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What will I learn on this module?
This module uses your computer programming skills to develop an understanding of the theory and practice of data visualization. Data visualization is an essential part every data scientist’s toolkit. Using a range of techniques and tools you will 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 a much sought-after skill.
Employers seek people who can analyse their datasets and use visualization techniques to generate rich insights into the 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 work through a series of exercises, making use of Northumbria’s state-of-the-art computer labs to anaylse and visualize a range of datasets. You will also critically engage with research outputs as part of your research-rich learning. You will also consider how to minimise
the environmental impact of data visualization, through the use of, for example, sustainable practices in data centres and using energy-efficient hardware. Ethical issues around how data visualizations can be used will be explored.
The main assessment will be a data visualization assignment that will bring together your newly developed skills and techniques and apply them to a data analysis and visualization problem which will be written up in the form of a research article supported by the source code of your visualization solution.
How will I learn on this module?
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.
How will I be supported academically on this module?
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
What will I be expected to read on this module?
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.
What will I be expected to achieve?
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.
How will I be assessed?
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.
Pre-requisite(s)
N/A
Co-requisite(s)
N/A
Module abstract
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.
Course info
UCAS Code G417
Credits 20
Level of Study Undergraduate
Mode of Study 3 years Full Time or 4 years with a placement (sandwich)/study abroad
Department Computer and Information Sciences
Location City Campus, Northumbria University
City Newcastle
Start September 2025
All information is accurate at the time of sharing.
Full time Courses are primarily delivered via on-campus face to face learning but could include elements of online learning. Most courses run as planned and as promoted on our website and via our marketing materials, but if there are any substantial changes (as determined by the Competition and Markets Authority) to a course or there is the potential that course may be withdrawn, we will notify all affected applicants as soon as possible with advice and guidance regarding their options. It is also important to be aware that optional modules listed on course pages may be subject to change depending on uptake numbers each year.
Contact time is subject to increase or decrease in line with possible restrictions imposed by the government or the University in the interest of maintaining the health and safety and wellbeing of students, staff, and visitors if this is deemed necessary in future.
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