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+#+title: Resit coursework
+#+subtitle: UoL MSc Data Science: Data Visualisation
+#+export_file_name: export/dv-resit-coursework
+#+options: H:3 toc:nil author:nil date:t
+#+date: October 2021
+#+setupfile: ~/org/latex/latex-setup.org
+#+author: Jamie Forth
+#+latex_header_extra: \setlist{nosep}
+#+latex_header_extra: \usepackage{parskip}
+
+
+This resit coursework is for students resitting DSM050 and in the
+previous session:
+
+- passed Final Survey Report (C1.1) =[25%]=
+- passed Mid-term test (C1.2) =[5%]=
+- failed the Exam =[70%]=.
+
+#+begin_center
+*This coursework replaces the Exam*
+#+end_center
+
+This coursework is in two parts. Part 1 is worth 30%, Part 2 is worth
+40%. The total weighting of this coursework is 70%.
+
+* Part 1 – Survey project: Reflective commentary =[30%]=
+** Assignment specification
+
+Produce a 1500–2000 word reflective essay about your previous survey
+coursework (Final Survey Report C1.1).
+
+- Submit your essay as a *PDF* file (1500–2000 words).
+- References and appendices do /not/ count towards the word limit.
+
+Give a brief introduction to your topic, but do not substantially
+recount your analysis here. The purpose of this essay is to
+self-evaluate the research you carried out.
+
+You should reflect and comment on:
+1. the design of the research project
+   - e.g. research questions, scope, population, background research
+2. the design and implementation of your survey
+   - e.g. operationalisation, wording and ordering of questions,
+     potential bias, the mapping of research questions to survey
+     questions
+3. your analysis
+   - e.g. data pre-processing, exploratory visualisation, explanatory
+     visualisation.
+
+Consider the strengths and weakness of the approach taken in your
+previous coursework. Identify and discuss the most important aspects
+of the process that both led to successful findings and that you would
+improve upon were you were to carry out this research again.
+
+** Essay structure and mark breakdown
+*** Introduction =[10%]=
+
+- brief summary of survey research topic and key findings
+- brief summary of the structure of the reflective essay (what are the
+  main points about your process that you are going to discuss?)
+
+*** Commentary / self-evaluation =[80%]=
+
+- discuss strengths and weakness of the previous research project
+- prioritise the most important or significant aspects of your process
+  to keep within the word limit
+- marks will be awarded for critical insight backed up by evidence,
+  references and/or best practices in the field
+- if any new important findings become apparent from revising this
+  work they can be briefly presented, but the main focus of the essay
+  should be critical reflection not re-analysis of your data
+
+*** Conclusion =[10%]=
+
+- specific: highlight the main takeaway messages from this process of
+  self-reflection regarding your survey project
+- general: summarise the most important things you have learnt from
+  this process that will be of value to future data analysis and
+  visualisation projects
+
+** Rubric
+*** Introduction =[5 marks]=
+
+- [0] No introduction
+- [1] Brief summary of topic
+- [2] Discretionary
+- [3] Overview of essay structure / key point to be discussed
+- [4] Discretionary
+- [5] Clear, concise, professionally written introduction
+
+*** Commentary / self-evaluation =[40 marks]=
+**** Strengths
+
+- [0] No strengths discussed
+- [2] Some attempt to identify strengths, but may lack coherence or
+  significance
+- [4] Discretionary
+- [6] Coherent set of strengths identified, providing a sound basis
+  for critical discussion
+- [8] Discretionary
+- [10] Important and significant strengths identified demonstrating a
+  thorough and thoughtful consideration of the research process and
+  prioritisation of key strengths
+
+**** Weaknesses
+
+- [0] No weaknesses discussed
+- [2] Some attempt to identify weaknesses, but may lack coherence or
+  significance
+- [4] Discretionary
+- [6] Coherent set of weaknesses identified, providing a sound basis
+  for critical discussion
+- [8] Discretionary
+- [10] Important and significant weaknesses identified demonstrating a
+  thorough and thoughtful consideration of the research process and
+  prioritisation of key weaknesses
+
+**** Critical insight
+
+- [0] No real critical reflection or deeper discussion around
+  strengths and weaknesses
+- [2] Basic insight into the impact of strengths and weaknesses on the
+  overall success of the project
+- [4] Discretionary
+- [6] Reflective insight with balanced and justified analysis of what
+  went well and what could have been improved
+- [8] Discretionary
+- [10] Highly insightful self-evaluation grappling with nuanced or
+  complex issues in data analysis and visualisation
+
+**** Argumentation and supporting evidence
+
+- [0] Unclear, irrelevant or highly subjective argumentation
+- [2] Some attempt to present reflective commentary in a logical and
+  coherent way
+- [4] Discretionary
+- [6] Logically structured and convincing discussion grounded in best
+  practices or general data science principles
+- [8] Discretionary
+- [10] Well referenced and evidenced insight resulting in a highly
+  informative and convincing discussion
+
+*** Conclusion =[5 marks]=
+
+- [0] No introduction
+- [1] Brief summary of key strengths/weakness of original process and
+  analysis
+- [2] Discretionary
+- [3] Brief summary of learning and new insights
+- [4] Discretionary
+- [5] Synthesis of critical reflection into useful guidelines or
+  principles for future work
+
+* Part 2 – Secondary data analysis project =[40%]=
+** Assignment specification
+
+Conduct a data visualisation-led investigation into a topic of your
+choice using secondary data (e.g. data found online), and produce a
+2500–3000 report in the form of a Jupyter notebook.
