Learning R Programming for Data Science

A Udemy course

Course Description

This course puts the participant in the right path to become a competent Data Scientist by teaching him/her the basics of R Language as one prominent tool in Data Science.

The course starts by introducing Data Science and the steps taken to complete a Data Science project. Then it continues with lectures on various methods and functions of R enabling the participant to start his/her journey towards becoming a Data Scientist with R.

In this course participants will learn how to install and configure R and RStudio. Besides, participants will be able to create various data structures such as Vectors, Matrices, Factors, Data Frames, and Lists. They will solve simple data problems using Operators, Conditional Statements, Loops, base and user-defined functions. Participants will understand and use different data gathering and manipulation methods such as getting and cleaning external files, the Apply family, Regular Expressions, Dates & Times, Base Plotting, and the dplyr package.

Course Outline

• Data Science Overview
  • Introduction
  • Data Science: Career of the Future
  • What is Data Science?
  • The Data Science process
  • The Data Scientist toolbox
• About R
  • About R and RStudio
• Introduction to R
  • Introduction to Basics
  • Vectors
  • Matrices
  • Factors
  • Data Frames
  • Lists
• Intermediate R
  • Conditionals and Control Flow
    • Relational Operators
    • Logical Operators
    • Conditional Statements
  • Loops
    • While loop
    • For Loop
  • Functions
    • Introduction to Functions
    • Writing Functions
    • R Packages
  • The apply family
    • lapply
    • sapply
    • vapply
  • Useful Functions
    • Mathematical functions
    • Data utilities
  • Regular Expressions
    • What is a Regular Expression?
    • grepl & grep
    • Metacharacters
    • sub & gsub
  • Dates and Times
    • Today and Now
    • Create and format dates
    • Create and format times
    • Calculations with Dates
    • Calculations with Times
  • Getting and Cleaning Data
    • Get and set current directory
    • Create a new directory
    • Get data from the web
    • Loading flat files
    • Loading Excel files
  • Plotting Data in R
    • Base plotting system
    • Base plots: Histograms
    • Base plots: Scatterplot
    • Base plots: Regression Line
    • Base plots: Boxplot
  • Data Manipulation with dplyr
    • Introduction to dplyr package
    • Using the pipe operator (%>%)
    • Columns component: select()
    • Columns component: rename() and rename_with()
    • Columns component: mutate()
    • Columns component: relocate()
    • Rows component: filter()
    • Rows component: slice()
    • Rows component: arrange()
    • Rows component: rowwise()
    • Grouping of rows: summarise()
    • Grouping of rows: across()
    • COVID-19 Analysis Task

Course Objectives

By the end of this course, the participant should be able to:

1. Describe Data Science and Big Data
2. Identify the importance of Data Science
3. Explain the Data Science process
4. Describe main tools used in Data Science
5. Explain steps of a Data Science project
6. Recognize the main environment and files of RStudio
7. Perform arithmetic calculations in R.
8. Solve simple data problems using vectors, matrices, factors, data frames, and lists in R.
9. Use Operators and Conditional Statements to perform comparison and controlled-flow data problems in R.
10. Solve data problems using Loops in R.
11. Use base R functions and create user-defined functions in R.
12. Install and use different R Packages
13. Get and clean data in R
14. Plot data in R
15. Manipulate datasets with dplyr package
16. Analyze data using helpful functions in R such as base mathematical functions, Apply family, Regular Expressions, and Dates & Times.

Course Audience

This course is targeted to professionals and academics who aspire to use R Language as part of their Data Analysis and Data Science tasks.

Course Pre-requisites

No prior knowledge is mandatory to this course. However, passion towards learning programming and statistics is essential.

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