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Scalable Data Processing in R
4.7(25)6k studentsADVANCEDEnglish
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Course Overview
The Scalable Data Processing in R course provides advanced strategies for analyzing datasets that exceed memory limits. Working with out-of-core disk-based data, you will implement split-apply-combine workflows, leverage the bigmemory and iotools packages, and process large mortgage financial records from Fannie Mae and Freddie Mac.
Who Is This Course For?
Intended for advanced R programmers, data engineers, and quantitative analysts who need to process large-scale datasets efficiently without running out of RAM.
Key Takeaways
- Analyze and manipulate large datasets directly from disk to bypass memory restrictions.
- Implement the split-apply-combine computational strategy for big data processing in R.
- Utilize specialized R packages including bigmemory and iotools for fast I/O operations.
- Work with large financial datasets such as FHFA mortgage records spanning multiple years.
About this course
Datasets are often larger than available RAM, which causes problems for R programmers since by default all the variables are stored in memory. You’ll learn tools for processing, exploring, and analyzing data directly from disk. You’ll also implement the split-apply-combine approach and learn how to write scalable code using the bigmemory and iotools packages. In this course, you'll make use of the Federal Housing Finance Agency's data, a publicly available data set chronicling all mortgages that were held or securitized by both Federal National Mortgage Association (Fannie Mae) and Federal Home Loan Mortgage Corporation (Freddie Mac) from 2009-2015.
Price
Free
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