Study the flow of data in Azure machine learning (Azure ML) in this data science course. We explain how to prepare your data and compare continuous and categorical variables. We then show you how to remove repeated values and outliers and study the quantification of information and the scaling of data.

The course introduces you to the data flow in Azure ML and covers ‘batch’ and real-time processing. We examine the different types of ‘joins’ you can use on your data. A ‘data join’ occurs when two data sets are combined side by side. We teach you how to use the R and Python programming languages with Azure and establish their role in completing data science projects.

The course then moves on to data sampling and preparation. We compare continuous and categorical variables and define ‘quantization’. ‘Data munging’ or ‘data wrangling’ is the process of mapping raw data into a readable format and we demonstrate its value while establishing how time-consuming it can be. We explain how to handle errors and outliers in your project and break down the use of ‘scaling’ with Python, R or Azure ML. This course is useful for anyone in scientific research in general or data science in particular.