Course Outline
Day One: Core Language Concepts
- Course Introduction
-
Overview of 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
- Matrices
-
String and Text Manipulation
- Character Data Types
- File Input/Output
- Lists
-
Functions
- Function Basics
- Closures
- lapply/sapply functions
- DataFrames
- Practical Labs for all sections
Day Two: Intermediate R Programming
- DataFrames and File I/O
- Loading Data from Files
- Data Preparation Techniques
- Built-in Datasets
-
Visualization
- Base Graphics Package
- plot() / barplot() / hist() / boxplot() / scatter plots
- Heat Maps
- ggplot2 package (qplot(), ggplot())
- Data Exploration Using Dplyr
- Practical Labs for all sections
Day Three: Advanced R Programming
-
Statistical Modeling in R
- Statistical Functions
- Handling NA Values
- Distributions (Binomial, Poisson, Normal)
-
Regression Analysis
- Introduction to Linear Regression
- Recommendations
- Text Processing (tm package / Wordclouds)
-
Clustering
- Clustering Basics
- KMeans Algorithm
-
Classification
- Classification Basics
- Naive Bayes
- Decision Trees
- Model Training with the caret package
- Algorithm Evaluation
-
R and Big Data
- Database Connectivity
- The Big Data Ecosystem
- Practical Labs for all sections
Requirements
- A basic programming background is recommended
Environment Setup
- A modern laptop
- The latest version of RStudio and the R environment installed
Testimonials (7)
The real life applications using Statcan and CER as examples.
Matthew - Natural Resources Canada
Course - Data Analytics With R
His knowledge, and the codes were already written in the files so I could study after the classes and practice on my own.
GLORIA ADANNE - Natural Resources Canada
Course - Data Analytics With R
Lots of R coding provided and good examples
Kasia - Natural Resources Canada
Course - Data Analytics With R
Extensive language and well-developed. Also a wealth of supporting information available online.
Michel - Natural Resources Canada
Course - Data Analytics With R
I liked that the trainer made sure we all understood and were following the lectures. if we had a problem, he stopped and helped us fix it.
Cesar - AMERICAN EXPRESS COMPANY MEXICO
Course - Data Analytics With R
The tool was interesting and I see the use. I would like to learn about more about it.
- Teleperformance
Course - Data Analytics With R
New tool which is “R” and I find it interesting to know the existence of such tool for data analysis.
