On completion of this module students will have explored best practices in managing, analysing and presenting their scientific data as they move forward in their research careers. Students will participate in tasks highlighting importance of data interpretation, communicating their findings in an accurate and appropriate manner to the wider scientific community. Specifically, students will:

• Organise and manipulate data on a computer using a variety of different tools
• Familiarise themselves with a Unix environment
• Navigate and explore biological databases
• Encrypt/decrypt data
• Apply statistical methods and hypothesis testing to life science datasets
• Explore various concepts in data management
• Communicate data using a variety of graphs and tables, while also objectively critiquing each method
• Answer questions pertaining to data management and analysis in life sciences.

Skills Developed

In this module, the students will be introduced to the following skills, which will be developed throughout its duration:
• Using Python/Perl programming for easy data manipulation
• Effective presentation and writing of technical and scientific information
• Apply the R statistical language to analyse biological datasets
• Learn concepts in data encryption and storage

1. Getting started with data
2. The data management plan
3. File formats and data encryption
4. Public biological data and databases
5. Data compression
6. Basic statistical analysis for data science
7. Big data and Machine Learning
8. Clustering methods in statistics
9. Hypothesis testing in statistics
10. Phylogenetics as a data case study
11. Displaying your data (and what not to do)

Not applicable to this module.

• Feedback individually to students, post-assessment
• Peer review activities

Not yet recorded.