Building Reproducible Analytical Pipelines

This course is based on my book titled Building Reproducible Analytical Pipelines with R. This course focuses only on certain aspects that are discussed in greater detail in the book.

This course is my take on setting up code that results in some data product. This code has to be reproducible, documented and production ready. Not my original idea, but introduced by the UK’s Analysis Function.

The basic idea of a reproducible analytical pipeline (RAP) is to have code that always produces the same result when run, whatever this result might be. This is obviously crucial in research and science, but this is also the case in businesses that deal with data science/data-driven decision making etc.

In this course each of you will develop a data product. A data product is anything that requires data as an input. This can be a very simple report in PDF or Word format or a complex web app. This website is actually also a data product, which I made using the R programming language. In this course we will not focus too much on how to create automated reports or web apps (but I’ll give an introduction to these, don’t worry) but our focus will be on how to set up a pipeline that results in these data products in a reproducible way.

No, being a master in machine learning is not enough to become a data scientist. Actually, the older I get, the more I think that machine learning is almost optional. What is not optional is knowing how:

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