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
- Working with Matrices
-
String and Text Manipulation
- Character Data Types
- File Input/Output Operations
- Lists
-
Functions
- Introduction to Functions
- Understanding Closures
- Using lapply and sapply Functions
- DataFrames
- Practical Labs for All Sections
Day Two: Intermediate R Programming
- DataFrames and File Input/Output
- Reading Data from Files
- Data Preparation Techniques
- Utilizing Built-in Datasets
-
Data Visualization
- The Graphics Package
- Using plot(), barplot(), hist(), boxplot(), and scatter plots
- Creating Heat Maps
- Using the ggplot2 Package (qplot(), ggplot())
- Data Exploration with dplyr
- Practical Labs for All Sections
Day Three: Advanced Programming with R
-
Statistical Modeling in R
- Essential Statistical Functions
- Handling Missing Data (NA)
- Common Distributions (Binomial, Poisson, Normal)
-
Regression Analysis
- Introduction to Linear Regression
- Recommendation Systems
- Text Processing (using the tm package and Word Clouds)
-
Clustering Techniques
- Introduction to Clustering
- K-Means Clustering
-
Classification Methods
- Introduction to Classification
- Naive Bayes Classifier
- Decision Trees
- Model Training using the caret Package
- Evaluating Algorithm Performance
-
R and Big Data
- Connecting R to Databases
- Overview of the Big Data Ecosystem
- Practical Labs for All Sections
Requirements
- A foundational understanding of programming is recommended
Preparation
- A modern laptop
- Installation of the latest version of R Studio and the R environment
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.
