DSC1004 : Software and Environments 2
- Inactive for Year: 2026/27
- Module Leader(s): Dr Phillip Lord
- Lecturer: Dr John Mace
- Owning School: Computing
- Teaching Location: Newcastle City Campus
Semesters
Your programme is made up of credits, the total differs on programme to programme.
| Semester 2 Credit Value: | 20 |
| ECTS Credits: | 10.0 |
| European Credit Transfer System | |
Aims
This module develops your understanding of software and computational environments used in data analytics. Through practical activities, you will work with libraries and tools commonly used for processing, analysing, and visualising data within modern computational workflows. The emphasis is on developing reliable and reproducible approaches to software and data analysis through the effective management of environments, workflows, testing, and computational processes.
Outline Of Syllabus
Topics covered by this module include:
Advanced data handling in Python and R
Libraries and tools including NumPy, pandas, matplotlib, Tidyverse, and Shiny
Development, extension, and maintenance of software libraries
Testing and reliable software development
Dependency management and reproducible computational environments
Continuous integration and cloud-based computational workflows
Scalable and maintainable software development practices
Computational workflows for data-focused analysis and software systems
Teaching Methods
Teaching Activities
| Category | Activity | Number | Length | Student Hours | Comment |
|---|---|---|---|---|---|
| Structured Guided Learning | Lecture materials | 40 | 1:00 | 40:00 | Reading through lecture materials and preparation for practical sessions |
| Scheduled Learning And Teaching Activities | Lecture | 10 | 1:00 | 10:00 | Lectures |
| Guided Independent Study | Assessment preparation and completion | 25 | 1:00 | 25:00 | Preparation and completion of Group Coursework |
| Guided Independent Study | Assessment preparation and completion | 25 | 1:00 | 25:00 | Preparation and completion of Individual Coursework |
| Scheduled Learning And Teaching Activities | Practical | 10 | 3:00 | 30:00 | Supervised practical sessions focused on hands on implementation. |
| Guided Independent Study | Directed research and reading | 1 | 20:00 | 20:00 | Wider reading as per provided reading material |
| Guided Independent Study | Independent study | 1 | 50:00 | 50:00 | N/A |
| Total | 200:00 |
Teaching Rationale And Relationship
This module includes an encounter with the leading edge of industry.
Assessment Methods
The format of resits will be determined by the Board of Examiners
Other Assessment
| Description | Semester | When Set | Percentage | Comment |
|---|---|---|---|---|
| Report | 2 | M | 50 | Group Software/Data analysis project equivalent in work to a 2000 word essay (MLO1, 2, 3, 4) |
| Report | 2 | M | 50 | Individual Software/Data analysis project equivalent in work to a 2000 word essay (MLO1, 2, 3) |
Assessment Rationale And Relationship
The assessment strategy for this module is based on authentic practical coursework designed to evaluate students’ ability to work within modern computational and software development environments using structured and reproducible approaches to technical problem solving. The assessments collectively support the development of computational, analytical, and collaborative software development skills appropriate to Stage 1 study.
Through the coursework assessments, students are required to apply programming techniques, computational tools, and software development practices within practical data-focused and computational contexts. The assessments provide opportunities for students to demonstrate structured approaches to computational workflows, technical problem solving, and collaborative working practices while engaging with realistic software and analytical tasks. (MLO1, MLO2, MLO3, MLO4)
The assessment strategy supports the progressive development of digital fluency, reproducible computational practice, and collaborative approaches to software development aligned with contemporary computational and data-focused practice.
Alternative assessment:
For students with a Student Support Plan (SSP) who have alternative assessment as a reasonable adjustment and/or students with supported Personal Extenuating Circumstances (PEC), it may be necessary to set an alternative assessment as presented below. Due to the professional requirements of some programmes, alternative assessments may not be available even where students are eligible.
The assessments are designed to be authentic and inclusive, reflecting genuine occupational and disciplinary practices associated with software development and computational data analysis. The teaching team does not anticipate any known barriers to inclusion associated with the assessment structure. Where appropriate, alternative arrangements for the group assessment may include an equivalent individual written submission addressing the same learning outcomes.
Reading Lists
Timetable
- Timetable Website: www.ncl.ac.uk/timetable/
- DSC1004's Timetable