Elevate your skills! Learn how to learn R programming for data science and become a proficient Data Scientist with our online course. Read more.

Mohammed Barakat holds a B.Sc. degree in Industrial Engineering and has been working in diverse industries since early 2000s. 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. As a programmer and data analyst, he started his journey with Microsoft Office VBA (Visual Basic) programming to cut down on labor costs and i

**Buy this course for $199 $10 and keep lifetime access.**

**Access all courses in our library for only $9/month with All Access Pass**

### About This Course

**Who this course is for:**

- Beginners who are new to Data Science and want a solid foundation in the basics, with practical application of R programming for data science.
- Aspiring Data Scientists seeking to learn R programming for data analysis and enhance their analytical skillset in data-driven decision-making.
- Professionals looking to advance their careers by mastering R programming and its applications in data science.
- Students and Researchers interested in using R programming for data analysis and statistical manipulation in research projects or academic studies.
- Individuals who have a passion for learning programming and statistical methods, and are eager to apply these skills in real-world data scenarios.

**What you’ll learn:Â **

- Understand key Data Science and Big Data concepts to build a strong analytical foundation.
- Recognize the importance and application of Data Science across various industries.
- Learn the complete Data Science process, from data collection to analysis and presentation.
- Identify the essential tools used by Data Scientists in their daily tasks.
- Master the steps involved in planning and executing a Data Science project.
- Get hands-on experience navigating the RStudio environment and working with its key components.
- Install R and RStudio efficiently on your own machine, ensuring a seamless learning experience.
- Perform arithmetic calculations in R to handle data accurately.
- Distinguish between different data types in R, which is crucial for data structuring and manipulation.
- Solve various data challenges using vectors, matrices, factors, data frames, and lists in R.
- Develop solutions using operators, conditional statements, and loops for controlled data processing.
- Create custom solutions with base R functions and user-defined functions.
- Apply mathematical functions, R packages, and the Apply function family to effectively analyze data.
- Manipulate data using regular expressions and date/time functions for advanced processing.
- Import, clean, and integrate external data for analysis using R programming for data science.
- Visualize data effectively using Râ€™s plotting functions, ensuring clear and impactful presentations.
- Evaluate and manipulate datasets efficiently using the powerful dplyr package.

**Requirements:Â **

- No prior programming or statistical knowledge is required for this course.
- A strong passion for learning programming and statistics is essential.

This course offers a comprehensive introduction to Data Science, focusing on how R programming for data science plays a vital role in helping aspiring Data Scientists excel in their careers. Youâ€™ll start by learning the basics of Data Science and Big Data, and then move on to a thorough overview of R and RStudioâ€”two essential tools in the field. By the end of the course, youâ€™ll have installed R and RStudio on your own machine, and through hands-on exercises, you’ll gain practical experience in using R for data analysis.

You will also explore the significance of R programming for data analysis by covering fundamental topics such as data types, variable assignment, arithmetic operations, vectors, matrices, factors, data frames, and lists. Advanced topics like operators, conditional statements, loops, functions, and packages will equip you with the skills to tackle more complex data problems. Additionally, the course delves into regular expressions, data cleaning techniques, data visualization, and data manipulation using the robust dplyr package.

As the amount of global data continues to expand, the need for professionals who can derive meaningful insights from this data has never been greater. Learning R programming empowers you to handle real-world data analysis projects and enables fact-based, data-driven decision-making.

**Ready to ignite your passion for learning? Join ****my courses**** and discover your full potential.**

**Our Promise to You**

By the end of this course, you will have learned how to effectively use R programming for data science, including data manipulation, statistical analysis, and visualization techniques, empowering you to tackle real-world data challenges confidently.

**10 Day Money Back Guarantee.** If you are unsatisfied for any reason, simply contact us and weâ€™ll give you a full refund. No questions asked.

