This module is aimed at biology students with little or no knowledge of programming and statistics, with the aims of
making them aware of effects of experimental design in the subsequent data analysis;
having a good understanding of technologies and methods for Bioinformatics and are introduced to basic coding and using of workflow and pipelines for their data analysis.
It will use a multidisciplinary approach integrated with programming tools and cloud environment to introduce students statistical concepts underpinning advanced data analysis and methods that are suitable for high-throughput data analysis. Theoretical concepts and detailed examples will be introduced to provide the students with key steps to perform experimental design in data collection, data analysis and results validation.
The course will present state-of-the art research in computational biology with guest lecture and enable students to critically assess statistical methods and enhance innovative thinking in data analysis.
The module is dedicated to Level 3 Biomedical Science students but prerequisites are compulsory. A pre-assessment test can be found here.
Some suggested sites where additional material can be found, are listed below:
Jupyter Notebook on CoCalc Please be aware that SageMathCloud has recently changed into Cocalc, some tutorial material can be still using the SageMathCloud name.
Recommended text books
There are no specific text books that can cover the material in the course but a large online resources that can be consulted. These resources are recommended since they will be the most up-to-date. All the tools (Jupyter notebook) and the element of R programming upgrade constantly being open source software.
However, there are a series of text books available in the library for you to consult:
- Computational Statistics - G Sawitzki
- R Programming for Bioinformatics - R. Gentleman