Description

Duration

5 Days

30 CPD hours

This course is intended for

This course is designed for students who want to learn the R programming language, particularly students who want to leverage R for data analysis and data science tasks in their organization. The course is also designed for students with an interest in applying statistics to real-world problems.
A typical student in this course should have several years of experience with computing technology, along with a proficiency in at least one other programming language.

Overview

In this course, you will use R to perform common data science tasks.You will:
Set up an R development environment and execute simple code.
Perform operations on atomic data types in R, including characters, numbers, and logicals.
Perform operations on data structures in R, including vectors, lists, and data frames.
Write conditional statements and loops.
Structure code for reuse with functions and packages.
Manage data by loading and saving datasets, manipulating data frames, and more.
Analyze data through exploratory analysis, statistical analysis, and more.
Create and format data visualizations using base R and ggplot2.
Create simple statistical models from data.

In our data-driven world, organizations need the right tools to extract valuable insights from that data. The R programming language is one of the tools at the forefront of data science. Its robust set of packages and statistical functions makes it a powerful choice for analyzing data, manipulating data, performing statistical tests on data, and creating predictive models from data. Likewise, R is notable for its strong data visualization tools, enabling you to create high-quality graphs and plots that are incredibly customizable.

This course will teach you the fundamentals of programming in R to get you started. It will also teach you how to use R to perform common data science tasks and achieve data-driven results for the business.

Lesson 1: Setting Up R and Executing Simple Code

Topic A: Set Up the R Development Environment

Topic B: Write R Statements

Lesson 2: Processing Atomic Data Types

Topic A: Process Characters

Topic B: Process Numbers

Topic C: Process Logicals

Lesson 3: Processing Data Structures

Topic A: Process Vectors

Topic B: Process Factors

Topic C: Process Data Frames

Topic D: Subset Data Structures

Lesson 4: Writing Conditional Statements and Loops

Topic A: Write Conditional Statements

Topic B: Write Loops

Lesson 5: Structuring Code for Reuse

Topic A: Define and Call Functions

Topic B: Apply Loop Functions

Topic C: Manage R Packages

Lesson 6: Managing Data in R

Topic A: Load Data

Topic B: Save Data

Topic C: Manipulate Data Frames Using Base R

Topic D: Manipulate Data Frames Using dplyr

Topic E: Handle Dates and Times

Lesson 7: Analyzing Data in R

Topic A: Examine Data

Topic B: Explore the Underlying Distribution of Data

Topic C: Identify Missing Values

Lesson 8: Visualizing Data in R

Topic A: Plot Data Using Base R Functions

Topic B: Plot Data Using ggplot2

Topic C: Format Plots in ggplot2

Topic D: Create Combination Plots

Lesson 9: Modeling Data in R

Topic A: Create Statistical Models in R

Topic B: Create Machine Learning Models in R

Course Images

R Programming for Data Science (v1.0)

By Nexus Human

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