This course introduces the concepts, motivation, and techniques for creating a Reproducible Analytical Pipeline (RAP). It explains the benefits of RAP and gives guidance to help you overcome common problems with it.
Learn more on Introduction to Reproducible Analytical Pipelines (RAP)This course will give an overview of the benefits and main features of RMarkdown.
Learn more on Introduction to RMarkdownThis course will give you an understanding of Sparklyr, the R interface to the distributed processing tool Spark.
Learn more on Introduction to SparklyrThis eLearning course aims to introduce participants to the main concepts of disclosure control.
Learn more on Introduction to statistical disclosure controlThis course will give you an understanding of unit testing: the practice of designing, creating and executing tests for your code in both R and Python.
Learn more on Introduction to unit testingThis course has been designed for beginners in the field of machine learning (ML).
Learn more on Machine learning in RIn this course you will learn important concepts relating to modular code and how to convert code to functions. There are modules in either R or Python.
Learn more on Modular Programming in Python and RIn this introductory course, we will cover the basics of Natural Language Processing (NLP) topics.
Learn more on Natural Language Processing (NLP) in RThis course will teach you how to build packages using your own custom functions and import them in both Python and R.
Learn more on Packaging and documentationLoops, Control Flow and the ability to write your own functions are valuable programming tools. This short course covers and introduction to these topics and demonstrates areas for additional learning.
Learn more on Python control flow loops and functions