1. Computing Science for non-continuing students Dr Helen Purchase School of Computing Science University of Glasgow helen.purchase@glasgow.ac.uk

2. Motivation Introductory courses for students who: – have a general interest in Computing Science – do not wish to study Computing Science further – do not (necessarily) have any programming experience – do not (think they) want to learn how to program – are studying any other degree programme Everyone can benefit from knowing about: – algorithmic and computational thinking – how computational processes are evident in the world around us

3. Degree structure

4. Two courses “Principles and Practise of Computing Science” “Programming Digital Media”

5. What makes these courses different? Not a typical ‘preparatory’ course to prepare students for advanced concepts in later years – programming (CS1P) – fundamental concepts (relations, sets, interaction design, circuits and registers etc.) (CS1Q) Broad, introductory focus – unexpected consequence: more advanced concepts can be taught early

6. PPCS: Freedom from constraints No requirement to focus on low-level programming and technical skills Topics described at a higher level of detail – still understandable – releases students from concerns of programming syntax and compilation errors Covers – diverse topics: databases, logic and operating systems – advanced topics: concurrency, cryptography and artificial intelligence

7. Principles Algorithms: simple constructs, procedures, functions, parameters, recursion Data structures: simple variables, arrays, graphs Boolean logic: simple statements, truth tables and circuits; simple transformations Theory: complexity, intractability

8. Practise Artificial intelligence: robots, game trees and learning Human computer interaction: usability concepts and guidelines Databases: relational structures and representations, simple normalisation Systems: concurrency, operating systems Security: cryptography, malware, safety-critical systems

9. Historical context Denning Aristotle de Morgan Boole Shannon Babbage Lovelace Kindall Mohammed al-Khwarizmi Turing Searle Dijkstra Diffie-Hellman-Merkle Mandelbrot Deep Blue

10. Lab work Raptor (flow charting) Nifty assignments (http://nifty.stanford.edu/)http://nifty.stanford.edu/ – Picobot – Black box sorting and testing Fractal software Game tree visualisation Critiques: interfaces, databases Essay on chosen current topic

11. Challenges Breadth (and intensity) of topics Pace Mathematics Engagement Lab work Class size Textbook

12. PDM: A back-door to Python Manipulation of digital media Basic control and data structures: – manipulate images by changing pixels – create sounds by iterating over samples – render lists of numbers into music – create artefacts like collages, music, and digital video special effects Show how computational primitives can manipulate media

13. What next… For the students: – high performers who have been persuaded to continue with Computing Science can do our level 2 ‘Fast Track’ programme For us: – refinement fewer topics in more depth more appropriate lab exercises – publicity

14. Personal reflections Pace Breadth: – no ‘building on’ topics – new topics Interesting historical context Fun exercises Guest lectures

15. helen.purchase@glasgow.ac.uk