Course Outline
Day One: Foundational Language Concepts
- Course Orientation
-
Overview of Data Science
- Definition and Scope
- Methodologies for Executing Data Science Projects
- Introduction to the R Programming 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 and Output Operations
- Lists
-
Functions
- Function Fundamentals
- Closures
- Applying lapply and sapply
- DataFrames
- Practical Labs for All Modules
Day Two: Intermediate R Programming Techniques
- DataFrame Management and File I/O
- Ingesting Data from External Files
- Data Preparation Strategies
- Utilizing Built-in Datasets
-
Data Visualization
- Base Graphics Package
- Generating plot(), barplot(), hist(), boxplot(), and scatter plots
- Creating Heat Maps
- Using ggplot2 (qplot() and ggplot())
- Data Exploration with Dplyr
- Practical Labs for All Modules
Day Three: Advanced Programming and Analytical Modeling in R
-
Statistical Modeling with R
- Core Statistical Functions
- Handling Missing Values (NA)
- Probability Distributions (Binomial, Poisson, Normal)
-
Regression Analysis
- Principles of Linear Regression
- Algorithm Recommendations
- Text Processing (tm package and Word Clouds)
-
Clustering Techniques
- Fundamentals of Clustering
- K-Means Clustering
-
Classification Methods
- Fundamentals of Classification
- Naive Bayes Algorithm
- Decision Tree Models
- Model Training with the caret package
- Evaluating Algorithm Performance
-
Integrating R with Big Data Ecosystems
- Connecting R to Database Systems
- Overview of the Big Data Environment
- Practical Labs for All Modules
Requirements
- Prior experience with programming fundamentals is advantageous
Configuration
- A contemporary computing device
- Installation of the most recent version of R Studio and the R environment, configured for government data analysis workflows
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.
