Mastering R Programming for Data Analysis

R Programming for Data Science

This course teaches you the basics of R programming and how to use it as a preliminary step to become a Data Scientist

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Last Updated: 2024-12-20 07:12:46 English Course Type: Self Learning

Describe Data Science and Big Data
Recognize the importance of Data Science
Explain the Data Science process
Identify main tools used in Data Science
Explain the steps of a Data Science project
Recognize the main environment and files of RStudio
Complete installing R and R Studio on own machine
Solve arithmetic calculations in R
Distinguish between different data types in R
Solve data problems using vectors, matrices, factors, data frames, and lists in R
Formulate controlled-flow data problems using Operators, Conditional Statements, and Loops
Recognize base R functions and user-defined functions in R
Analyze data using base mathematical functions, R Packages, and Apply function family
Modify data using Regular Expressions and Dates & Times functions
Plot data in R
Evaluate datasets in R using dplyr package

This course provides an overview of Data Science and how R language with its methods and functions constitutes a primary tool to become a Data Scientist. The course presents an introduction to Data Science and Big Data. Then, it gives an overview of R and RStudio software and how to set them up. Lastly, it explains in detail, with the aid of numerous exercises, how to use various R methods and functions to analyze data.

The course demonstrates the importance and advantages of R language as a start, then it presents topics on R data types, variable assignment, arithmetic operations, vectors, matrices, factors, data frames and lists. Besides, it includes topics on operators, conditionals, loops, functions, and packages. It also covers regular expressions, getting and cleaning data, plotting, and data manipulation using the dplyr package.

The ever-increasing size of data globally coupled with the prominent need to extract insightful information out of it necessitate learning analytical programming languages such as R. This course paves the road for beginners to start using R in real life analysis tasks and research projects to enable a fact-based decision-making process.

No prior knowledge is mandatory to this course.
Passion towards learning programming and statistics is essential

Professionals and academics who aspire to use R Language as part of their Data Analysis and Data Science tasks.

