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
- Matrices
-
String and Text Manipulation
- Character Data Types
- File Input/Output
- Lists
-
Functions
- Getting Started with Functions
- Closures
- lapply and sapply functions
- DataFrames
- Practical Labs for all sections
Day Two: Intermediate R Programming
- DataFrames and File I/O
- Importing Data from Files
- Data Preparation
- Built-in Datasets
-
Visualization
- Graphics Package
- plot(), barplot(), hist(), boxplot(), and scatter plots
- Heat Maps
- ggplot2 package (qplot(), ggplot())
- Data Exploration with dplyr
- Practical Labs for all sections
Day Three: Advanced Programming with R
-
Statistical Modeling in R
- Statistical Functions
- Handling NA Values
- Distributions (Binomial, Poisson, Normal)
-
Regression
- Introduction to Linear Regression
- Recommendations
- Text Processing (tm package and Word Clouds)
-
Clustering
- Introduction to Clustering
- KMeans
-
Classification
- Introduction to Classification
- Naive Bayes
- Decision Trees
- Training Models using the caret package
- Evaluating Algorithms
-
R and Big Data
- Connecting R to Databases
- The Big Data Ecosystem
- Practical Labs for all sections
Requirements
- A basic background in programming is recommended
Setup
- A modern laptop
- Latest versions of RStudio and the R environment must be installed
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
Working with functions in R.
Mustafa Kirmizigul - Senatsverwaltung fur Bildung, Jugend und Familie
Course - Data Analytics With R
Machine Translated
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.
