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Course Outline
Day One: Language Fundamentals
- Course Overview
- Foundations of Data Science
- Definition of Data Science
- The Data Science Workflow
- Introduction to the R Language
- Variables and Data Types
- Control Flow (Loops and Conditionals)
- R Scalars, Vectors, and Matrices
- Creating R Vectors
- Matrices
- String and Text Handling
- Character Data Types
- File Input/Output
- Lists
- Functions
- Function Basics
- Closures
- lapply/sapply Functions
- DataFrames
- Practical Labs for all modules
Day Two: Intermediate R Programming
- DataFrames and File I/O
- Importing Data from Files
- Data Preparation Techniques
- Utilizing Built-in Datasets
- Visualization
- Graphics Package
- plot(), barplot(), hist(), boxplot(), and scatter plots
- Heat Maps
- ggplot2 package (qplot(), ggplot())
- Data Exploration Using dplyr
- Practical Labs for all modules
Requirements
- A foundational background in programming is recommended.
Target 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.
