KV5040 - Data Visualization

What will I learn on this module?

This module takes your knowledge of computer programming and uses those 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 visualization techniques and tools you will be able 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 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. 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. The reading list for this module can be found at: http://readinglists.northumbria.ac.uk
(Reading List service online guide for academic staff this containing contact details for the Reading List team – http://library.northumbria.ac.uk/readinglists)

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 and ethical issues associated with data visualization.

Intellectual / Professional skills & abilities:
ML04 – Select and apply effective computational 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, followed by conclusions and a full list of references. 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 GN50

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 2023

Fee Information

Module Information

All information on this course page is accurate at the time of viewing.

Our Campus based courses starting in 2022 and 2023 will be delivered on-campus with supporting online learning content. We continue to monitor government and local authority guidance in relation to Covid-19 and we are ready and able to adjust the delivery of our education accordingly to ensure the health and safety of our students and staff.

On-campus contact time is subject to increase or decrease in line with any additional restrictions, which may be imposed by the Government or the University in the interest of maintaining the health and safety and wellbeing of students, staff, and visitors. This could potentially mean increased or fully online delivery, should such restrictions on in-person contact time be required.

 

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