Course Outline
Day One: Language Fundamentals
- 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 Types
- File Input/Output
- Lists
-
Functions
- Introduction to Functions
- Closures
- lapply and sapply Functions
- DataFrames
- Practical Labs for all sections
Day Two: Intermediate R Programming
- DataFrames and File I/O
- Reading Data from Files
- Data Preparation
- Built-in Datasets
-
Visualization Techniques
- Graphics Package
- plot(), barplot(), hist(), boxplot(), and Scatter Plots
- Heat Maps
- ggplot2 Package (qplot(), ggplot())
- Data Exploration with Dplyr
- Practical 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 Analysis
- Introduction to Linear Regressions
- Recommendation Systems
- Text Processing (tm package and Wordclouds)
-
Clustering Algorithms
- Introduction to Clustering
- K-Means Clustering
-
Classification Techniques
- Introduction to Classification
- Naive Bayes
- Decision Trees
- Training with the caret package
- Evaluating Algorithm Performance
-
R and Big Data
- Connecting R to Databases
- The Big Data Ecosystem
- Practical Labs for all sections
Requirements
- A foundational background in programming is recommended
Setup Requirements
- 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.
