Course Outline
Day One: Language Basics
- Course Introduction
-
Understanding Data Science
- Defining Data Science.
- The Data Science Process.
- Introduction to the R Language
- Variables and Data Types
- Control Structures (Loops and Conditionals)
-
R Scalars, Vectors, and Matrices
- Defining R Vectors.
- Matrices.
-
String and Text Manipulation
- Character data type.
- File Input/Output.
- Lists
-
Functions
- Introduction to Functions.
- Closures.
- Using lapply/sapply functions.
- DataFrames
- Labs for all sections
Day Two: Intermediate R Programming
- DataFrames and File I/O
- Reading data from files.
- Data Preparation.
- Built-in Datasets.
-
Visualization
- Base Graphics Package.
- Using plot(), barplot(), hist(), boxplot(), and scatter plots.
- Heat Maps.
- ggplot2 package (qplot(), ggplot()).
- Data Exploration with dplyr
- Labs for all sections
Day Three: Advanced Programming with R
-
Statistical Modeling with R
- Statistical Functions.
- Handling Missing Values (NA).
- Distributions (Binomial, Poisson, Normal).
-
Regression
- Introduction to Linear Regression.
- Recommendation Systems.
- Text Processing (tm package / Word Clouds).
-
Clustering
- Introduction to Clustering.
- K-Means Clustering.
-
Classification
- Introduction to Classification.
- Naive Bayes.
- Decision Trees.
- Training using the caret package.
- Evaluating Algorithms.
-
R and Big Data
- Connecting R to databases.
- The Big Data Ecosystem.
- Labs for all sections
Requirements
- A basic background in programming is recommended.
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.
