Using R in the Enterprise

Course Overview

R has become the default data science and statistical modelling tool across all shapes and sizes of organisation. R is open source, and its functionality can be extended by community written packages, significantly increasing its power and flexibility. R can be used standalone, in a notebook environment like R Studio or Juptyer, or embedded in enterprise reporting tools such as Oracle’s OBIEE and Data Visualisation tools.

This course aims to teach people with a background in enterprise reporting, data visualisation, data, or business analysis the fundamentals of R and how to use it in an enterprise context. The lessons and labs use both local datasets and outputs, and Oracle's database and reporting tools to teach students how to perform simple data manipulation build models and visualise the results.

Who Should Attend

BI Developers, Report Authors, DBAs, Data and Business Analytsts

Previous Knowledge

Some Experience with Oracle's Data and Business Intelligence Tools, SQL or modelling.


3 Days

Course Agenda

A Brief Tour of R

  • History
  • Examples

The R Environment

  • RStudio
  • Lab – The R Environment

Data Types in R

  • Vectors, matrices, lists, and data frames
  • Lab – Data structures in R and Oracle

Conditions and Loops

  • If - Else If – Else
  • Vectorized conditionals – ifelse
  • While Loops & For Loops
  • Lab – Conditions and Loops

Functions and the
Apply Family

  • Writing Functions
  • lapply, sapply and mclapply
  • Lab – Functions and the Apply Family

Acquire Data

  • From Flat Files (readr)
  • From Oracle (ROracle)
  • From APIs (twitterR)

Tidy Data

  • Theory of Tidy Data
  • Tidy Data and Star Schemas
  • The TidyR Package and its Functions
  • Lab – Acquire and Tidy Data

Transform Data

  • Single-Table Transformations
  • Multi-Table Transformations
  • DPLYR vs SQL - Syntax Showdown
  • Lab – Transform Data

Visualize Data

  • The Grammar of Graphics
  • ggplot2 - Static Graphics with R
  • Lab – Visualize Data

Linear Models

  • Introduction to Linear Models
  • Pitfalls of Linear Models
  • Outliers and the Stories They Tell
  • Lab – Linear Models

The Predictive Modeling Pipeline

  • Feature Selection and Feature Engineering
  • Preprocessing Data for Models
  • Training and Tuning Models
  • Cross Validation
  • Interpreting the Results
  • Lab – The Predictive Modeling Pipeline

Training Schedule

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