**Get started today!**

### Course Curriculum

Section 1 - Introduction | |||

Introduction | 00:00:00 | ||

Section 2 - Data Science Overview | |||

Introduction To Data Science | 00:00:00 | ||

Data Science: Career Of The Future | 00:00:00 | ||

What Is Data Science? | 00:00:00 | ||

Data Science As A Process | 00:00:00 | ||

Data Science Toolbox | 00:00:00 | ||

Data Science Process Explained | 00:00:00 | ||

Formative Questions – Data Science | Unlimited | ||

Section 3 - R And RStudio | |||

Engine And Coding Environment | 00:00:00 | ||

Installing R And RStudio | 00:00:00 | ||

RStudio: A Quick Tour | 00:00:00 | ||

Formative Questions â€“ R And RStudio | Unlimited | ||

Section 4 - Introduction To Basics | |||

Arithmetic With R | 00:00:00 | ||

Variable Assignment | 00:00:00 | ||

Basic Data Types In R | 00:00:00 | ||

Formative Questions – Introduction To Basics | Unlimited | ||

Section 5 - Vectors | |||

Creating A Vector | 00:00:00 | ||

Naming A Vector | 00:00:00 | ||

Arithmetic Calculations On Vectors | 00:00:00 | ||

Vector Selection | 00:00:00 | ||

Selection By Comparison | 00:00:00 | ||

Formative Questions – Vectors | Unlimited | ||

Section 6 - Matrices | |||

What’s A Matrix? | 00:00:00 | ||

Analyzing Matrices | 00:00:00 | ||

Naming A Matrix | 00:00:00 | ||

Adding Columns And Rows To A Matrix | 00:00:00 | ||

Selection Of Matrix Elements | 00:00:00 | ||

Arithmetic With Matrices | 00:00:00 | ||

Formative Questions – Matrices | Unlimited | ||

Section 7 - Factors | |||

What’s A Factor? | 00:00:00 | ||

Categorical Variables And Factor Levels | 00:00:00 | ||

Summarizing A Factor | 00:00:00 | ||

Ordered Factors | 00:00:00 | ||

Formative Questions – Factors | Unlimited | ||

Section 8 - Data Frames | |||

What’s A Data Frame? | 00:00:00 | ||

Creating A Data Frame | 00:00:00 | ||

Selection Of Data Frame Elements | 00:00:00 | ||

Conditional Selection | 00:00:00 | ||

Sorting A Data Frame | 00:00:00 | ||

Formative Questions – Data Frames | Unlimited | ||

Section 9 - Lists | |||

Why Would You Need Lists? | 00:00:00 | ||

Creating A List | 00:00:00 | ||

Selecting Elements From A List | 00:00:00 | ||

Adding More Data To The List | 00:00:00 | ||

Formative Questions – Lists | Unlimited | ||

Section 10 - Relational Operators | |||

Equality | 00:00:00 | ||

Greater And Less Than | 00:00:00 | ||

Compare Vectors | 00:00:00 | ||

Compare Matrices | 00:00:00 | ||

Formative Questions – Relational Operators | Unlimited | ||

Section 11 - Logical Operators | |||

AND, OR, NOT Operators | 00:00:00 | ||

Logical Operators With Vectors And Matrices | 00:00:00 | ||

Reverse The Result: (!) | 00:00:00 | ||

Relational And Logical Operators Together | 00:00:00 | ||

Formative Questions – Logical Operators | Unlimited | ||

Section 12 - Conditional Statements | |||

The IF Statement | 00:00:00 | ||

IFâ€¦ELSE | 00:00:00 | ||

The ELSEIF Statement | 00:00:00 | ||

Formative Questions – Conditional Statements | Unlimited | ||

Section 13 - Loops | |||

Write A While loop | 00:00:00 | ||

Looping With More Conditions | 00:00:00 | ||

Break: Stop The While Loop | 00:00:00 | ||

Whatâ€™s A For Loop? | 00:00:00 | ||

Loop Over A Vector | 00:00:00 | ||

Loop Over A List | 00:00:00 | ||

Loop Over A Matrix | 00:00:00 | ||

For Loop With Conditionals | 00:00:00 | ||

Using Next And Break With For loop | 00:00:00 | ||

Formative Questions – Loops | Unlimited | ||

Section 14 - Functions | |||

What Is A Function? | 00:00:00 | ||

Arguments Matching | 00:00:00 | ||

Required And Optional Arguments | 00:00:00 | ||

Nested Functions | 00:00:00 | ||

Writing Own Functions | 00:00:00 | ||

Functions With No Arguments | 00:00:00 | ||

Defining Default Arguments In Functions | 00:00:00 | ||

