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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)
it was informative and useful
Brenton - Lotterywest
Course - Building Web Applications in R with Shiny
The involvement of the trainer and good preparation of the topic.
Bruno Scibilia - Lesaffre International
Course - Advanced R Programming
Machine Translated
Day 1 and Day 2 were really straight forward for me and really enjoyed that experience.
Mareca Sithole - Africa Health Research Institute
Course - R Fundamentals
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
It was very informative and professionally held. Wojteks knowledge level was so advanced that he could basically answer any question and he was willing to put effort into fitting the training to my personal needs.
