School of Computer Science

Module 06-32258 (2022)

Algorithms for Data Science

Level 4/M C

Peter Tino Miqing Li Semester 1 20 credits
Co-ordinator: Miqing Li
Reviewer: Peter Tino
The Module Description is a strict subset of this Syllabus Page.

Outline

Modern data science encompasses a huge range of methods – from supervised methods for learning from labelled data, to statistical pattern analysis and data mining. In this module students will study a range of modern techniques from across the data science spectrum including supervised learning, data mining, and statistical pattern recognition. The module will give the student a good understanding of how, why and when different methods work and experience of applying them in practice.

Learning Outcomes

On successful completion of this module, the student should be able to:

  • Understand and explain a range of methods and algorithms for data science
  • Be able to apply a range of algorithms to solve data science problems
  • Compare and contrast different methods, analysing their relative advantages and disadvantages
  • Make informed choices between different methods, given a data science question, and be able to justify these choices.

Assessment

  • Main Assessments: Examination (80%) and continuous assessment (20%)
  • Supplementary Assessments: Examination (100%)

Programmes containing this module