I was recently awarded flexible funding to attend the “7th Annual Summer Institute in Statistics for Big Data (SISBID)” offered by the University of Washington. The course covered the main steps for analysis and visualisation of big datasets. My PhD project involves the utilisation of various -omic techniques, and receiving advanced training in bioinformatic skills is of pivotal importance for my current and future work.
The course was divided in 4 modules, which very well complemented one another and took place over two weeks. We started by understanding how to handle and explore big datasets in the first stages of data analysis. Subsequently, two separate modules covered two complementary approaches for data analysis, supervised and unsupervised machine learning. The course ended with a training on how to best visualise big datasets.
All modules comprised theory lectures followed by hands-on labs, which allowed me to actively apply and better understand the topics discussed. Importantly, reproducibility of research and good practice in data analysis were also debated separately by each module instructors.
I started the course having basic knowledge in most of the methods discussed, and I have now a deeper understanding of the complex data-analysis that is part of my work. The skills acquired will certainly improve the quality of my research, but will also be of great importance future employment, either in an academic or industrial setting. I would like to thank the DiMeN DTP for awarding me this funding and giving me this great opportunity!
Type: Flexible Funding award