Section 1 : Data Science Overview 6 Lectures 00:17:17
Lecture 1 :
Introduction to Data Science Preview
Lecture 2 :
Data Science: Career of the Future
Lecture 3 :
What is Data Science?
Lecture 4 :
Data Science as a Process
Lecture 5 :
Data Science Toolbox
Lecture 6 :
Data Science Process Explained
Section 2 : R and RStudio 3 Lectures 00:10:33
Lecture 1 :
Engine and Coding Environment
Lecture 2 :
Installing R and RStudio
Lecture 3 :
RStudio: A quick tour
Section 3 : Introduction to Basics 3 Lectures 00:10:06
Lecture 1 :
Arithmetic with R
Lecture 2 :
Variable Assignment
Lecture 3 :
Basic Data Types in R
Section 4 : Vectors Data Structure 5 Lectures 00:25:46
Lecture 1 :
Creating a Vector
Lecture 2 :
Naming a Vector
Lecture 3 :
Arithmetic Calculations on Vectors
Lecture 4 :
Vector Selection
Lecture 5 :
Selection by Comparison
Section 5 : Matrices Data Structure 6 Lectures 00:25:43
Lecture 1 :
What is a Matrix
Lecture 2 :
Analyzing Matrices
Lecture 3 :
Naming a Matrix
Lecture 4 :
Adding columns and rows to a matrix
Lecture 5 :
Selection of matrix elements
Lecture 6 :
Arithmetic with matrices
Section 6 : Factors Data Structure 4 Lectures 00:13:04
Lecture 1 :
What is a Factor
Lecture 2 :
Categorical Variables and Factor Levels
Lecture 3 :
Summarizing a Factor
Lecture 4 :
Ordered Factors
Section 7 : Data Frames Structure 5 Lectures 00:15:45
Lecture 1 :
What is a Data Frame
Lecture 2 :
Creating a Data Frame
Lecture 3 :
Selection of Data Frame elements
Lecture 4 :
Conditional selection
Lecture 5 :
Sorting a Data Frame
Section 8 : Lists Data Structure 4 Lectures 00:11:52
Lecture 1 :
Why would you need lists?
Lecture 2 :
Creating a List
Lecture 3 :
Selecting elements from a list
Lecture 4 :
Adding more data to the list
Section 9 : Relational Operators 4 Lectures 00:10:21
Lecture 1 :
Equality
Lecture 2 :
Greater and Less Than
Lecture 3 :
Compare Vectors
Lecture 4 :
Compare Matrices
Section 10 : Logical Operators 4 Lectures 00:14:58
Lecture 1 :
AND, OR, NOT Operators
Lecture 2 :
Logical operators with vectors and matrices
Lecture 3 :
Reverse the result: (!)
Lecture 4 :
Relational and Logical Operators together
Section 11 : Conditional Statements 3 Lectures 00:11:20
Lecture 1 :
The IF statement
Lecture 2 :
IF…ELSE
Lecture 3 :
The ELSEIF statement
Section 12 : Loop Structures 9 Lectures 00:26:48
Lecture 1 :
Write a While loop
Lecture 2 :
Looping with more conditions
Lecture 3 :
Break: stop the While Loop
Lecture 4 :
What’s a For loop?
Lecture 5 :
Loop over a vector
Lecture 6 :
Loop over a list
Lecture 7 :
Loop over a matrix
Lecture 8 :
For loop with conditionals
Lecture 9 :
Using Next and Break with For loop
Section 13 : Functions in R 9 Lectures 00:20:03
Lecture 1 :
What is a Function?
Lecture 2 :
Arguments matching
Lecture 3 :
Required and Optional Arguments
Lecture 4 :
Nested functions
Lecture 5 :
Writing own functions
Lecture 6 :
Functions with no arguments
Lecture 7 :
Defining default arguments in functions
Lecture 8 :
Function scoping
Lecture 9 :
Control flow in functions
Section 14 : Packages in R 3 Lectures 00:07:43
Lecture 1 :
Installing R Packages
Lecture 2 :
Loading R Packages
Lecture 3 :
Different ways to load a package
Section 15 : The apply family – lapply 4 Lectures 00:11:45
Lecture 1 :
What is lapply and when is used?
Lecture 2 :
Use lapply with user-defined functions
Lecture 3 :
lapply and anonymous functions
Lecture 4 :
Use lapply with additional arguments
Section 16 : The apply family – sapply & vapply 6 Lectures 00:14:02
Lecture 1 :
What is sapply
Lecture 2 :
How to use sapply
Lecture 3 :
sapply with your own function
Lecture 4 :
sapply with a function returning a vector
Lecture 5 :
When can not sapply simplify
Lecture 6 :
What is vapply and why is it used
Section 17 : Useful Functions in R 2 Lectures 00:12:26
Lecture 1 :
Mathematical functions
Lecture 2 :
Data Utilities
Section 18 : Regular Expressions 4 Lectures 00:15:16
Lecture 1 :
grepl & grep
Lecture 2 :
Metacharacters
Lecture 3 :
sub & gsub
Lecture 4 :
More metacharacters
Section 19 : Dates and Times 5 Lectures 00:20:44
Lecture 1 :
Today and Now
Lecture 2 :
Create and format dates
Lecture 3 :
Create and format times
Lecture 4 :
Calculations with Dates
Lecture 5 :
Calculations with Times
Section 20 : Getting and Cleaning Data 4 Lectures 00:16:12
Lecture 1 :
Get and set current directory
Lecture 2 :
Get data from the web
Lecture 3 :
Loading flat files
Lecture 4 :
Loading Excel files
Section 21 : Data Manipulation with dplyr 12 Lectures 00:32:04
Lecture 1 :
Introduction to dplyr package
Lecture 2 :
Using the pipe operator (%>%)
Lecture 3 :
Columns component: select()
Lecture 4 :
Columns component: rename() and rename_with()
Lecture 5 :
Columns component: mutate()
Lecture 6 :
Columns component: relocate()
Lecture 7 :
Rows component: filter()
Lecture 8 :
Rows component: slice()
Lecture 9 :
Rows component: arrange()
Lecture 10 :
Rows component: rowwise()
Lecture 11 :
Grouping of rows: summarise()
Lecture 12 :
Grouping of rows: across()
Section 22 : Plotting Data in R 5 Lectures 00:15:57
Lecture 1 :
Base plotting system
Lecture 2 :
Base plots: Histograms
Lecture 3 :
Base plots: Scatterplots
Lecture 4 :
Base plots: Regression Line
Lecture 5 :
Base plots: Boxplot
Section 23 : COVID19 Analysis: Case Study 1 Lectures 00:07:36
Lecture 1 :
COVID19 Analysis: Case Study

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Mohammed Barakat,

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Mohammed Barakat holds a B.Sc. degree in Industrial Engineering and has been working in diverse industries for more than two decades. His area of focus is Process Management and Data Analysis. In his capacity as a process improvement expert, he managed and implemented a multitude of improvement projects using Six Sigma, Lean, Theory of Constraints, and Kaizen. And as a programmer and data scientist, he started his journey with Microsoft Office VBA (Visual Basic) programming to cut down on labor cost and improve processes efficiency. Then, he joined the Data Science field and has developed several R scripts and reproducible research handling statistical and machine learning algorithms. Barakat is a Jordan Consultant Engineer (JCE), he holds several certifications in Project Management (PMP and PMI-RMP) and Six Sigma (ASQ Six Sigma Black Belt and Green Belt). He is also a Microsoft Certified Trainer (MCT) and a Microsoft Certified Technology Specialist (MCTS). He was named a winner of the ASQ MEA Quality Professionals Award for the year 2018.