Dr Tugkan Batu

This course is compulsory on the BSc in 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.

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.

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.

This course is delivered through a combination of lectures and classes totalling a minimum of 30 hours across Lent Term. Apart from pre-recorded lecture videos, there will be a weekly live online session of an hour. Depending on circumstances, classes might be online.

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

• T H Cormen, C E Leiserson & R L Rivest, Introduction to Algorithms, MIT Press 1990 (or 2nd edn, 2001 or 3rd edn, 2009)

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

Some departments have produced short videos to introduce their courses. Please refer to the course selection videos index page for further information.