.config/config.org

@@ -310,6 +310,8 @@
   ;; (setq *mdp-dir* (concat *root-dir* "mdp"))
 
   (defun include-slide (overlay)
+    (unless overlay
+      (setq overlay "<1>"))
     (by-backend
      ('latex
       (let ((id (org-entry-get nil "custom_id" t)))
@@ -393,6 +395,7 @@
 #+macro: MO-data [[MO-data][(MO3)]]
 #+macro: MO-processing [[MO-processing][(MO4)]]
 #+macro: MO-vis [[MO-vis][(MO5)]]
+#+macro: MO-crit [[MO-crit][(MO6)]]
 
 # Macros for comments
 #+macro: jamie \jamie{$1}

mdp/01.org

@@ -68,7 +68,7 @@ By the end of this topic, you should be able to:
    different contexts {{{MO-methods}}}
 
 6. articulate the importance of understanding visual perception in
-   making visualisation design decisions {{{MO-vis}}}.
+   making visualisation design decisions {{{MO-crit}}}.
 
 ** Topic description
 

mdp/05.org

@@ -54,13 +54,13 @@ By the end of this topic, you should be able to:
    {{{MO-data}}}
 
 4. apply the three-stage information processing model when analysing
-   effective visual communication {{{MO-vis}}}
+   effective visual communication {{{MO-crit}}}
 
 5. justify visual design decisions in terms of the visual system's
-   sensitive to relative difference rather than absolute values {{{MO-vis}}}
+   sensitive to relative difference rather than absolute values {{{MO-crit}}}
 
 6. demonstrate an awareness of colour blindness and seek to mitigate
-   against ambiguity when making design decisions {{{MO-vis}}}
+   against ambiguity when making design decisions {{{MO-crit}}}
 
 7. select appropriate colourmaps for different kinds of data {{{MO-vis}}}.
 

mdp/dv-mdp.org

@@ -117,29 +117,31 @@
 
 #+latex: \addcontentsline{toc}{chapter}{Module information}
 
-** Module description
+** Module rationale
 
-#+latex: \addcontentsline{toc}{section}{Module description}
+#+latex: \addcontentsline{toc}{section}{Module rationale}
 
 Visualisation is essential for understanding and communicating
-information. This module takes the view that data visualisation is a
-core interdisciplinary component of data science. Effective
-visualisation requires a combination of computational skills,
-statistical knowledge, an understanding of human visual perception,
-and a rigorous and creative approach to working with data. You will
-learn practical skills for manipulating and visualising data, as well
-as theoretical knowledge essential for making judgements about how to
-most effectively discover and communicate insights from data.
+information, and for making informed decisions based on data. This
+module takes the view that data visualisation is a core
+interdisciplinary component of data science. Effective visualisation
+requires a combination of computational skills, statistical knowledge,
+an understanding of human visual perception, and a rigorous and
+creative approach to working with data. This module will cover both
+the practical skills necessary for manipulating and visualising data,
+as well as the theoretical knowledge essential for making judgements
+about how to most effectively discover and communicate insights from
+data.
 
 ** Module goals
 
 #+latex: \addcontentsline{toc}{section}{Module goals}
 
-The module goals are to become familiar with core visualisation tools
-and techniques within the Python data science ecosystem. You will
-establish a critical approach to data visualisation, starting with
-interrogating how data is collected, continuing throughout each stage
-of the visualisation process culminating in the communication of
+The module will enable you to become familiar with core visualisation
+tools and techniques within the Python data science ecosystem. You
+will establish a critical approach to data visualisation, starting
+with interrogating how data is collected, continuing throughout each
+stage of the visualisation process culminating in the communication of
 information and possible real-world implications. You will gain
 practice answering questions with data and build confidence in
 presenting your findings, ensuring they are trustworthy and valid. You
@@ -155,19 +157,24 @@ By the end of this module, you should be able to:
 1. <<MO-tools>> identify, install, and configure the necessary
    components of a standard Python-based data visualisation toolkit
 
-2. <<MO-methods>> articulate the role and practices of data
-   visualisation within the broader context of data science
+2. <<MO-methods>> based on independent research and analysis, design
+   and execute data-driven investigations demonstrating understanding
+   of the role and practices of data visualisation within the broader
+   context of data analysis and data science
 
 3. <<MO-data>> critically evaluate and compare different sources of
    data
 
-4. <<MO-processing>> apply data gathering techniques to obtain and
-   prepare data for visualisation, with due regard to legal issues
+4. <<MO-processing>> select and apply data gathering techniques to
+   obtain and prepare data for visualisation, with due regard to legal
+   issues
 
 5. <<MO-vis>> select and apply data visualisation techniques to
-   generate and communicate information, and make effective
-   visualisation decisions taking into account ethical and social
-   issues, principles of good design, and knowledge of human visual
+   generate and communicate information
+
+6. <<MO-crit>> critically evaluate different forms of visual
+   communication taking into account ethical and social issues,
+   principles of good design, and knowledge of human visual
    perception.
 
 ** Module reading lists

topics/03.org

@@ -2575,17 +2575,6 @@ TBC
 |  5 | female    | Instagram;Twitter          |
 |  6 | male      | Facebook;Twitter           |
 
-#+results:
-| id | gender    | use_services               |
-|----+-----------+----------------------------|
-|  0 | female    | Facebook                   |
-|  1 | male      | PixelFed;Mastodon          |
-|  2 | female    | Facebook;Twitter           |
-|  3 | nonbinary | Mastodon                   |
-|  4 | female    | Friendica;Write.as;Twitter |
-|  5 | female    | Instagram;Twitter          |
-|  6 | male      | Facebook;Twitter           |
-
 ****** Split strings                                              :B_onlyenv:
 :PROPERTIES:
 :BEAMER_env: onlyenv

topics/05.org

@@ -236,7 +236,8 @@ during your analysis.
 #+end_src
 
 #+results:
-: array(['female', 'male', 'nonbinary', 'prefer not to say'], dtype=object)
+: array(['female', 'male', 'nonbinary', 'prefer not to say'],
+:       dtype=object)
 
 #+name: social-media-gender-pref-clean-gender
 #+begin_src jupyter-python :exports both
@@ -252,9 +253,6 @@ during your analysis.
 #+results: social-media-gender-pref-clean-gender
 | female | male | nonbinary | NaN |
 
-#+results:
-| female | male | nonbinary | NaN |
-
 ****** Pre-processing: pref_service                               :B_onlyenv:
 :PROPERTIES:
 :BEAMER_env: onlyenv
@@ -277,9 +275,6 @@ during your analysis.
 #+end_src
 
 #+results: social-media-gender-pref-clean-all
-: CategoricalDtype(categories=['Facebook', 'Instagram', 'Mastodon', 'Other', 'Twitter'], ordered=False)
-
-#+results:
 : CategoricalDtype(categories=['Facebook', 'Instagram', 'Mastodon',
 :     'Other', 'Twitter'], ordered=False)
 

topics/06.org

@@ -2192,7 +2192,7 @@ TBC
 :BEAMER_ACT: <+>
 :END:
 
-#+attr_latex: :width 300
+#+attr_org: :width 500
 #+attr_latex: :width \textwidth
 [[file:.ob-jupyter/london-borough-population-inner.svg]]
 
@@ -3080,6 +3080,8 @@ TBC
 :BEAMER_ACT: <+>
 :END:
 
+#+attr_org: :width 500
+#+attr_latex: :width .8\textwidth
 [[file:.ob-jupyter/london-borough-population-inner-outer.svg]]
 
 #+begin_center

topics/dv-conda-environment-minimal.yml

+name: dv
+dependencies:
+  - beautifulsoup4
+  - bokeh
+  - descartes
+  - geopandas
+  - jupyterlab
+  - lxml
+  - mapclassify
+  - matplotlib
+  - networkx
+  - notebook
+  - numpy
+  - openpyxl
+  - pandas
+  - pip
+  - pip:
+    - contextily
+    - folium
+    - geopy
+    - jupyterlab-git
+    - pandas-bokeh
+    - plot-likert
+    - scikit-plot
+    - wordcloud
+  - scikit-learn
+  - scipy
+  - scrapy
+  - seaborn
+  - urllib3
+  - vispy
+#  - conda-forge::vtk
+  - xarray
+  - xlrd

topics/dv-conda-environment.yml

+name: dv
+dependencies:
+  - beautifulsoup4
+  - bokeh
+  - dask
+  - datashader
+  - descartes
+  - geopandas
+  - hvplot
+#  - ipycytoscape
+  - jupyter
+  - jupyterlab
+  - lxml
+  - mapclassify
+  - matplotlib
+  - networkx
+  - numpy
+  - pandas
+  - panel
+  - pip
+  - pip:
+    - contextily
+    - dask-labextension
+    - folium
+    - geopy
+# jupyter labextension install jupyterlab-spreadsheet
+#    - jupyterlab-git
+#    - jupyterlab-spreadsheet
+    - pandas-bokeh
+    - plot-likert
+    - scikit-plot
+    - wordcloud
+#  - pyvis
+#  - pyvista
+  - scikit-learn
+  - scipy
+  - scrapy
+  - seaborn
+  - urllib3
+  - vispy
+#  - conda-forge::vtk
+  - xarray
+  - openpyxl
+
+
+# Unavailable in anaconda, install with pip within the dv environment.
+# - clustergrammer2
+# - hypertools
+# - yellowbrick