Do we know our data, as good as we know our tools? by Mani Sarkar & Jeremie Charlet

For many of us who are developer turning data scientist, we are always concerned about how to build a model, train it, etc... And yes, we want the best accuracy (close to 99%). But as every seasoned data scientist will always advise us, we need first and foremost to understand our data, ensure it’s clean and prepared before doing any training on it. During the conference, we will explore multiple problems occurring during data analysis or preparation and for each a technique to solve them (from a list of them). You will go away with a number of resources to explore at your own pace. We will cover these categories of problems: dirty data disparate datasets - needing normalisation too much information to process and others… We will cover some of these techniques: analysis - detecting misleading data, outliers, specific time series issues cleaning - deal with missing/ambiguous values, outliers, generating synthetic data, resampling preparation - using statistical and physics functions, dimensionality reduction, feature selection, resampling And using different kinds of plots relevant at different stages.