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Course Outline
Day One: Foundations of the Language
- Course Overview
- Understanding Data Science
- Defining 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
- Working with Matrices
- String and Text Processing
- Character data types
- File Input/Output
- Lists
- Functions
- Overview of Functions
- Closures
- Utilizing lapply/sapply
- DataFrames
- Hands-on Labs for each topic
Day Two: Intermediate R Programming
- DataFrames and File I/O
- Ingesting data from files
- Data Preparation
- Utilizing Built-in Datasets
- Data Visualization
- The Graphics Package
- Using plot() / barplot() / hist() / boxplot() / scatter plot
- Heat Maps
- The ggplot2 package (qplot(), ggplot())
- Exploratory Analysis with Dplyr
- Hands-on Labs for each topic
Requirements
- A foundational understanding of programming is advantageous
Target Audience
- Data analysts
14 Hours
Testimonials (2)
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.
