Computing Science for non-continuing students

1. Computing Science for non-continuing students Dr Helen PurchaseSchool of Computing ScienceUniversity 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/) • 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 n 2

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