Data Analytics and Statistical Machine Learning

Program Overview

The Data Analytics and Statistical Machine Learning module at the University of Birmingham empowers students with expertise in data integration, mining, and analysis, particularly in biological and biomedical contexts. Graduates develop a deep understanding of omics and clinical data, proficiency in analytical techniques, and a conceptual foundation in computing principles. This program is designed for students pursuing an MSc in Bioinformatics or the International Doctoral Training Programme.

Program Outline

Objectives:

Upon completion of the module, students will be able to:

• Demonstrate a thorough understanding of the complexities of omics and clinical data, including their management and semantic representation.
• Demonstrate proficiency in data integration, mining, and analysis techniques.
• Demonstrate a conceptual understanding of computing, algorithmic, and programming principles, enabling them to evaluate methodologies, develop critiques, and propose new methods.
• Effectively integrate diverse data types.
• Demonstrate self-direction and originality in problem-solving, applying appropriate modeling and optimization techniques.

Assessment:

The module is assessed through:

• Essay: 60% of the final grade
• Presentation: 40% of the final grade

Teaching:

• Module Lead: Professor Georgios Gkoutos, whose research interests lie in clinical and biomedical informatics, computational biology, and integrative and translational research.
• Teaching Methods: The module likely involves a combination of lectures, seminars, practical exercises, and potentially group projects.

Other:

• Credits: The module is worth 20 credits.
• Availability: This module is only available as part of the MSc Bioinformatics and the International Doctoral Training Programme.
• Web-based Resources: The module includes training on several widely used web-based resources, such as OMIM, TCGA, DAVID, and REACTOME.