Course Outline
Day One: Language Fundamentals
- Course Introduction
-
Overview of Data Science
- Definition of 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 handling
- File Input/Output (IO)
- Lists
-
Functions
- Introduction to Functions
- Closures
- Using lapply and sapply functions
- DataFrames
- Practical Labs covering all sections
Day Two: Intermediate R Programming
- DataFrames and File Input/Output
- Reading data from various file formats
- Data Preparation and Cleaning
- Utilising Built-in Datasets
-
Data Visualization
- The Base Graphics Package
- plot(), barplot(), hist(), boxplot(), and scatter plots
- Heat Maps
- The ggplot2 package (qplot(), ggplot())
- Data Exploration Using Dplyr
- Practical Labs covering all sections
Day Three: Advanced Programming with R
-
Statistical Modelling with R
- 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 using the caret package
- Algorithm Evaluation
-
R and Big Data
- Connecting R to databases
- The Big Data Ecosystem
- Practical Labs covering all sections
Requirements
- A foundational understanding of programming is recommended
Prerequisites
- A modern laptop
- The latest version of R Studio 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 3900 € + 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.
