£1,800

TRN802: Advanced Analytics with Oracle’s R Technologies (London, July 2017)

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Euston House

24 Eversholt Street

London

NW1 1AD

United Kingdom

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Oracle has made significant investments in the R language with Oracle R, ROracle and Oracle R Enterprise. Using these tools, data scientists and business intelligence practitioners can work together more efficiently and can transition between these roles more easily. R has a notoriously difficult learning curve, so Rittman Mead has designed a course for Oracle professionals that draws on their existing skills to accelerate their growth into R programmers and data scientists.

Day 1 - Intro to R

A brief tour of R

  • History
  • Examples

Oracle’s R technologies

The R Environment

  • Rstudio
  • Lab – The R Environment

Types in R and their analogs in Oracle

  • Numerics, characters, logicals, factors, and dates
  • Lab – Types in R and Oracle

Data Structures in R and their analogs in Oracle

  • 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

Day 2 - Analysis Pipelines

Acquire data

  • From flat files (readr)
  • From Oracle (ROracle)
  • From APIs (twitteR)

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 Graphis
  • ggplot2 - Static graphics with R
  • Lab - Visualize Data

Day 3 – Modeling

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
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Date and Time

Location

Euston House

24 Eversholt Street

London

NW1 1AD

United Kingdom

View Map

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