Course Outline
Day One: Language Fundamentals
- 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 and Output
- Lists
-
Functions
- Function Basics
- Closures
- 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
- Built-in Datasets
-
Visualisation
- Graphics Package
- plot() / barplot() / hist() / boxplot() / scatter plot
- Heat Maps
- ggplot2 package (qplot(), ggplot())
- Data Exploration with Dplyr
- Practical Labs for all sections
Day Three: Advanced Programming With R
-
Statistical Modelling in R
- Statistical Functions
- Handling Missing Values (NA)
- Probability Distributions (Binomial, Poisson, Normal)
-
Regression Analysis
- Introduction to Linear Regression
- Recommendation Systems
- Text Processing (tm package and Word Clouds)
-
Clustering
- Introduction to Clustering
- K-Means
-
Classification
- Introduction to Classification
- Naive Bayes
- Decision Trees
- Model training using the caret package
- Evaluating Algorithms
-
R and Big Data
- Connecting R to databases
- Big Data Ecosystem
- Practical Labs for all sections
Requirements
- A foundational background in programming is preferred
Setup
- A modern laptop
- The latest version of R Studio 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.
