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Software 1: Foundations of Programming for Computer Science - COM00015C

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• Department: Computer Science
• Credit value: 20 credits
• Credit level: C
• Academic year of delivery: 2024-25
  • See module specification for other years: 2022-23 2023-24

Module summary

Foundations of Programming for Computer Science

Module will run

Occurrence	Teaching period
A	Semester 1 2024-25

Module aims

Students will be introduced to different programming constructs, basic data structures, command line tools, integrated development environments and unit testing of programs. Students will learn how to describe well-defined tasks using pseudocode and translate them into programs using a procedural programming paradigm. The module will be taught using a procedural language for practising these skills.

Module learning outcomes

S101	Describe and apply the fundamental concepts of procedural programming. Write small procedural programs from scratch to perform well-defined tasks, following well-defined requirements, in a procedural programming language like Python. Relate the syntax of the language to its semantics, and analyse the result of executing fragments of syntax. Integrate library code with their own programs using appropriate software tools.
S102	Implement bespoke data structures to store states of a process. Implement simple algorithms written in pseudocode. Develop programs incrementally, using simple tests (automated where appropriate) to check each increment.
S103	Store data in memory in standard built-in collection types, and to store and retrieve data from simple text files such as CSV and JSON files.
S104	Use an appropriate software development environment, such as Eclipse, IntelliJ or VS Code. Given a program and a debugging tool, students will be able to identify and correct bugs which prevent the program from functioning as intended.
S105	Organise and document program code following the principles of software engineering. Write documentation to explain the design and implementation of their own code, or example code which is supplied to them.

Indicative assessment

Task	% of module mark
Online Exam -less than 24hrs (Centrally scheduled)	100

Special assessment rules

None

Indicative reassessment

Task	% of module mark
Online Exam -less than 24hrs (Centrally scheduled)	100

Module feedback

Feedback is provided through work in practical sessions, formative assessments, and after the final assessment as per normal University guidelines.

Indicative reading

Allen B. Downey - Think Python: How to Think Like a Computer Scientist - 2nd ed. (2015), O'Reilly Media

Mike Dawson - Python programming for the absolute beginner - 3rd ed. (2010) - Course Technology

Kent D. Lee and Steve Hubbard - Data structures and algorithms with Python (2015), Springer