Course Outline
Day One: Core Language Fundamentals
- Course Overview
-
Introduction to 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 I/O Operations
- Lists
-
Functions
- Getting Started with Functions
- Closures
- Using lapply and sapply
- DataFrames
- Labs for all sections
Day Two: Intermediate R Programming
- DataFrames and File I/O
- Importing Data from Files
- Data Preparation Techniques
- Utilizing Built-in Datasets
-
Data Visualization
- Graphics Package
- Creating plots using plot(), barplot(), hist(), boxplot(), and scatter plots
- Heat Maps
- Using the ggplot2 package (qplot(), ggplot())
- Data Exploration with dplyr
- Labs for all sections
Day Three: Advanced R Programming
-
Statistical Modeling with R
- Statistical Functions
- Handling Missing Values (NA)
- Probability Distributions (Binomial, Poisson, Normal)
-
Regression Analysis
- Introduction to Linear Regression
- Recommendations
- Text Processing (tm package and Wordclouds)
-
Clustering
- Introduction to Clustering
- KMeans Algorithm
-
Classification
- Introduction to Classification
- Naive Bayes
- Decision Trees
- Training Models with the caret package
- Evaluating Algorithm Performance
-
R and Big Data
- Connecting R to Databases
- Overview of the Big Data Ecosystem
- Labs for all sections
Requirements
- A basic background in programming is recommended
Setup
- A modern laptop
- The latest version 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
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
the clarity with which he explained the entire course, as well as the willingness to revisit topics in the syllabus when necessary
Carlos Eloy - AMERICAN EXPRESS COMPANY MEXICO
Course - Data Analytics With R
Machine Translated
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.
