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ST340: Programming for Data Science

Lecturer(s): Dr Paul Jenkins

Prerequisite(s): ST221 Linear Statistical Models

Commitment: 2 lectures and 1 computer laboratory per week. This module runs in Term 1.

Content: In the modern world it is very easy to generate very large amounts of data. Capturing and exploiting the important information contained within such datasets poses a number of statistical challenges. It may not even be clear how much useful information the data contains. The module will cover a variety of algorithms developed to tackle some of these challenges.

Aims: To introduce students to algorithms suitable to the analysis of large datasets.

Objectives: At the end of the module, students should be able to implement various algorithms in the context of image analysis, handwriting recognition, personalized product suggestion, spam filtering and fraud detection.

Note that there will be a cap on student numbers for this module.

Assessment: 50% coursework (three assignments) and 50% exam.

Deadline: Assignment 1: week 4, Assignment 2: week 7, Assignment 3: week 10.

​Feedback: ​Marked assignments will be returned within 2 weeks.