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Course Outline
Day One: Core Language Concepts
- Course Overview
- Understanding Data Science
- Defining Data Science
- The Data Science Process.
- 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
- Introduction to Functions
- Closures
- lapply and sapply functions
- DataFrames
- Practical Labs for all modules
Day Two: Intermediate R Programming
- DataFrames and File I/O
- Importing data from files
- Data Preparation and Cleaning
- Utilizing 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 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.