+
+- You must write your report as a Jupyter notebook using inline
+  markdown (see template provided on the VLE).
+- You must submit a *PDF* of your notebook (“print as PDF” from your
+  browser), and a separate *ZIP* file containing:
+  - your notebook (=ipynb=)
+  - all secondary data (=csv=, =xlsx=, =ods= files etc., make sure you
+    observe all legal and/or ethical restrictions)
+  - any supplementary scripts.
+- The maximum word limit is 3000 words (suggested range 2500–3000
+  words).
+- Include any supplementary information not essential to the main body
+  of the report as appendices. References and appendices do /not/
+  count towards the word limit.
+- No marks will be directly awarded for material submitted in
+  appendices.
+- No marks will be awarded for analysis discussion submitted as
+  comments in code cells.
+- See provided template notebook for how to count the number of words
+  in your notebook.
+
+** Project guidelines
+*** Steps
+
+1. Define your topic and research questions. What are you going to
+   investigate?
+2. Gather data (clean, pre-process, merge datasets etc.)
+3. Data visualisation. Focus on exploratory data visualisation
+   initially, and then progress to explanatory visualisation to
+   present key findings. The visuals presented in the main body of the
+   final notebook should be of a professional standard and communicate
+   your findings effectively and efficiently. Use your research
+   questions to structure your analysis and the presentation of
+   results.
+4. Conclusion and evaluation
+
+*** Where to find data?
+
+Data can come from multiple sources in order to answer your research
+questions. Make sure you are aware of any legal and/or ethical issues
+with the data you use.
+
+- Google
+  - [[http://www.powersearchingwithgoogle.com/][power search]]
+  - [[https://www.google.com/publicdata/directory][public data]]
+- https://www.kaggle.com/ (do not use a dataset that already has
+  extensive published analysis, especially if Python code is available
+  – check with your tutor if you are unsure)
+- [[https://data.world]]
+- https://data.gov.uk
+- http://data.london.gov.uk
+- https://www.ons.gov.uk
+- https://data.europa.eu
+- http://blog.visual.ly/data-sources
+- http://datajournalismhandbook.org/1.0/en/getting_data_0.html
+
+** Report structure and mark breakdown
+*** Research topic =[10%]=
+
+- summary of the domain of research/field of enquiry
+  - some references to contextualise the investigation
+- research question(s)
+  - what do you want to find out?
+  - can be general as the emphasis here is exploratory data
+    visualisation
+- identify and define important concepts w.r.t research question(s)
+  - reference academic/technical literature where appropriate
+
+*** Data =[10%]=
+
+- data source(s), where/how did you find your data?
+- data format
+- data cleaning and pre-processing
+- critically evaluate your data, is the data reliable?
+- how was the data originally gathered, could there be bias?
+
+*** Exploratory and explanatory data visualisation =[60%]=
+
+- brief description of the variables of interest
+- appropriate graphs and/or tables summarising key variables
+- *descriptive statistics*
+- visualisations of all basic variable types
+  - categorical (nominal, ordinal) and quantitative (interval and/or
+    ratio scales)
+  - aim for *at least one* of each basic kind of graph: pie, bar, box,
+    histogram and scatterplot
+  - marks will be awarded for *appropriateness* and *effectiveness* of
+    the visualisations
+- visualise relationships between variables
+- full marks will require examples of more specialised or advanced
+  types of visualisation, e.g., time-series, geospatial,
+  high-dimensional, networks, clustering, qualitative data etc.
+- briefly justify visualisation choices in terms of *data types* and
+  aspects of *human perception*
+
+*** Conclusion and evaluation =[10%]=
+
+- summarise key findings (these don't have to be ground breaking
+  discoveries!)
+- evaluate your process and visualisations
+- what did you learn?
+- what could you have improved?
