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Computing Skills

Overview

An introductory module exploring the foundations of computer science and computational thinking.
Designed for students with little or no prior programming experience, it builds the core technical, analytical, and ethical skills required to understand how computers work — and how we model real-world problems using computation.

1. Computer Systems and Architecture

  • Components: CPU, memory, storage, I/O, and system buses
  • CPU internals — fetch → decode → execute → write-back cycle
  • Von Neumann vs. Harvard architectures
  • Instruction sets: RISC vs. CISC
  • Performance factors: clock speed, cache, pipelines, cores
  • Memory hierarchy and data flow in modern systems

2. Data Representation

  • Bits, bytes, nibbles, and word sizes
  • Number bases: decimal ↔ binary ↔ hexadecimal
  • ASCII and text encoding
  • RGB and HEX colour representation
  • Why hexadecimal is used as a shorthand for binary

3. Computational Thinking

  • Decomposition, abstraction, pattern recognition, and algorithms
  • Expressing logic using pseudocode
  • Reasoning about algorithm efficiency
  • The Word RAM model as an abstraction of hardware

4. Python Fundamentals

  • Development environment setup via Anaconda / Jupyter Notebook
  • Basic shell structure and the main() entry point
  • Input/output, variables, arithmetic operations
  • Control flow and functions
  • Example lab activities:
  • Temperature logger
  • Conversion calculator
  • RGB to HEX converter

5. Modelling and Abstraction

  • What is a model and why do we build them?
  • Model categories:
  • Explanatory
  • Predictive
  • Experimental
  • Optimisation
  • The modelling process:
  • Scoping
  • Specification
  • Planning
  • Building
  • Analysing results
  • Simplification, assumptions, and bias in models
  • Guiding principle: Model simple, think complicated.

6. Ethics and Artificial Intelligence

  • Ethical challenges in computing and AI
  • Bias in facial recognition systems
  • Responsible and transparent AI use in academia
  • Academic integrity and appropriate AI disclosure

Skills Developed

  • Converting data between binary, decimal, and hexadecimal
  • Applying computational thinking to real-world problems
  • Writing structured, readable Python code
  • Building and evaluating computational models
  • Reflecting critically on ethics, bias, and fairness in technology

Tools and Environment

  • Language: Python 3
  • Environment: Jupyter Notebook (via Anaconda Navigator or other method)

References

  1. M. Pidd, Tools for Thinking: Modelling in Management Science, 3rd ed., Wiley, 2009.
  2. A. M. Law, Simulation Modeling and Analysis, 5th ed., McGraw-Hill Education, 2015.
  3. UK Government Analysis Function, GovS 010: Analysis Functional Standard, 2022. [Online]. Available: https://www.gov.uk/government/publications/government-analysis-standard
  4. W3Schools, HTML Colours – Hexadecimal Notation. [Online]. Available: https://www.w3schools.com/colors/colors_hexadecimal.asp
  5. D. J. Spiegelhalter, The Art of Statistics: Learning from Data, Penguin, 2019.