With both pride and relief -- I get to share news of completing the Capstone course for the Microsoft Professional Program in Data Science. Over the past six months, I've been systematically working through the nine courses leading up to this final 10th course in the series.
A database analysts experience with the Microsoft Professional Program in Data Science. This is the first of 10 blog entries describing course work included in this new edX.org curriculum aligned with Microsoft data science tools. This entry concerns the overview course.
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 it's 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 with R for Data Science is taught by Anders Stockmarr (on 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 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.
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