+
+*** Code =[10%]=
+
+- all python code files should be submitted
+- all pre-processing and data cleaning should be implemented in code
+  for transparency and reproducibility (do not manually edit data in a
+  spreadsheet programme or hard-code data values in your notebooks)
+- code should be legible, with brief comments
+- re-using and adapting code you find in documentation or elsewhere
+  online is completely fine, but sources must be attributed correctly
+  (web link and date accessed)
+- re-using and adapting code that we have covered in class is
+  encouraged
+
+** Rubric
+*** Research topic =[10 marks]=
+**** Report introduction
+- [0] No introduction
+- [1] Brief overview of the analysis undertaken
+- [2] Clear and concise overview of the report, summarising its
+  structure and key findings
+
+**** Background and context
+- [0] No background
+- [1] Brief discussion of related wider issues
+- [2] Clear and concise discussion of related research or news stories
+
+**** Motivation
+- [0] No motivation
+- [1] Key motivations briefly discussed
+- [2] Clear and concise rational motivating the study and potential
+  impact
+
+**** Research questions
+- [0] Not explicitly stated
+- [1] Attempt to construct research questions, but some inconsistency
+  or lack of focus
+- [2] Basic set of coherent research questions with realistic scope
+- [3] Clear and well-designed set of research questions with potential
+  to generate novel insight within the scope of the study
+- [4] Research questions informed by previous research or in
+  conjunction with a theoretical framework
+
+*** Data =[10 marks]=
+**** Data source
+- [0] Not discussed
+- [1] States the source of the dataset
+- [2] Discussion of how the data was found
+- [3] Discussion of why the data was selected: trustworthiness and validity
+- [4] Discussion of how the data was initially collected
+- [5] Thorough critical assessment of the data, discussing potential
+  issues or bias
+
+**** Data format and pre-processing
+- [0] Not discussed
+- [1] Brief description of data format and available variables
+- [2] Brief discussion of data tidiness
+- [3] Lists appropriate data types for all variables used
+- [4] Discussion of all data pre-processing undertaken, including
+  cleaning, parsing, handling missing values, transformation and
+  filtering
+- [5] Sophisticated data pre-processing that ensures clean and
+  accurate data, and providing sound reasoning for all pre-processing
+  undertaken
+
+*** Exploratory and explanatory data visualisation =[60 marks]=
+**** Tables and summary stats
+- [0] No tables or summary stats used
+- [1] Basic tables for key variables, (e.g. using pandas describe);
+  use of simple statistics in prose (99% of cats...)
+- [2] Discretionary
+- [3] Appropriate use of cross-tabulation and sorting; good use of
+  language to convey quantitative facts (1 in 4 cats...)
+- [4] Discretionary
+- [5] Highly effective communicative tables showing more advanced data
+  processing such as grouping, aggregating, filtering or normalisation
+
+**** Appropriate plots for each variable data type
+
+- [0] Many highly inappropriate visualisations, e.g. using line graphs
+  for non-sequential data, pie charts with many categories, or
+  pointless use of 3D
+- [2] Some inappropriate visualisations for certain variables
+- [4] Discretionary
+- [6] Appropriate univariate visualisations of all data types
+  (nominal, ordinal and numerical)
+- [8] Appropriate multivariate visualisations across a range of
+  different data type combinations
+- [10] Appropriate use of advanced visualisation techniques
+
+**** Presentation quality
+- [0] Poor quality and lack of attention to detail
+- [2] Inconsistent titles and/or axes labelling, screenshot/low
+  resolution images, unreadable labels or occluding visual elements
+- [4] Discretionary
+- [6] Consistent titles/figure captions and labels, attention to
+  spacing and meaningful use of colour
+- [8] Discretionary
+- [10] Professional level presentation quality, immaculate plots with
+  no extraneous or obscuring details
+
+**** Visual communication
+- [0] Many meaningless or pointless plots
+- [5] Some effective simple plots, but also some confusing or
+  misleading visualisations
+- [10] Discretionary
+- [15] Consistent high quality univariate plots, with some effective
+  multivariate plots
+- [20] Good range of univariate and multivariate plots, each able to
+  effectively communicate a strong message
+- [25] Discretionary
+- [30] Consistent highly efficient visual communication requiring
+  little or no explanation; key elements of visual design related to
+  human visual perception
+
+**** Exploratory and explanatory data vis process
+- [0] Disorganised approach, no clear method to the analysis and no
+  coherent story presented
+- [1] Some attempt to explore the data methodically and to construct a
+  basic narrative
+- [2] Discretionary
+- [3] Clear evidence of direction in exploratory analysis and findings
+  presented logically and related to stated research questions
+- [4] Discretionary
+- [5] Exploratory analysis is well planned and executed, leading to
+  interesting insights that are conveyed within a clear and logical
+  narrative
+
+*** Conclusion and evaluation =[10 marks]=
+**** Conclusion
+- [0] No conclusion
+- [1] Superficial conclusion listing key findings
+- [2] Discretionary
+- [3] Discussion of findings in relation to research questions
+- [4] Discretionary
+- [5] Clear and concise discussion of main findings in relation to
+  research questions, scope and possible impact
+
+**** Evaluation
+- [0] No evaluation
+- [1] Superficial discussion of problems
+- [2] Discretionary
+- [3] Insightful discussion of problems, solutions and what could have
+  been improved
+- [4] Discretionary
+- [5] Insightful and honest reflection on the aims, process and
+  execution of the study, and pointers to possible future directions
+  of research
+
+*** Code =[10 marks]=
+**** Code
+- [0] Missing code
+- [2] Python code for each visualisation
+- [4] Discretionary
+- [6] Scripts and notebooks are well commented and make idiomatic use
+  of Python data science libraries (i.e. using the APIs correctly
+  results in fewer lines of code, which is generally better)
+- [8] Discretionary
+- [10] Scripts and notebooks are well commented and logically
+  structured with minimal copy-and-paste code, ensuring that the
+  process of analysis is transparent and reproducible