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Duration 14 hours
Course Outline
Day One: Fundamental R Language Concepts
- Course Overview
- Foundations of Data Science
- Definition of Data Science
- Methodologies in Data Science Practice
- Overview of the R Programming Environment
- Variables and Data Types
- Control Flow (Iteration and Conditional Logic)
- R Data Structures: Scalars, Vectors, and Matrices
- Construction of R Vectors
- Matrix Operations
- String and Text Processing
- Character Data Types
- Input/Output Operations
- Lists
- Functionality in R
- Function Definitions
- Closures
- Application of lapply and sapply
- DataFrames
- Practical Exercises for All Modules
Day Two: Advanced R Programming
- DataFrames and File Handling
- Data Ingestion from External Files
- Data Preparation Techniques
- Utilizing Built-in Datasets
- Data Visualization
- Base Graphics Package
- Plotting Functions: plot(), barplot(), hist(), boxplot(), and scatter plots
- Heat Maps
- ggplot2 Package (qplot(), ggplot())
- Data Exploration Using Dplyr
- Practical Exercises for All Modules
Requirements
- Prior programming experience 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