Course Outline
Day One: Language Fundamentals
- Course Overview
-
Introduction to Data Science
- Defining Data Science
- The Data Science Workflow
- Overview of 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
- The Character Data Type
- File Input/Output Operations
- Lists
-
Functions
- Fundamentals of Functions
- Understanding Closures
- Using lapply and sapply
- DataFrames
- Practical Labs for All Sections
Day Two: Intermediate R Programming
- DataFrames and File I/O Techniques
- Importing Data from Files
- Data Preparation Strategies
- Utilizing Built-in Datasets
-
Data Visualization
- Base 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 with R
- Core Statistical Functions
- Handling Missing Values (NA)
- Probability Distributions (Binomial, Poisson, Normal)
-
Regression Analysis
- Introduction to Linear Regression
- Recommendation Systems
- Text Processing (tm package and Word Clouds)
-
Clustering Techniques
- Introduction to Clustering
- K-Means Algorithm
-
Classification Methods
- Introduction to Classification
- Naive Bayes
- Decision Trees
- Model Training with the caret Package
- Evaluating Algorithm Performance
-
R and Big Data Integration
- Connecting R to Databases
- Overview of the Big Data Ecosystem
- Practical Labs for All Sections
Requirements
- A foundational background in programming is recommended
Prerequisites & Setup
- A modern laptop
- The latest version of RStudio and the R environment installed
Custom Corporate Training
Training solutions designed exclusively for businesses.
- Customized Content: We adapt the syllabus and practical exercises to the real goals and needs of your project.
- Flexible Schedule: Dates and times adapted to your team's agenda.
- Format: Online (live), In-company (at your offices), or Hybrid.
Price per private group, online live training, starting from 4800 € + VAT*
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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.
