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Course Outline
Day One: Language Basics
- Course Introduction
- About Data Science
- Defining Data Science
- The Data Science Process
- Introducing the R Language
- Variables and Data Types
- Control Structures (Loops and Conditionals)
- R Scalars, Vectors, and Matrices
- Defining R Vectors
- Matrices
- String and Text Manipulation
- Character Data Type
- File Input/Output
- Lists
- Functions
- Introducing Functions
- Closures
- lapply/sapply Functions
- DataFrames
- Labs covering all sections
Day Two: Intermediate R Programming
- DataFrames and File Input/Output
- Reading Data from Files
- Data Preparation
- Built-in Datasets
- Visualisation
- Graphics Package
- plot(), barplot(), hist(), boxplot(), and Scatter Plots
- Heat Maps
- ggplot2 Package (qplot(), ggplot())
- Exploration with dplyr
- Labs covering all sections
Requirements
- A basic programming background is preferred
Audience
- Data analysts
14 Hours
Testimonials (3)
a multitude of points
Joanna - Instytut Ekonomiki Rolnictwa i Gospodarki Zywnosciowej-PIB
Course - Statistical Analysis with Stata and R
knowledge of the trainer, tailor based, all topics covered
eleni - EUAA
Course - Forecasting with R
The real life applications using Statcan and CER as examples.