Function Scoping | 00:00:00 | ||

Control Flow In Functions | 00:00:00 | ||

Formative Questions – Functions | Unlimited | ||

Section 15 - R Packages | |||

Installing R Packages | 00:00:00 | ||

Loading R Packages | 00:00:00 | ||

Different Ways To Load A Package | 00:00:00 | ||

Formative Questions – R Packages | Unlimited | ||

Section 16 - The apply Family - lapply | |||

What Is lapply And When It Is Used? | 00:00:00 | ||

Use lapply With User-Defined Functions | 00:00:00 | ||

lapply And Anonymous Functions | 00:00:00 | ||

Use lapply With Additional Arguments | 00:00:00 | ||

Formative Questions – The apply Family – lapply | Unlimited | ||

Section 17 - The apply Family â€“ sapply And vapply | |||

What Is sapply? | 00:00:00 | ||

How To Use sapply | 00:00:00 | ||

sapply With Your Own Function | 00:00:00 | ||

sapply With A Function Returning A Vector | 00:00:00 | ||

When Can’t sapply Simplify? | 00:00:00 | ||

What Is vapply And Why Is It Used? | 00:00:00 | ||

Formative Questions – The apply Family â€“ sapply And vapply | Unlimited | ||

Section 18 - Useful Functions | |||

Mathematical Functions | 00:00:00 | ||

Data Utilities | 00:00:00 | ||

Formative Questions – Useful Functions | Unlimited | ||

Section 19 - Regular Expressions | |||

grepl And grep | 00:00:00 | ||

Metacharacters | 00:00:00 | ||

sub And gsub | 00:00:00 | ||

More Metacharacters | 00:00:00 | ||

Formative Questions – Regular Expressions | Unlimited | ||

Section 20 - Dates And Times | |||

Today And Now | 00:00:00 | ||

Create And Format Dates | 00:00:00 | ||

Create And Format Times | 00:00:00 | ||

Calculations With Dates | 00:00:00 | ||

Calculations With Times | 00:00:00 | ||

Formative Questions – Dates And Times | Unlimited | ||

Section 21 - Getting And Cleaning Data | |||

Get And Set Current Directory | 00:00:00 | ||

Get Data From The Web | 00:00:00 | ||

Loading Flat Files | 00:00:00 | ||

Loading Excel Files | 00:00:00 | ||

Formative Questions – Getting And Cleaning Data | Unlimited | ||

Section 22 - Data Manipulation With dplyr | |||

Introduction To dplyr Package | 00:00:00 | ||

Using The Pipe Operator (%>%) | 00:00:00 | ||

Columns Component: select() | 00:00:00 | ||

Columns Component: rename() and rename_with() | 00:00:00 | ||

Columns Component: mutate() | 00:00:00 | ||

Columns Component: relocate() | 00:00:00 | ||

Rows Component: filter() | 00:00:00 | ||

Rows Component: slice() | 00:00:00 | ||

Rows Component: arrange() | 00:00:00 | ||

Rows Component: rowwise() | 00:00:00 | ||

Grouping Of Rows: summarise() | 00:00:00 | ||

Grouping Of Rows: across() | 00:00:00 | ||

COVID-19 Analysis Task | 00:00:00 | ||

Formative Questions – Data Manipulation With dplyr | Unlimited | ||

Section 23 - Plotting Data In R | |||

Base Plotting System | 00:00:00 | ||

Base Plots: Histograms | 00:00:00 | ||

Base Plots: Scatterplots | 00:00:00 | ||

Base Plots: Regression Line | 00:00:00 | ||

Base Plots: Boxplot | 00:00:00 | ||

Formative Questions – Plotting Data In R | Unlimited |

### About This Course

**Who this course is for:**

- Beginners who are new to Data Science and want a solid foundation in the basics, with practical application of R programming for data science.
- Aspiring Data Scientists seeking to learn R programming for data analysis and enhance their analytical skillset in data-driven decision-making.
- Professionals looking to advance their careers by mastering R programming and its applications in data science.
- Students and Researchers interested in using R programming for data analysis and statistical manipulation in research projects or academic studies.
- Individuals who have a passion for learning programming and statistical methods, and are eager to apply these skills in real-world data scenarios.

