DSC8001 : Data Visualisation and Data Handling
- Offered for Year: 2025/26
- Module Leader(s): Dr Joe Matthews
- Lecturer: Dr Alma Cantu
- Owning School: Mathematics, Statistics and Physics
- Teaching Location: Newcastle City Campus
Semesters
Your programme is made up of credits, the total differs on programme to programme.
| Semester 1 Credit Value: | 20 |
| ECTS Credits: | 10.0 |
| European Credit Transfer System | |
Aims
This module aims to equip students with a comprehensive foundation in data science by integrating the principles and practices of data management, exploratory data analysis, and data visualisation. Emphasis is placed on understanding the scientific method as applied to computational workflows, alongside the use of technologies that support automation, reproducibility, and end-to-end system design. Students will also explore how data can be effectively represented and communicated through visualisation, informed by theoretical concepts of data and task abstraction, as well as human perception and cognition. By combining analytical rigour with visual design, the module supports the development of data-driven products and insights that are both technically robust and communicatively effective.
Outline Of Syllabus
The syllabus will cover topics from:
• The scientific method in computational analyses
• The software and data lifecycles
• Variable characterisation and experimental design
• Exploratory data analysis and data abstraction
• Open Science, reproducibility, and the development of data products
• Data architectures and system design, including microservices and workflow automation
• Task abstraction: understanding the purpose and audience of data visualisations
• Human perception and cognition in the context of visual representation
• Visualisation techniques for categorical, ordinal, numerical, geographic, and time series data
• Dashboard design: principles, layout, and practical implementation
• Critical evaluation of visualisation effectiveness and efficiency
Teaching Methods
Teaching Activities
| Category | Activity | Number | Length | Student Hours | Comment |
|---|---|---|---|---|---|
| Guided Independent Study | Assessment preparation and completion | 1 | 40:00 | 40:00 | Assessment - Preparation and completion of 12 page technical project to include source data analysis |
| Scheduled Learning And Teaching Activities | Lecture | 1 | 2:00 | 2:00 | Guest/Specialist Lecture |
| Guided Independent Study | Assessment preparation and completion | 9 | 0:30 | 4:30 | Lecture content quizzes. Approx one per lecture |
| Scheduled Learning And Teaching Activities | Lecture | 9 | 2:00 | 18:00 | Lectures |
| Guided Independent Study | Assessment preparation and completion | 1 | 40:00 | 40:00 | Project - Preparation and completion of Power BI dashboard plus 4 page report |
| Scheduled Learning And Teaching Activities | Practical | 12 | 2:00 | 24:00 | Practical Sessions |
| Scheduled Learning And Teaching Activities | Drop-in/surgery | 2 | 2:00 | 4:00 | Coursework Support |
| Guided Independent Study | Independent study | 1 | 47:30 | 47:30 | Background reading |
| Guided Independent Study | Independent study | 20 | 1:00 | 20:00 | Independent study on course content |
| Total | 200:00 |
Teaching Rationale And Relationship
Lectures explain the underpinning principles for the module and technologies that support data management, exploratory data analysis and data visualisation.
Lectures are complemented by supervised practical sessions to guide the application of these principles using suitable computational tools. The practical work builds up experience working with a computational toolset that is used to complete a substantive project working with data from a real-world context.
Assessment Methods
The format of resits will be determined by the Board of Examiners
Other Assessment
| Description | Semester | When Set | Percentage | Comment |
|---|---|---|---|---|
| Computer assessment | 1 | M | 50 | Source code of a Power BI dashboard, supplemented by a 4-page report justifying design choices and a 3-minute video demonstrating interactivity of the dashboard. The combination of all three deliverables (source code, report and video) contribute to mark |
| Design/Creative proj | 1 | M | 50 | 12 page report of a technical project with source code of a Power BI dashboard, alongside a 3 minute video demonstrating interactivity of the dashboard. The combination of all 3 deliverables contribute to the mark |
Formative Assessments
Formative Assessment is an assessment which develops your skills in being assessed, allows for you to receive feedback, and prepares you for being assessed. However, it does not count to your final mark.
| Description | Semester | When Set | Comment |
|---|---|---|---|
| Computer assessment | 1 | M | Set of quizzes on lecture content. Approx one per lecture session. max 30 mins per quiz |
Assessment Rationale And Relationship
The module includes two summative assessments:
• Project Proposal Report – This assessment evaluates students’ ability to define a data science
problem, manage and prepare data, and design an appropriate analytical approach. It focuses on
planning and applying data management and exploratory data analysis techniques.
• Project Execution and Presentation – In this assessment, students complete the project using
real-world data and present their results through a written report and a video demonstration. It
assesses their ability to apply data visualisation and interaction techniques to solve a problem and
communicate findings clearly.
In addition to the summative assessments, the module includes formative assessment in the form of automatically graded quizzes. These can be completed at the students’ own pace and are designed to help them check their understanding of lecture content and key concepts. This provides immediate feedback and supports their learning throughout the module.
Reading Lists
Timetable
- Timetable Website: www.ncl.ac.uk/timetable/
- DSC8001's Timetable