Dr Tugkan Batu

This course is available on the MPA in Data Science for Public Policy, MSc in Applicable Mathematics, MSc in Data Science, MSc in Operations Research & Analytics, MSc in Statistics, MSc in Statistics (Financial Statistics), MSc in Statistics (Financial Statistics) (Research) 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 Applicable Mathematics.

Good general knowledge of mathematics, including familiarity with abstract concepts. 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.

This course is delivered through a combination of seminars and lectures totalling a minimum of 30 hours across Autumn Term. There are also optional computer help sessions for this course. Before the start of Autumn Term, there will be 6 hours of pre-sessional programming tutorials.

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.

Exam (65%, duration: 1 hour and 30 minutes) in the spring exam period.
Coursework (25%) in the AT.
Continuous assessment (10%).