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Duration 14 hours
Course Outline
Day One: Core Language Foundations
- Orientation and Course Overview
- Foundations of Data Science
- Defining Data Science
- The Data Science Workflow.
- Overview of the R Language
- Understanding Variables and Data Types
- Control Flow (Loops and Conditional Statements)
- R Data Structures: Scalars, Vectors, and Matrices
- Creating Vectors in R
- Matrix Operations
- Handling Strings and Text Data
- Character Data Types
- Input and Output Operations
- Working with Lists
- Functionality
- Introduction to Functions
- Concept of Closures
- Using lapply and sapply
- DataFrames
- Practical Labs for all covered topics
Day Two: Intermediate R Techniques
- DataFrames and File Handling
- Ingesting Data from External Files
- Data Cleaning and Preparation
- Utilizing Built-in Datasets
- Data Visualization
- The Graphics Package
- Using plot(), barplot(), hist(), boxplot(), and scatter plots
- Generating Heat Maps
- The ggplot2 package (utilizing qplot() and ggplot())
- Data Exploration using dplyr
- Practical Labs for all covered topics
Requirements
- A foundational knowledge of programming is recommended
Target Audience
- Data analysts
Testimonials (3)
I finally understood the idea of the pipes and how to use R in our work context.
Katrin Groth - Senatsverwaltung fur Bildung, Jugend und Familie
Course - Data Analytics With R
a multitude of points
Joanna - Instytut Ekonomiki Rolnictwa i Gospodarki Zywnosciowej-PIB
Course - Statistical Analysis with Stata and R
knowledge of the trainer, tailor based, all topics covered
