Course Outline
Day One: Core Language Concepts
- Course Overview
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Foundations of Data Science
- Defining Data Science
- The Data Science Workflow
- Introduction to the R Language
- Variables and Data Types
- Control Flow (Loops and Conditionals)
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R Scalars, Vectors, and Matrices
- Constructing R Vectors
- Matrix Operations
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String and Text Processing
- Character Data Types
- File Input/Output
- Lists
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Functionality in R
- Defining Functions
- Closures
- lapply/sapply Functions
- DataFrames
- Practical 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
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Data Visualization
- Base Graphics Package
- Plotting Functions (plot, barplot, hist, boxplot, scatter)
- Heatmaps
- ggplot2 Package (qplot, ggplot)
- Data Exploration Using dplyr
- Practical Labs for all Sections
Day Three: Advanced R Programming
-
Statistical Modeling with R
- Statistical Functions
- Handling Missing Values (NA)
- Probability Distributions (Binomial, Poisson, Normal)
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Regression Analysis
- Introduction to Linear Regression
- Recommendation Systems
- Text Processing (tm package and Wordclouds)
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Clustering Techniques
- Overview of Clustering
- K-Means Algorithm
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Classification Models
- Overview of Classification
- Naive Bayes
- Decision Trees
- Model Training with caret Package
- Algorithm Evaluation
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R in the Big Data Context
- Database Connectivity
- Big Data Ecosystems
- Practical Labs for all Sections
Requirements
- Prior experience in programming is recommended
Environment Setup
- A contemporary laptop computer
- Installation of the latest version of RStudio and the R runtime environment
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.
