Experienced in signal/image analysis, and new to data science, I recently was challenged with a relatively simple dataset: 100 to 200 items, about 10-20 numerical variables (in the [0-1] or percentage range), with only one variable used at present time for ranking, and 5 to 10 categorical variables, each with few options. A categorical variable takes about 2 to 4 different values.
I would like first to get insight on potential structures in such data. I have browsed Agresti's Analysis of Ordinal Categorical Data, some have advised me to invest on TDA (Topological data Analysis). Yet I do not know where to start from.
Do you have guidelines, best practices on such REAL data to gradually address the aforementioned issues, from visualization to genuine processing/inference?