Commit ecd331a3 authored by Jamie Forth's avatar Jamie Forth
Browse files

first stab at topic structure and begin topic 1

Pipeline #338 canceled with stages
#+LATEX_HEADER: \defaultfontfeatures{Mapping=tex-text}
#+LATEX_HEADER: \usepackage{libertine}
#+LATEX_HEADER_EXTRA: \usepackage[a4paper,top=2.25cm,bottom=2.25cm,left=3.0cm,right=3.0cm]{geometry}
#+LATEX_HEADER_EXTRA: \usepackage{svg}
#+LATEX_HEADER_EXTRA: \usepackage[style=authoryear,backend=biber]{biblatex}
#+LATEX_HEADER_EXTRA: \ExecuteBibliographyOptions{sorting=nyt,maxcitenames=2,giveninits=true,doi=false,url=false,isbn=false,eprint=false,uniquename=allinit,uniquelist=minyear}
#+LATEX_HEADER_EXTRA: \addbibresource{refs.bib}
#+LATEX_HEADER_EXTRA: \usepackage{paralist}
#+LATEX_HEADER_EXTRA: \let\itemize\compactitem
#+LATEX_HEADER_EXTRA: \let\description\compactdesc
#+LATEX_HEADER_EXTRA: \let\enumerate\compactenum
#+LATEX_HEADER_EXTRA: \usepackage{setspace}
#+LATEX_HEADER_EXTRA: \setlength{\tabulinesep}{5pt}
# Colorlinks sets pdfborder to zero.
#+LATEX_HEADER_EXTRA: \hypersetup{colorlinks,linkcolor=,urlcolor=red}
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
#+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/
* Module Development Plan
: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
** 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
**** 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
- 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
Markdown is supported
0% or .
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!
Please register or to comment