Course Outline
Day One: Fundamentals of R Programming
- Course Overview and Objectives
-
Data Science Principles
- Definition and Scope of Data Science
- Standardized Data Science Workflow
- Introduction to the R Language for Government Applications
- Variables and Data Types
- Control Flow Logic (Iteration and Conditional Statements)
-
R Data Structures: Scalars, Vectors, and Matrices
- Vector Construction and Definition
- Matrix Operations
-
String and Text Manipulation
- Character Data Handling
- File Input and Output (I/O) Operations
- List Structures
-
Function Creation and Usage
- Fundamentals of Defining Functions
- Closures and Enclosure
- Application of lapply and sapply Functions
- Data Frames Structure
- Practical Exercises for All Modules
Day Two: Intermediate R Programming Techniques
- Data Frames and File I/O Procedures
- Ingesting Data from External Files
- Data Cleaning and Preparation Methods
- Utilization of Built-in Datasets
-
Data Visualization Strategies
- Base Graphics Package
- Plotting Functions (plot, barplot, hist, boxplot, and scatter plots)
- Heat Map Generation
- ggplot2 Package Implementation (qplot and ggplot)
- Data Exploration Using the dplyr Package
- Practical Exercises for All Modules
Day Three: Advanced Programming and Analysis in R
-
Statistical Modeling with R
- Core Statistical Functions
- Handling Missing Data (NA Values)
- Probability Distributions (Binomial, Poisson, and Normal)
-
Regression Analysis
- Fundamentals of Linear Regression
- Algorithmic Recommendations
- Text Processing and Analysis (tm Package and Word Clouds)
-
Clustering Techniques
- Introduction to Clustering Methodologies
- K-Means Clustering
-
Classification Algorithms
- Introduction to Classification Tasks
- Naive Bayes Classifier
- Decision Tree Algorithms
- Model Training via the caret Package
- Algorithm Performance Evaluation
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R Integration with Big Data Systems
- Establishing Database Connections from R
- Overview of the Big Data Ecosystem
- Practical Exercises for All Modules
Requirements
- Foundational programming experience is recommended
System Requirements
- A compatible modern laptop computer
- Installation of the latest version of R Studio and the R environment
Testimonials (7)
I finally understood the idea of the pipes and how to use R in our work context.
Katrin Groth - Senatsverwaltung fur Bildung, Jugend und Familie
Course - Data Analytics With R
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.
