KV5040 - Data Visualization

You will learn through lectures, workshops, and independent learning. The lectures will cover theories and concepts that will enable you to tackle a series of guided exercises. You will work on these during workshops and hands-on sessions in Northumbria’s CIS building computer labs, which are fully equipped with the latest industry-standard software.

You will be supported by lecturers during the timetabled sessions when you will receive feedback on your work. The University’s eLearning Portal offers remote access to all lecture and seminar materials to reinforce your learning. In addition, the university library offers support for all students through providing electronic resources

All modules at Northumbria include a range of reading materials that students are expected to engage with. Online reading lists (provided after enrolment) give you access to your reading material for your modules. The Library works in partnership with your module tutors to ensure you have access to the material that you need.

Knowledge & Understanding:
ML01 – Demonstrate knowledge and understanding of the use of data visualization theory and principles and its use in decision making.
ML02 – Demonstrate understanding of tools and techniques for visualizing data.
ML03 – Demonstrate critical knowledge and understanding of professional, ethical, environmental, societal, and security issues associated with data visualization.

Intellectual / Professional skills & abilities:
ML04 – Select and apply effective computational and risk management methods to analyse, design, build and test a solution to a data visualization problem.

Personal Values Attributes (Global / Cultural awareness, Ethics, Curiosity) (PVA):
ML05 – Demonstrate critical engagement with research articles and peer reviewed papers.

The summative assessment (100%) will be a final assignment that brings together all your new skills and techniques. The assignment will be a practical exercise in designing, implementing, and evaluating a visualization. The work will involve writing a program or programs to load, clean, process, and visualize the chosen dataset and will be accompanied by a technical report written in the format of a research article describing the process, the design choices made, the results of the data analysis and visualization, and a reflective analysis of the lessons learned, both in terms of the questions being answered by the visualization and in terms of the process (e.g, how could it be improved or optimized next time round). The report will then reflect on all environmental and ethical issues raised by the work and detail all steps taken to mitigate any possible security risks raised by the exercise. This will be followed by conclusions and a full list of references. An appendix will contain a risk analysis detailing all identified risks associated with the project and how these were mitigated. The word limit for the research article (excluding source code and references) will be 2000 words. You will receive both informative and confirmatory feedback on your assessments.
This assessment addresses Module Learning Outcomes ML01–ML05
On an on-going basis you will also receive formative feedback on exercises you are required to complete.

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Data visualization is an essential part every data scientist’s toolkit. Using a range of visualization techniques and tools you will learn how to explore complex datasets and communicate the findings of your data analysis activities. The wide availability of dashboards, programming languages such as R and Python, and interactive literate programming environments such as Jupyter Lab and Google Colab have made data visualization an essential and much sought-after skill.

Employers are seeking people who can analyse their datasets and use visualization techniques to generate rich insights into the real-world processes and phenomena from which the datasets were generated. These insights are then used to create value for the employer by, for example, improving practice, generating new leads, identifying productivity challenges, and so forth.

You will critically engage with research outputs as part of your research-rich learning. The assessment will be a practical data visualization assignment.