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CM10311: Discrete mathematics and databases

[Page last updated: 27 October 2020]

Follow this link for further information on academic years Academic Year: 2020/1
Further information on owning departmentsOwning Department/School: Department of Computer Science
Further information on credits Credits: 6      [equivalent to 12 CATS credits]
Further information on notional study hours Notional Study Hours: 120
Further information on unit levels Level: Certificate (FHEQ level 4)
Further information on teaching periods Period:
Semester 1
Further information on unit assessment Assessment Summary: CW 25%, EX 75%
Further information on unit assessment Assessment Detail:
  • Coursework (CW 25%)
  • Exam (EX 75%)
Further information on supplementary assessment Supplementary Assessment:
Like-for-like reassessment (where allowed by programme regulations)
Further information on requisites Requisites:
Description: Aims:
To introduce students to a mathematical basis to support computer science in general and other Computer Science units in particular.
To provide a systems-based understanding of data, data modelling, storage, access, retrieval and protection.

Learning Outcomes:
1. Calculate and reason with sets, functions and relations using the language of set theory.
2. Write formal propositional and predicate calculus formulae to express mathematical assertions, reason formally, and recognise when formulae are correct.
3. Recognise and reason about elementary number theory.
4. Recognise rigorous mathematical proofs in ordinary mathematical language and write elementary proofs.
5. Demonstrate understanding of the nature, capture, formal description and representation of data and its processing.
6. Be conversant with emerging database developments such as big data, data mining, and cloud-based data storage, and be able to critically review their professional, legal and ethical implications.

Skills:
Use of IT (T/F, A), Problem Solving (T/F, A), Application of Number (T/F,A).

Content:
Propositional and predicate calculus.
Sets, functions, and relations (e.g. injections, surjections, bijections, and relational composition).
Elementary number theory (e.g. prime numbers, induction and modular arithmetic, rational numbers).
Basic mathematical proofs using induction.
Data modelling methodologies (e.g. UML and relational and non-relational models).
Data management and exchange techniques (e.g. relational algebra, SQL, normalization, and XML)
Datamining, big data and pattern discovery in large datasets.
Cloud-based, distributed data storage and its professional, legal and ethical implications.
Further information on programme availabilityProgramme availability:

CM10311 is Compulsory on the following programmes:

Department of Computer Science
  • USCM-AFB06 : BSc(Hons) Computer Science (Year 1)
  • USCM-AAB07 : BSc(Hons) Computer Science with Study year abroad (Year 1)
  • USCM-AKB07 : BSc(Hons) Computer Science with Year long work placement (Year 1)
  • USCM-AFB27 : BSc(Hons) Computer Science and Artificial Intelligence (Year 1)
  • USCM-AAB27 : BSc(Hons) Computer Science and Artificial Intelligence with Study year abroad (Year 1)
  • USCM-AKB27 : BSc(Hons) Computer Science and Artificial Intelligence with Year long work placement (Year 1)
  • USCM-AFM01 : MComp(Hons) Computer Science (Year 1)
  • USCM-AAM02 : MComp(Hons) Computer Science with Study year abroad (Year 1)
  • USCM-AKM02 : MComp(Hons) Computer Science with Year long work placement (Year 1)
  • USCM-AFM27 : MComp(Hons) Computer Science and Artificial Intelligence (Year 1)
  • USCM-AAM27 : MComp(Hons) Computer Science and Artificial Intelligence with Study year abroad (Year 1)
  • USCM-AKM27 : MComp(Hons) Computer Science and Artificial Intelligence with Year long work placement (Year 1)

Notes: