This course teaches exploratory data analysis skills using the Microsoft R Server implementation known as RevoScaleR. This product is in most ways functionally equivalent to the open source CRAN-R. RevoScaleR offers three significant benefits over its open source brother: the ability to run analyses in parallel across different servers, the ability to "chunk" data for evaluation and bypass the in-memory limitation of R, and the ability to read more natively from data sources like SQL Server, Hadoop, and Spark.
Programming R for Data Science is taught by Anders Stockmarr (on the faculty of Technical University of Denmark.) For US audiences, his accent requires some getting used to. He places emphasis on unexpected syllables and has a unique way of pronouncing many things. I found it helpful to use headphones and to adjust the playback speed of the recordings. It is worth making the effort to understand Dr.
Principles of Machine Learning (DAT203.2) is the 7th in a series of 10 courses that form the Microsoft Professional Program in Data Science. It proves that the further you get into this 10-course sequence, the more enjoyable the classes become. Similar to Data Science Orientation, this class is co-led by Cynthia Rudin and Steve Elston.
Data Science Essentials (DAT203) marks the point where we have enough foundation that we can start forming a bigger picture of data science. To that goal, the course provides this definition:
Data Science is the exploration and quantitative analysis of all available structured and unstructured data to develop understanding, extract knowledge, and formulate actionable results.