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Duration 14 hours
Course Outline
Day One: Core Language Concepts
- Course Overview
- Introduction to Data Science
- Definition of Data Science
- The Data Science Workflow
- Introduction to the R Language
- Variables and Data Types
- Control Structures (Loops and Conditionals)
- R Scalars, Vectors, and Matrices
- Creating R Vectors
- Matrices
- String and Text Manipulation
- Character Data Types
- File Input/Output
- Lists
- Functions
- Introduction to Functions
- Closures
- lapply and sapply functions
- DataFrames
- Practical Labs for all sections
Day Two: Intermediate R Programming
- DataFrames and File I/O
- Importing Data from Files
- Data Preparation
- Built-in Datasets
- Data Visualization
- Graphics Package
- plot(), barplot(), hist(), boxplot(), and scatter plots
- Heat Maps
- ggplot2 package (qplot(), ggplot())
- Data Exploration Using Dplyr
- Practical Labs for all sections
Requirements
- A basic programming background is recommended
Target Audience
- Data Analysts
Testimonials (3)
I finally understood the idea of the pipes and how to use R in our work context.
Katrin Groth - Senatsverwaltung fur Bildung, Jugend und Familie
Course - Data Analytics With R
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
