Course Outline
Day One: Core Language Concepts
- Course Introduction
-
Understanding 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 Type
- File Input and Output
- Lists
-
Functions
- Introduction to Functions
- Closures
- Using lapply and sapply functions
- DataFrames
- Practical Labs for All Sections
Day Two: Intermediate R Programming
- DataFrames and File I/O
- Importing Data from External Files
- Data Preparation Techniques
- Working with Built-in Datasets
-
Data Visualization
- Using the Graphics Package
- Creating plots, bar charts, histograms, box plots, and scatter plots
- Generating Heat Maps
- Utilizing the ggplot2 package (qplot, ggplot)
- Data Exploration with Dplyr
- Practical Labs for All Sections
Day Three: Advanced Programming with R
-
Statistical Modeling in R
- Applying Statistical Functions
- Handling Missing Values (NA)
- Probability Distributions (Binomial, Poisson, Normal)
-
Regression Analysis
- Introduction to Linear Regression
- Making Recommendations
- Text Processing (tm package and Wordclouds)
-
Clustering Algorithms
- Fundamentals of Clustering
- Implementing KMeans
-
Classification Models
- Fundamentals of Classification
- Naive Bayes Algorithm
- Decision Trees
- Model Training using 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
Environment Setup
- A modern laptop computer
- 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.
