Course Outline
Day One: Fundamentals of the Language
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Course Orientation
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Overview of Data Science
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Defining Data Science
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The Data Science Workflow
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Introduction to the R Language
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Variables and Data Types
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Control Flow (Loops and Conditionals)
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R Scalars, Vectors, and Matrices
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Creating R Vectors
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Matrices
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String and Text Manipulation
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Character Data Types
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File Input and Output (I/O)
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Lists
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Functions
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Function Basics
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Closures
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lapply and sapply functions
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DataFrames
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Practical Labs for all topics
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Day Two: Intermediate R Programming
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Working with DataFrames and File I/O
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Importing data from files
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Data Preparation Techniques
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Utilizing Built-in Datasets
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Data Visualization
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Graphics Package
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Functions: plot(), barplot(), hist(), boxplot(), and scatter plots
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Heat Maps
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The ggplot2 package (qplot(), ggplot())
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Data Exploration Using dplyr
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Practical Labs for all topics
<\/ul>
Day Three: Advanced Programming With R
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Statistical Modeling in R
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Statistical Functions
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Handling NA (Missing Values)
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Probability Distributions (Binomial, Poisson, Normal)
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Regression Analysis
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Introduction to Linear Regression
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Recommendations
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Text Processing (tm package and Word clouds)
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Clustering Algorithms
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Overview of Clustering
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K-Means Clustering
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Classification Algorithms
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Overview of Classification
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Naive Bayes
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Decision Trees
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Model Training using the caret package
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Algorithm Evaluation
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R and Big Data
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Connecting R to databases
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The Big Data Ecosystem
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Practical Labs for all topics
<\/ul>
- Defining Data Science
- The Data Science Workflow
- Creating R Vectors
- Matrices
- Character Data Types
- File Input and Output (I/O)
- Function Basics
- Closures
- lapply and sapply functions
Day Two: Intermediate R Programming
<\/h3>
-
Working with DataFrames and File I/O
-
Importing data from files
-
Data Preparation Techniques
-
Utilizing Built-in Datasets
-
Data Visualization
-
Graphics Package
-
Functions: plot(), barplot(), hist(), boxplot(), and scatter plots
-
Heat Maps
-
The ggplot2 package (qplot(), ggplot())
-
Data Exploration Using dplyr
-
Practical Labs for all topics
<\/ul>
Day Three: Advanced Programming With R
<\/h3>
-
Statistical Modeling in R
-
Statistical Functions
-
Handling NA (Missing Values)
-
Probability Distributions (Binomial, Poisson, Normal)
-
Regression Analysis
-
Introduction to Linear Regression
-
Recommendations
-
Text Processing (tm package and Word clouds)
-
Clustering Algorithms
-
Overview of Clustering
-
K-Means Clustering
-
Classification Algorithms
-
Overview of 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 for all topics
<\/ul>
- Graphics Package
- Functions: plot(), barplot(), hist(), boxplot(), and scatter plots
- Heat Maps
- The ggplot2 package (qplot(), ggplot())
Day Three: Advanced Programming With R
<\/h3>
-
Statistical Modeling in R
-
Statistical Functions
-
Handling NA (Missing Values)
-
Probability Distributions (Binomial, Poisson, Normal)
-
Regression Analysis
-
Introduction to Linear Regression
-
Recommendations
-
Text Processing (tm package and Word clouds)
-
Clustering Algorithms
-
Overview of Clustering
-
K-Means Clustering
-
Classification Algorithms
-
Overview of 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 for all topics
<\/ul>
- Statistical Functions
- Handling NA (Missing Values)
- Probability Distributions (Binomial, Poisson, Normal)
- Introduction to Linear Regression
- Overview of Clustering
- K-Means Clustering
- Overview of Classification
- Naive Bayes
- Decision Trees
- Model Training using the caret package
- Algorithm Evaluation
- Connecting R to databases
- The Big Data Ecosystem
Requirements
- A basic background in programming is recommended <\/ul>
- A modern laptop
- The latest version of R Studio and the R environment must be installed <\/ul>
Setup Requirements
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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.
