbook that uses the
-  `tidyverse` / `tidymodels` framework. Chapter 6 complements the material in
-  the current chapter well; it covers some slightly more advanced concepts than
-  we do without getting mathematical. Give this chapter a read before moving on
-  to the next reference. It is also worth noting that this book takes a more
-  "explanatory" / "inferential" approach to regression in general (in Chapters 5,
-  6, and 10), which provides a nice complement to the predictive tack we take in
-  the present book.
 - *An Introduction to Statistical Learning* {cite:p}`james2013introduction` provides
   a great next stop in the process of
   learning about regression. Chapter 3 covers linear regression at a slightly

source/version-control.md

@@ -1170,7 +1170,7 @@ you can expand your knowledge through the resources listed below:
   learning about Git and GitHub.
 - [Good enough practices in scientific
   computing](https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1005510#sec014)
-  [@wilson2014best] provides more advice on useful workflows and "good enough"
+  {cite:p}`wilson2014best` provides more advice on useful workflows and "good enough"
   practices in data analysis projects.
 - In addition to [GitHub](https://github.com), there are other popular Git
   repository hosting services such as [GitLab](https://gitlab.com) and
@@ -1181,3 +1181,11 @@ you can expand your knowledge through the resources listed below:
   token](https://docs.github.com/en/authentication/keeping-your-account-and-data-secure/creating-a-personal-access-token)
   is an excellent additional resource to consult if you need help
   generating and using personal access tokens.
+
+## References
+
++++
+
+```{bibliography}
+:filter: docname in docnames
+```

source/viz.md

@@ -2025,17 +2025,19 @@ and guidance that the worksheets provide will function as intended.
 
 ## Additional resources
 
-- The [altair documentation](https://altair-viz.github.io/){cite:p}`altair` is
+- The [altair documentation](https://altair-viz.github.io/) {cite:p}`altair` is
   where you should look if you want to learn more about the functions in this
   chapter, the full set of arguments you can use, and other related functions.
   The site also provides a very nice cheat sheet that summarizes many of the data
   wrangling functions from this chapter.
-- The *Fundamentals of Data Visualization* {cite:p}`wilkeviz` has
+- The [*Fundamentals of Data Visualization*](https://clauswilke.com/dataviz/) {cite:p}`wilkeviz` has
   a wealth of information on designing effective visualizations. It is not
   specific to any particular programming language or library. If you want to
   improve your visualization skills, this is the next place to look.
-- The [dates and times](https://wesmckinney.com/book/time-series.html){cite:p}`mckinney2012python`
-  chapter is where you should look if you want to learn about `date` and `time`, including how to create them, and how to use them to effectively handle durations, etc
+- The [dates and times](https://wesmckinney.com/book/time-series.html) chapter
+  of [*Python for Data Analysis*](https://wesmckinney.com/book/) {cite:p}`mckinney2012python`
+  is where you should look if you want to learn about `date` and `time`, including
+  how to create them, and how to use them to effectively handle durations, etc
 
 ## References
 

source/wrangling.md

@@ -1900,9 +1900,7 @@ and guidance that the worksheets provide will function as intended.
 - The [`pandas` package documentation](https://pandas.pydata.org/docs/reference/index.html) is
   another resource to learn more about the functions in this
   chapter, the full set of arguments you can use, and other related functions.
-  The site also provides a very nice cheat sheet that summarizes many of the
-  data wrangling functions from this chapter.
-- *Python for Data Analysis* {cite:p}`mckinney2012python` has a few chapters related to
+- [*Python for Data Analysis*](https://wesmckinney.com/book/) {cite:p}`mckinney2012python` has a few chapters related to
   data wrangling that go into more depth than this book. For example, the
   [data wrangling chapter](https://wesmckinney.com/book/data-wrangling.html) covers tidy data,
   `melt` and `pivot`, but also covers missing values
@@ -1911,8 +1909,7 @@ and guidance that the worksheets provide will function as intended.
   `groupby`, aggregating functions, `apply`, etc.
 - You will occasionally encounter a case where you need to iterate over items
   in a data frame, but none of the above functions are flexible enough to do
-  what you want. In that case, you may consider using [a for
-  loop](https://wesmckinney.com/book/python-basics.html#control_for) {cite:p}`mckinney2012python`.
+  what you want. In that case, you may consider using [a for loop](https://wesmckinney.com/book/python-basics.html#control_for) {cite:p}`mckinney2012python`.
 
 
 ## References