Course Outline
Day One: Language Basics
- Course Introduction
-
About Data Science
- Definition of Data Science
- Process of Conducting Data Science
- Introducing the R Language for Government Applications
- Variables and Types
- Control Structures (Loops / Conditionals)
-
Scalars, Vectors, and Matrices in R
- Defining R Vectors
- Matrices
-
String and Text Manipulation
- Character Data Type
- File Input/Output Operations
- Lists
-
Functions
- Introduction to Functions
- Closures
- lapply/sapply Functions
- DataFrames
- Labs for All Sections
Day Two: Intermediate R Programming
- DataFrames and File Input/Output
- Reading Data from Files
- Data Preparation
- Built-in Datasets
-
Visualization
- Graphics Package
- plot() / barplot() / hist() / boxplot() / Scatter Plot
- Heat Map
- ggplot2 Package (qplot(), ggplot())
- Exploration with Dplyr
- 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
- Recommendations
- Text Processing (tm Package / Wordclouds)
-
Clustering Techniques
- Introduction to Clustering
- KMeans Algorithm
-
Classification Methods
- Introduction to Classification
- Naive Bayes
- Decision Trees
- Training with the caret Package
- Evaluating Algorithms
-
R and Big Data Integration
- Connecting R to Databases
- Overview of the Big Data Ecosystem
- Labs for All Sections
Requirements
- A basic programming background is preferred.
Setup
- A modern laptop for government use.
- The latest R Studio and 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.
