Dr Tugkan Batu

This course is compulsory on the MSc in Mathematics and Computation. This course is available on the MPA in Data Science for Public Policy, MSc in Data Science, MSc in Financial Statistics, MSc in Financial Statistics (Research), MSc in Operations Research & Analytics, MSc in Statistics and MSc in Statistics (Research). This course is available with permission as an outside option to students on other programmes where regulations permit.

The course is compulsory for students on the MSc Mathematics and Computation.

Good general knowledge of mathematics, including familiarity with abstract concepts and mathematical proofs. A willingness to cope with technical details of computer usage, and with a rapid introduction to programming.

Introduction to programming in Python.  Introduction to the theory of algorithms: running time and correctness of an algorithm.  Recursion.  Data structures:  arrays, linked lists, stacks, queues, binary search trees.  Sorting algorithms.  Greedy algorithms.  Dynamic programming. Online algorithms.

10 hours of seminars and 22 hours of lectures in the Autumn Term.

Before the start of the Autumn Term, there will be 3 hours of pre-sessional, in-person programming tutorial and additional online tutorials for self study.

Weekly exercises are set and marked. Many of these will require implementation of programming exercises in Python.

T H Cormen, C E Leiserson, R L Rivest and C Stein, Introduction to Algorithms, 3rd or 4th edition, The MIT Press.

Exam (75%), duration: 120 Minutes in the January exam period.

Problem sets (25%).