Course Outline
Day One: Core Language Features
- Introduction to the Course
-
Overview of Data Science
- Defining Data Science
- The Data Science Workflow
- Getting to Know the R Language
- Variables and Data Types
- Control Structures (Loops and Conditional Logic)
-
R Scalars, Vectors, and Matrices
- Creating R Vectors
- Working with Matrices
-
String and Text Manipulation
- The Character Data Type
- File Input/Output (IO)
- Lists
-
Functions
- Function Basics
- Closures
- The lapply and sapply functions
- DataFrames
- Practical Labs for all sections
Day Two: Intermediate R Programming
- DataFrames and File I/O
- Importing Data from Files
- Data Preparation Techniques
- Using Built-in Datasets
-
Data Visualization
- The Graphics Package
- Functions: plot(), barplot(), hist(), boxplot(), and scatter plots
- Heat Maps
- The ggplot2 package (using qplot() and ggplot())
- Data Exploration with dplyr
- Practical Labs for all sections
Day Three: Advanced Programming in R
-
Statistical Modeling with R
- Key Statistical Functions
- Handling Missing Values (NA)
- Distributions (Binomial, Poisson, and Normal)
-
Regression Analysis
- Introduction to Linear Regression
- Making Recommendations
- Text Processing (using the tm package and Wordclouds)
-
Clustering
- Overview of Clustering Techniques
- K-Means Clustering
-
Classification
- Introduction to Classification Algorithms
- Naive Bayes
- Decision Trees
- Model Training with the caret package
- Evaluating Algorithm Performance
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R and Big Data
- Connecting R to Databases
- The Big Data Ecosystem
- Practical Labs for all sections
Requirements
- A basic background in programming is recommended
Environment Setup
- A modern laptop
- The latest version of RStudio and the R environment must be installed
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.
