DSC1002 : Software and Environments 1
- 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 1 Credit Value: | 20 |
| ECTS Credits: | 10.0 |
| European Credit Transfer System | |
Aims
This module introduces software development and its role in data analysis. Through practical programming activities, you will develop programs in Python and R, two languages widely used in industry for working with data and computational problem solving. The emphasis is on understanding software development as a collaborative and team-oriented practice, including the tools and approaches that support shared development, version control, and reliable software production.
Outline Of Syllabus
Topics covered by this module include:
Programming in Python and R
Variables, operators, control flow, lists, functions, and data structures
Programming approaches and workflows in different languages
Computational and Linux-based environments
Command-line tools and scripting
Libraries and dependency management
Version control systems and collaborative software development
Isolated and reproducible development environments
Advanced programming concepts, including typing and structured data representations
Development environments and computational workflows for data-focused tasks
Teaching Methods
Teaching Activities
| Category | Activity | Number | Length | Student Hours | Comment |
|---|---|---|---|---|---|
| Guided Independent Study | Assessment preparation and completion | 1 | 25:00 | 25:00 | Preparation and completion of Coursework 1 |
| 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 | 1 | 25:00 | 25:00 | Preparation and completion of Coursework 2 |
| Guided Independent Study | Directed research and reading | 1 | 20:00 | 20:00 | Wider reading as per provided material |
| Scheduled Learning And Teaching Activities | Practical | 10 | 3:00 | 30:00 | Supervised practical sessions focused on hands on implementation. |
| Guided Independent Study | Independent study | 1 | 50:00 | 50:00 | Independent study |
| 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 | 1 | M | 50 | Software/Data analytics practical equivalent in work to a 2000 word essay (MLO1,2,3,4) |
| Report | 1 | M | 50 | Software/Data analytics practical equivalent in work to a 2000 word essay (MLO2,3,4,5) |
Assessment Rationale And Relationship
The assessment strategy for this module is based on authentic practical coursework designed to evaluate students’ ability to apply programming and computational techniques within realistic software and data-focused contexts. The assessments collectively support the development of technical competence, computational problem solving, and professional software development practices appropriate to Stage 1 study.
Through the coursework assessments, students are required to develop, test, and apply programs to manipulate data and address defined computational problems using appropriate programming techniques and computational workflows. The assessments provide opportunities for students to demonstrate the use of programming constructs, data processing approaches, debugging techniques, and software development practices within practical and applied settings. (MLO1, MLO2, MLO3, MLO4, MLO5)
The assessment strategy supports the progressive development of programming confidence, digital fluency, and independent computational problem solving while encouraging students to engage with professional approaches to software development and collaborative working practices. The practical nature of the assessments enables students to demonstrate applied technical skills in contexts 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.
Reading Lists
Timetable
- Timetable Website: www.ncl.ac.uk/timetable/
- DSC1002's Timetable