Information on this page, including unit offerings, is from the 2019 academic year.
Interactive Data Analytics and Visualisation (BIO311)
|Organisational Unit||Medical, Molecular and Forensic Sciences|
|Teaching Timetables||Murdoch S2
|Description||Life science research has progressed to high-throughput systems biology which has in turn necessitated the discovery of knowledge buried deep in big biological data sets. In this unit, students will learn exploration and visualisation methods for analysing large amounts of biological data such as raw DNA sequence information. It will demonstrate how such approaches can generate user-friendly outputs for meaningful interpretation of results. Tutorials and assessments will help students to perform systems biology research and critically assess experimental design.|
|Unit Learning Outcomes||On successful completion of the unit students should be able to:
1. Understand basic data mining techniques and its application in analysing big life science data;
2. Comprehend complex datasets and identify correct and effective exploration methods to assist with data analysis;
3. Describe and master important data exploration and visualisation methodologies;
4. Develop skills to interactively and visually explore datasets at any stage of the analysis, and thus apply appropriate methods to solve any problems identified;
5. Master data visualisation methodology and become acquainted with graph-based algorithms for effective data and results presentation;
6. Understand pathway and network-related analytical methods and workflow, and apply these methods to answer difficult and practical biological questions;
7. Analyse real high-throughput life science datasets of heterogeneous sources.
|Timetabled Learning Activities||Lectures: 2 hours per week x 12 week; 2 hours per tutorial x 10, project 2 hours per week x 8.
All offerings of this unit include the equivalent of 60 hours of structured learning.
|Unit Learning Experiences||This unit will be organised into two sections. The first section is lecture-intensive and introduces widely used data exploration and machine learning methods. Students will learn data exploratory and visualisation methods based on these techniques and develop proper data analysis doctrines for complex analytical challenges. Tutorials in this unit will help students develop practical skills in interactive data analysis and give students practical examples to learn. The second section is focused on an individual project, where students will apply what they have learnt into a real discovery project. Assessments will be designed to help students successfully complete the project.
To get the most out of this unit, students should attend each lecture and tutorial where possible. Students are advised to consolidate the concepts and knowledge of the first section for completion of the project which consists of a large proportion of the marks. This unit will require basic skills in statistical analysis and basic skills in using computers. Students are expected to engage independent study and develop critical thinking skills to apply the theory and concepts covered to practical situations. Students are expected to have completed unit BIO3XX (Omics Technologies & Bioinformatics) as a prerequisite for this unit. Tutorials will be conducted on the assumption that students have already engaged with the lecture materials. Failure to do so will diminish the quality of the class discussions on the unit topics. Tutorial classes will involve practice on analysing real datasets and interactive activities related to enhance individual learning outcomes. The project and assessments will assist in your consolidation of the unit content, and help you to develop advanced skills to handle complex data of heterogeneous origin.
|Other Learning Experiences||First-hand experience in generating high-throughput -omics data on one's own and practical experience in analysing real life complex data.|
|Assessment||1. Demonstrate understanding and mastery of widely used data exploratory tools to understand complex data;
2. Display basic knowledge of machine learning, including supervised and unsupervised learning, clustering, and decision tree-based methods;
3. Explain pathway and network analytical methods, and apply them into data exploration;
4. Demonstrated advanced capability in analysing complex high throughput genomics data.
5. Show potential in applying interactive and visualisation methods for individual analysis.
The assessments in this unit consist of 3 components:
Practical data analysis project, part one - 20%
Practical data analysis project, part two - 50%
End of semester examination - 30%
|Appears in these Minors||Bioinformatics
|Internet Access Requirements||Murdoch units normally include an online component comprising materials, discussions, lecture recordings and assessment activities. All students, regardless of their location or mode of study, need to have access to and be able to use computing devices with browsing capability and a connection to the Internet via Broadband (Cable, ADSL or Mobile) or Wireless. The Internet connection should be readily available and allow large amounts of data to be streamed or downloaded (approximately 100MB per lecture recording). Students also need to be able to enter into online discussions and submit assignments online.|
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