Course Outline
Day One: Fundamentals of the Language
- Course Overview
-
Introduction to Data Science
- Defining Data Science
- The Data Science Workflow
- Overview of the R Language
- Variables and Data Types
- Control Flow (Loops and Conditional Statements)
-
R Data Structures: Scalars, Vectors, and Matrices
- Creating R Vectors
- Working with Matrices
-
String and Text Handling
- Character Data Types
- File Input and Output (I/O)
- Using Lists
-
Function Programming in R
- Basics of Functions
- Closures
- The lapply and sapply functions
- Working with DataFrames
- Practical Labs for All Modules
Day Two: Intermediate R Programming
- DataFrames and File Handling
- Importing Data from External Files
- Data Cleaning and Preparation
- Utilizing Built-in Datasets
-
Data Visualization Techniques
- The Base Graphics Package
- Using plot(), barplot(), hist(), boxplot(), and scatter plots
- Generating Heat Maps
- The ggplot2 package (qplot() and ggplot())
- Data Exploration Using Dplyr
- Practical Labs for All Modules
Day Three: Advanced Programming with R
-
Statistical Modeling in R
- Statistical Functions
- Handling Missing Values (NA)
- Probability Distributions (Binomial, Poisson, Normal)
-
Regression Analysis
- Introduction to Linear Regression
- Recommendations
- Text Processing (tm package and Wordclouds)
-
Clustering Algorithms
- Overview of Clustering
- KMeans Clustering
-
Classification Techniques
- Overview of Classification
- Naive Bayes
- Decision Trees
- Model Training with the caret package
- Algorithm Evaluation
-
R and Big Data Integration
- Connecting R to Databases
- The Big Data Ecosystem
- Practical Labs for All Modules
Requirements
- A foundational understanding of programming principles is recommended
Environment Setup
- A contemporary laptop computer
- The most recent version of R Studio and the R environment installed and configured
Testimonials (7)
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
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.