**What you’ll learn:Â **

- Understand key Data Science and Big Data concepts to build a strong analytical foundation.
- Recognize the importance and application of Data Science across various industries.
- Learn the complete Data Science process, from data collection to analysis and presentation.
- Identify the essential tools used by Data Scientists in their daily tasks.
- Master the steps involved in planning and executing a Data Science project.
- Get hands-on experience navigating the RStudio environment and working with its key components.
- Install R and RStudio efficiently on your own machine, ensuring a seamless learning experience.
- Perform arithmetic calculations in R to handle data accurately.
- Distinguish between different data types in R, which is crucial for data structuring and manipulation.
- Solve various data challenges using vectors, matrices, factors, data frames, and lists in R.
- Develop solutions using operators, conditional statements, and loops for controlled data processing.
- Create custom solutions with base R functions and user-defined functions.
- Apply mathematical functions, R packages, and the Apply function family to effectively analyze data.
- Manipulate data using regular expressions and date/time functions for advanced processing.
- Import, clean, and integrate external data for analysis using R programming for data science.
- Visualize data effectively using Râ€™s plotting functions, ensuring clear and impactful presentations.
- Evaluate and manipulate datasets efficiently using the powerful dplyr package.

**Requirements:Â **

- No prior programming or statistical knowledge is required for this course.
- A strong passion for learning programming and statistics is essential.

This course offers a comprehensive introduction to Data Science, focusing on how R programming for data science plays a vital role in helping aspiring Data Scientists excel in their careers. Youâ€™ll start by learning the basics of Data Science and Big Data, and then move on to a thorough overview of R and RStudioâ€”two essential tools in the field. By the end of the course, youâ€™ll have installed R and RStudio on your own machine, and through hands-on exercises, you’ll gain practical experience in using R for data analysis.

You will also explore the significance of R programming for data analysis by covering fundamental topics such as data types, variable assignment, arithmetic operations, vectors, matrices, factors, data frames, and lists. Advanced topics like operators, conditional statements, loops, functions, and packages will equip you with the skills to tackle more complex data problems. Additionally, the course delves into regular expressions, data cleaning techniques, data visualization, and data manipulation using the robust dplyr package.

As the amount of global data continues to expand, the need for professionals who can derive meaningful insights from this data has never been greater. Learning R programming empowers you to handle real-world data analysis projects and enables fact-based, data-driven decision-making.

**Ready to ignite your passion for learning? Join ****my courses**** and discover your full potential.**

**Our Promise to You**

By the end of this course, you will have learned how to effectively use R programming for data science, including data manipulation, statistical analysis, and visualization techniques, empowering you to tackle real-world data challenges confidently.

**10 Day Money Back Guarantee.** If you are unsatisfied for any reason, simply contact us and weâ€™ll give you a full refund. No questions asked.

