Reference no: EM133817638

Data Visualisation

Assignment - Economic Inactivity

Learning Outcomes

1. Reflect upon and critically appraise an implemented solution against a given brief.

2. Collaborate effectively with others appropriate to the professional/academic context.

3. Demonstrate a systematic and critical understanding of the range of charts suitable for representing large multi-dimensional datasets and the mathematical theory underpinning them.

4. Develop critical responses to the theoretical discourse on human visual perception of shape and form.

5. Integrate and synthesise diverse knowledge concepts and theory to analyse and design a solution for a given brief with clear reasoned justifications.

6. Demonstrate the ability to autonomously and effectively plan and execute a project to satisfy a complex brief.

7. Incorporate a professional dimension in researching new and presenting emerging forms of charts.

Assessment Brief

Using cleansed and publicly accessible versions of this data, available through the ONS and via this link Economic inactivity - Office for National Statistics (ons.gov.uk), you are being set the task of undertaking some exploratory analysis through visualising the data. You are encouraged to use this core dataset, or an equivalent, in combination with any other datasets that you find to determine areas for further exploration in the main research project. For example, you may choose to examine health and mental health data, age profiles, education levels of residents in Teesside and would therefore need to source those additional datasets. You may also choose to compare Teesside to the rest of the UK.

You must find and visualise some interesting narrative within the data which you believe requires further investigation and will help the research team narrow their focus when they begin working with the restricted data.

If you are a student on the MSc Digital and Technology Solutions pathway with an Integrated Apprenticeship, you may (optionally) choose your own brief and data for the ICA where the data has some relevance to the company with which you are undertaking your apprenticeship. There are are two conditions to this which must be met if the module leader is to approve the alternative brief:
It is your responsibility to get clearance from your employer to work with this data and this must be shared with the tutors. If a non-disclosure agreement is required to be signed by Teesside University tutors then allow for adequate time for this to be processed with our legal department (usually 14 days) prior to commencing work.

Assessment Strategy

The module is assessed by an in-course assessment with two elements:

Element 1 is an individual assessment which requires the students to examine a selection of materials, critically evaluate them with regard to graphical integrity, misrepresentation and distortion, and make appropriate recommendations for improvement or alternative charts.

Element 2 is a group assessment which will require students to work in small teams to undertake a piece of research regarding some aspect of mathematical theory underpinning multivariable charts.

Element 1 - Individual Report

You will perform an extensive literature review and a review of some of the materials provided during the module and critique the top 10 quality criteria list that has been generated by AI (see list below). Use your research of work completed by authors in this field to support or reject the inclusion of these criteria. Should these be grouped in a different way? Do they all count as quality criteria? Are any missing? Are the descriptions over simplified? Has sufficient consideration been given to theory of human visual perception?
After providing a detailed critique, supported by references to relevant texts, provide an improved list of your own design.
Hint: if this list had been submitted for assessment it would not receive a pass mark at postgraduate level largely due to the lack of references to the literature, lack of detail etc so there is work to be done to improve it.
Accuracy: Ensure that your data visualization accurately represents the underlying data. Avoid distorting or misrepresenting information, and use appropriate scales and measurements.
Relevance: Focus on displaying data that is relevant to the message or story you want to convey. Eliminate unnecessary data points or distractions that don't contribute to the visualization's purpose. Get Homework Help Now!
Clarity and Simplicity: Strive for clarity and simplicity in your design. Keep the visualization uncluttered, use clear labels, and avoid excessive embellishments that could confuse viewers.
Consistency: Maintain consistency in visual elements such as color, fonts, and style throughout the visualization to create a cohesive and easily digestible experience.
Interactivity: If applicable, incorporate interactive elements that allow users to explore the data further. Interactive tooltips, filtering options, and drill-down capabilities can enhance engagement.
Accessibility: Ensure that your visualization is accessible to a wide range of users, including those with disabilities. Use alt text for images, choose accessible color schemes, and provide alternative ways to access the data.
Context: Provide context and background information to help viewers understand the significance of the data. Include clear titles, subtitles, and annotations to guide interpretation.
Storytelling: Use the visualization to tell a compelling story or convey a message. Arrange data points in a logical sequence that guides viewers through the narrative.
Aesthetics: Pay attention to the aesthetics of your visualization. While functionality is essential, a visually pleasing design can capture and hold the audience's attention.
Testing and Feedback: Regularly test your visualization with potential users or colleagues to gather feedback and identify areas for improvement. Ensure that the visualization effectively communicates the intended message.

Select 5 published data visualisations on the same topic as in this brief and critique them according to your own pre-defined quality criteria derived from the taught theory.
Choose a wide range of different types of chart or infographic. For each chosen visualisation make recommendations for improvements or suggest alternative visualisations that would have illustrated the data in a better way. Do NOT create any data visualisations yourself - you are appraising the work of others.

Element 2 - Group presentation (Presentation and Infographic

Select the data you wish to visualise.
Generate a project brief which details the following:
A question that you seek to answer through data visualisation (and any supporting questions)
A description of the dataset i.e. data types, relationships etc
A description of your target audience and the context of the project e.g. what motivates them, what biases do they have, what are they interested in, what is the cultural, community or commercial context?
What you hope to achieve through the visualisation of this data e.g. explore, explain, educate, entertain, engage, influence
Agree your project brief with your tutor before progressing.
Generate a set of graphs which explore the statistical properties of your dataset, demonstrating a deep knowledge of the mathematical theory underpinning explorative multivariable charts.
Select a set of explanative charts, which in combination, helps you satisfy your project brief and provides a visual solution.
Generate a data representation in the form of an infographic, remembering to design for your target audience.
Create a PowerPoint slide deck which includes the following 7 slides:
Title slide
Your project brief
A description of the data using explorative and explanative charts
Your infographic (or a link to your infographic)
A justification for the choice of visualisations
A critical reflection of the resulting data representation.
Your project workplan and individual contributions to the project.
You will present your infographic and slides as a group at a time agreed with the module leader in Week 12.