Welcome to Data Analysis and Visualisation. This module is an exciting journey into the field of data science and its applicability to the research and industrial world. It teaches students to collect, select and model various data originated from publically available sources. This module is perhaps one the best practical exercises in analysing data in which you will be able to ultimately create effective web-based tools for exploring and explaining your data. In this module, you will be working on the preparation of a large dataset as well as pre-processing, visualising and ultimately exploring your data with various practical data analytics and machine learning techniques.
Shiny - https://rstudio.github.io/shiny/tutorial/#welcome
A step by step explanation of Principal Component Analysis by Zakaria Jaadi; https://towardsdatascience.com/a-step-by-step-explanation-of-principal-component-analysis-b836fb9c97e2 | Principal Component Analysis by Mark Richardson (2009) PCA helps you interpret your data, but it will not always find the important patterns by Jake Lever, Marin Krzywinski & Naomi Altman, Nature Methods 14, pages641–642 (2017); https://www.nature.com/articles/nmeth.4346
Learning the parts of objects by non-negative matrix factorization by Daniel D. Lee & H. Sebastian Seung, Nature volume 401, pages 788–791 (1999); https://www.nature.com/articles/44565
Visualizing Data using t-SNE by Laurens van der Maaten & Geoffrey Hinton; Journal of Machine Learning Research, Volume 9, pages 2579-2605 (2008); https://lvdmaaten.github.io/tsne/