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Course Outline
Day One: Language Basics
- Course Introduction
- About Data Science
- Data Science Definition
- Process of Doing Data Science.
- Introducing R Language
- Variables and Types
- Control Structures (Loops / Conditionals)
- R Scalars, Vectors, and Matrices
- Defining R Vectors
- Matricies
- String and Text Manipulation
- Character data type
- File IO
- Lists
- Functions
- Introducing Functions
- Closures
- lapply/sapply functions
- DataFrames
- Labs for all sections
Day Two: Intermediate R Programming
- DataFrames and File I/O
- Reading data from files
- Data Preparation
- Built-in Datasets
- Visualization
- Graphics Package
- plot() / barplot() / hist() / boxplot() / scatter plot
- Heat Map
- ggplot2 package (qplot(), ggplot())
- Exploration With Dplyr
- Labs for all sections
Requirements
- Basic programming background is preferred
Audience
- Data analysts
14 Hours
Testimonials (5)
I genuinely enjoyed the hands passed exercises.
Yunfa Zhu - Environmental and Climate Change Canada
Course - Foundation R
The pace was just right and the relaxed atmosphere made candidates feel at ease to ask questions.
Rhian Hughes - Public Health Wales NHS Trust
Course - Introduction to Data Visualization with Tidyverse and R
the matter was well presented and in an orderly manner.
Marylin Houle - Ivanhoe Cambridge
Course - Introduction to R with Time Series Analysis
Michael the trainer is very knowledgeable and skillful about the subject of Big Data and R. He is very flexible and quickly customize the training meeting clients' need. He is also very capable to solve technical and subject matter problems on the go. Fantastic and professional training!.
Xiaoyuan Geng - Ottawa Research and Development Center, Science Technology Branch, Agriculture and Agri-Food Canada
Course - Programming with Big Data in R
A lot of knowledge - theoretical and practical.
