Course code Title Language Price # Unit Startdate Hour Enddate Location Signup
R004 R track English €1275.00 3 Day(s) 04-12-2017 09u00 06-12-2017 Kontich Subscribe
R004 R track Dutch €1275.00 3 Day(s) 11-06-2018 09u00 13-06-2018 Kontich Subscribe
R004 R track on your request on your request Contact Us

R track

R track

Overview

Course code: 
R004
Duration: 
3
Time Unit: 
Day(s)
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

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

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

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.