Course Details in 2026/27 Session


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Module Title LM Data Analytics and Statistical Machine Learning
SchoolSchool of Medical Sciences
Department Inst of Cancer / Genomic Sci
Module Code 02 29783
Module Lead Dr. Mamunur Rashid
Level Masters Level
Credits 20
Semester Semester 1
Pre-requisites
Co-requisites
Restrictions None
Exclusions
Description Data analytics and statistical machine learning is a broad introductory module. It will introduce concepts from statistical machine learning (ML), with the aim to provide an in-depth understanding of classical supervised and unsupervised learning models. It will cover analysis of algorithms, implementation, optimization and explain how to structure ML projects. This will allow students to start applying ML techniques to solve problems. Students will develop understanding how to select appropriate ML tools for a given tasks/data types.
Data Analytics will build on statistical machine learning concepts, with the main aim to provide hands on experience with health and biomedical data. This will give understanding what are the basic blocks of data analysis using machine learning. This will cover various data management approaches such as quality control, tools to deal with missing data, data normalization and to visualize data. It will also introduce common techniques to evaluate model performance and to carry out model selection using frequentist and Bayesian approaches. Students will further develop their programming skills using python and R languages.
Learning Outcomes By the end of the module students should be able to:
  • Demonstrate a good understanding of complexity of omics and clinical data and their management including their semantic representation
  • Demonstrate an in-depth understanding and ability to perform Data integration, mining and analysis
  • Demonstrate conceptual understanding of Computing, Algorithms and Programming that enables the student to evaluate methodologies and develop critiques of them and, where appropriate, propose new methods
  • Understand the complexity of information available to enable the integration of diverse data types
  • Demonstrate self-direction and originality in tackling and solving problems to perform the appropriate Modelling and Optimization
Assessment 29783-01 : Essay : Coursework (60%)
29783-02 : Programming challenge with Presentation : Presentation (40%)
Assessment Methods & Exceptions Assessment:

- Programming challenge with Presentation (40%)
- Essay (60%)

Reassessment:

If a student fails the module then they will be required to repeat the failed components only.
Other
Reading List