MA214      Half Unit
Algorithms and Data Structures

This information is for the 2023/24 session.

Teacher responsible

Prof Julia Boettcher

Availability

This course is compulsory on the BSc in Data Science and BSc in Mathematics with Data Science. This course is available on the BSc in Mathematics and Economics, BSc in Mathematics with Economics and BSc in Mathematics, Statistics and Business. This course is available as an outside option to students on other programmes where regulations permit. This course is available with permission to General Course students.

Pre-requisites

Students must have completed Mathematical Proof and Analysis (MA102) or Introduction to Abstract Mathematics (MA103).

Basic Knowledge of Python is essential, such as provided by ST101 or a pre-sessional provided by the Digital Skills Lab.

Course content

Introduction to the fundamental principles of data structures and algorithms and their efficient implementation. Developing algorithmic thinking. Basic toolkit for the design and analysis of algorithms: Running time, Recurrence relations, Big-O notation, Correctness, Finite induction, Loop invariants. Tour of the most important data structures, fundamental algorithms, and algorithm design techniques: lists, stacks, queues, dynamic arrays, hash tables, priority queues, disjoint set unions, binary search trees, breadth-first search, depth-first search, minimum spanning tree computation, maximum flow, incremental and recursive algorithms, divide-and-conquer, greedy algorithms.

Teaching

This course is delivered through a combination of lectures and classes totalling a minimum of 30 hours across Winter Term.

Formative coursework

Written answers to set problems will be expected on a weekly basis.

Indicative reading

  • T H Cormen, C E Leiserson, R L Rivest & C Stein, Introduction to Algorithms, MIT Press, 3rd edition, 2009.

Assessment

Exam (80%, duration: 2 hours) in the spring exam period.
Coursework (20%) in the WT.

Key facts

Department: Mathematics

Total students 2022/23: 50

Average class size 2022/23: 13

Capped 2022/23: Yes (90)

Lecture capture used 2022/23: Yes (LT)

Value: Half Unit

Guidelines for interpreting course guide information

Course selection videos

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Personal development skills

  • Self-management
  • Problem solving
  • Application of information skills
  • Communication
  • Application of numeracy skills
  • Specialist skills