first stab at topic structure and begin topic 1

.config/latex-setup.org

+#+LATEX_COMPILER: xelatex
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+#+LATEX_HEADER_EXTRA: \addbibresource{refs.bib}
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refs.bib

+@book{Dale2016,
+  address =      {Sebastopol, CA, USA},
+  author =       {Dale, Kyran},
+  isbn =         9781491920510,
+  keywords =     {Information visualization -- Data processing,
+                  JavaScript (Computer program language), Python
+                  (Computer program language)},
+  publisher =    {O'Reilly Media},
+  title =        {{Data visualization with Python {\&} Javascript:
+                  Scrape, clean, explore {\&} transform your data}},
+  year =         2016
+}

uol-data-vis.org

+#+TITLE: UoL MSc Data Science: Data Visualisation
+
+* Setup                                                            :noexport:
+** Org settings
+
+#+filetags: :dv:gold:uni:
+#+todo: TODO(t) IN-PROGRESS(i) | DONE(d)
+
+** LaTeX setup                                                     :noexport:
+
+#+setupfile: .config/latex-setup.org
+
+* Module Development Plan
+:PROPERTIES:
+:EXPORT_TITLE: MSc Data Science: Data Visualisation
+:EXPORT_SUBTITLE: Module Development Plan
+:EXPORT_FILE_NAME: export/msc-ds-mdp
+:EXPORT_OPTIONS: H:2 toc:t email:nil
+:END:
+
+** IN-PROGRESS Topic 1 – Introduction to data visualisation
+*** Topic outcomes
+
+By the end of this topic, you should be able to:
+
+1. define data visualisation;
+
+2. articulate the importance and value of visualising data;
+
+3. describe the similarities and differences between /information/ and
+   /scientific/ data visualisation;
+
+4. explain how data visualisation can be used at different stages
+   in a data science investigation;
+
+5. identify practical applications of data visualisation across a
+   range of different contexts.
+
+*** Key concepts
+
+1. Amplifying cognition
+
+2. Information versus scientific data visualisation
+
+3. Exploratory versus explanatory data visualisation
+
+*** Introduction
+
+*** Essential reading and resources
+**** eBooks
+
+\fullcite{Dale2016}
+
+**** Journal articles
+
+**** Web resources
+
+**** Reading time
+
+*** Mini lecture/presentation
+
+1. Introduction (straight to camera) [5 minutes]
+   - context (lots of data, problems of making sense of it)
+   - use visual perception to develop understanding (amplify
+     cognition)
+   - difference between data, information, and knowledge
+   - connect aims of data visualisation with other modules
+
+2. Types of data visualisation (slides plus audio) [5 minutes]
+   - information vs scientific visualisation
+
+3. Applying data visualisation in data science and beyond (slides plus
+   audio) [5 minutes]
+   - exploratory vs explanatory data visualisation
+   - data visualisation across contexts: analysis; communication;
+     monitoring; and planning
+
+*** Formative assessment
+
+*** Learning activities
+
+*** Module assessment
+
+*** Further reading
+
+*** Summary
+
+*** Indicative time for activities
+
+** TODO Topic 2 – Setting up your data visualisation development tools
+** TODO Topic 3 – Quantitative relationships, variables and types
+** TODO Topic 4 – Visualising descriptive statistics part 1
+** TODO Topic 5 – Visualising descriptive statistics part 2
+** TODO Topic 6 – Time-series data visualisation
+** TODO Topic 7 – Visualising structure part 1: Clusters
+** TODO Topic 8 – Visualising structure part 2: Networks
+** TODO Topic 9 – Working with real-world data
+** TODO Topic 10 – Scientific data visualisation
+
+- Tends to be very specialised within particular domains, but should
+  probably cover as a counterpart to information visualisation.
+
+** TODO Topic x – Data gathering techniques
+
+- Can cover data APIs and web scraping, but this doesn’t seem like a
+  core data visualisation topic.
+
+** TODO Topic x – Telling a story with data
+
+- This seems more like a theme that runs through all topics.
+
+** TODO Topic x – Misrepresenting data
+
+- This is interesting, but have no idea where to start.
+
+** TODO Topic x – Interactive data plotting and animation
+
+- Yes, but potentially a huge topic requiring very different tools.
+
+** TODO Topic x – Publishing data visualisations on the web
+
+- This seems more like a topic in web programming.
+
+** References                                                        :ignore:
+
+#+latex: \printbibliography