Course code | Title | Language | Price | # | Unit | Startdate | Hour | Enddate | Location | Signup | |
---|---|---|---|---|---|---|---|---|---|---|---|

R004 | R track | on your request | on your request | Contact Us |

# R track

R track

## Overview

R started as a statistical programming tool, as the open source version of S. Unlike SPSS it requires some programming skills, since it is no drag and drop tool.

The CRAN repository holds numerous R packages where you can find several prepackaged functions which can turn out to be handy in many situations.

This track is a combination of three courses: “Getting started with R”, “Data science with R” and “Advanced R”

Essentially, this track helps you to evolve from an R-starter to an advanced R programmer

- Getting started with R

--- Data types

--- Processing data with predefined functions

--- Loading and using packages

--- All kinds of plotting with R

- Data science with R

--- The process

--- Building recommendation engines

--- Building classifiers

- Advanced R

--- R markdown and Slidify

--- R shiny: interactive data products

Hands on exercises on all topics are offered

Learning objectives:

- Introducing working with R

- Learn simple data processing techniques and know how to use them with R

- Discover the CRAN repository

- Understanding the data science process and knowing where predictions fit in

- Make recommendations in R

- Implement machine learning ideas

- Learning to create documentation and presentations with R code and results

- Building interactive data products using R

- Optionally understanding the link between R and Big Data

## Topics

For a more complete topic overview, we refer to the topic pages of the individual courses: “Getting started programming with R”, “Data science and prediction models with R” and “Building data products with R”

Day 1

CHAPTER 1: Introducing R and RStudio

CHAPTER 2: Data

CHAPTER 3: Data processing

CHAPTER 4: The CRAN repository

CHAPTER 5: Plotting

CHAPTER 6: Re-using code

Day 2

CHAPTER 1: The data science process in R

CHAPTER 2: The CRAN repository

CHAPTER 3: Recommendation engines

CHAPTER 4: Machine learning techniques

CHAPTER 5: Upgrading your model

CHAPTER 6: Deep learning with R

Day 3

CHAPTER 1: R markdown

CHAPTER 2: R slidify

CHAPTER 3: R shiny

CHAPTER 4 (optionally): R and Big Data

## Prerequisites

- R can be regarded as a programming language. No prior knowledge is required though. We will start slowly with all programming skills, trying to offer a hands on head start for everyone.

- No mathematical knowledge of the machine learning techniques is required, but in practice it might be useful to understand what you are applying. Therefore we refer to the course “The math behind data science”

- Some knowledge of css will turn out handy to style your presentation, but it is not required

## Audience

R started as a statistical tool, but many machine learning techniques are available as well. Everyone who is willing to use descriptive statistics, more general statistics and machine learning techniques can benefit from the usage of R.

The course is aimed to a wide variety of people: from management over BI personnel to developers.