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

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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

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Degree structure

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

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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

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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

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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

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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

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

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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

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

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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

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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

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

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helen.purchase@glasgow.ac.uk

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