**Get started today!**

### Course Curriculum

Section 1 - Introduction | |||

Introduction | 00:00:00 | ||

Section 2 - Data Science Overview | |||

Introduction To Data Science | 00:00:00 | ||

Data Science: Career Of The Future | 00:00:00 | ||

What Is Data Science? | 00:00:00 | ||

Data Science As A Process | 00:00:00 | ||

Data Science Toolbox | 00:00:00 | ||

Data Science Process Explained | 00:00:00 | ||

Formative Questions – Data Science | Unlimited | ||

Section 3 - R And RStudio | |||

Engine And Coding Environment | 00:00:00 | ||

Installing R And RStudio | 00:00:00 | ||

RStudio: A Quick Tour | 00:00:00 | ||

Formative Questions â€“ R And RStudio | Unlimited | ||

Section 4 - Introduction To Basics | |||

Arithmetic With R | 00:00:00 | ||

Variable Assignment | 00:00:00 | ||

Basic Data Types In R | 00:00:00 | ||

Formative Questions – Introduction To Basics | Unlimited | ||

Section 5 - Vectors | |||

Creating A Vector | 00:00:00 | ||

Naming A Vector | 00:00:00 | ||

Arithmetic Calculations On Vectors | 00:00:00 | ||

Vector Selection | 00:00:00 | ||

Selection By Comparison | 00:00:00 | ||

Formative Questions – Vectors | Unlimited | ||

Section 6 - Matrices | |||

What’s A Matrix? | 00:00:00 | ||

Analyzing Matrices | 00:00:00 | ||

Naming A Matrix | 00:00:00 | ||

Adding Columns And Rows To A Matrix | 00:00:00 | ||

Selection Of Matrix Elements | 00:00:00 | ||

Arithmetic With Matrices | 00:00:00 | ||

Formative Questions – Matrices | Unlimited | ||

Section 7 - Factors | |||

What’s A Factor? | 00:00:00 | ||

Categorical Variables And Factor Levels | 00:00:00 | ||

Summarizing A Factor | 00:00:00 | ||

Ordered Factors | 00:00:00 | ||

Formative Questions – Factors | Unlimited | ||

Section 8 - Data Frames | |||

What’s A Data Frame? | 00:00:00 | ||

Creating A Data Frame | 00:00:00 | ||

Selection Of Data Frame Elements | 00:00:00 | ||

Conditional Selection | 00:00:00 | ||

Sorting A Data Frame | 00:00:00 | ||

Formative Questions – Data Frames | Unlimited | ||

Section 9 - Lists | |||

Why Would You Need Lists? | 00:00:00 | ||

Creating A List | 00:00:00 | ||

Selecting Elements From A List | 00:00:00 | ||

Adding More Data To The List | 00:00:00 | ||

Formative Questions – Lists | Unlimited | ||

Section 10 - Relational Operators | |||

Equality | 00:00:00 | ||

Greater And Less Than | 00:00:00 | ||

Compare Vectors | 00:00:00 | ||

Compare Matrices | 00:00:00 | ||

Formative Questions – Relational Operators | Unlimited | ||

Section 11 - Logical Operators | |||

AND, OR, NOT Operators | 00:00:00 | ||

Logical Operators With Vectors And Matrices | 00:00:00 | ||

Reverse The Result: (!) | 00:00:00 | ||

Relational And Logical Operators Together | 00:00:00 | ||

Formative Questions – Logical Operators | Unlimited | ||

Section 12 - Conditional Statements | |||

The IF Statement | 00:00:00 | ||

IFâ€¦ELSE | 00:00:00 | ||

The ELSEIF Statement | 00:00:00 | ||

Formative Questions – Conditional Statements | Unlimited | ||

Section 13 - Loops | |||

Write A While loop | 00:00:00 | ||

Looping With More Conditions | 00:00:00 | ||

Break: Stop The While Loop | 00:00:00 | ||

Whatâ€™s A For Loop? | 00:00:00 | ||

Loop Over A Vector | 00:00:00 | ||

Loop Over A List | 00:00:00 | ||

Loop Over A Matrix | 00:00:00 | ||

For Loop With Conditionals | 00:00:00 | ||

Using Next And Break With For loop | 00:00:00 | ||

Formative Questions – Loops | Unlimited | ||

Section 14 - Functions | |||

What Is A Function? | 00:00:00 | ||

Arguments Matching | 00:00:00 | ||

Required And Optional Arguments | 00:00:00 | ||

Nested Functions | 00:00:00 | ||

Writing Own Functions | 00:00:00 | ||

Functions With No Arguments | 00:00:00 | ||

Defining Default Arguments In Functions | 00:00:00 | ||

Function Scoping | 00:00:00 | ||

Control Flow In Functions | 00:00:00 | ||

Formative Questions – Functions | Unlimited | ||

Section 15 - R Packages | |||

Installing R Packages | 00:00:00 | ||

Loading R Packages | 00:00:00 | ||

Different Ways To Load A Package | 00:00:00 | ||

Formative Questions – R Packages | Unlimited | ||

Section 16 - The apply Family - lapply | |||

What Is lapply And When It Is Used? | 00:00:00 | ||

Use lapply With User-Defined Functions | 00:00:00 | ||

lapply And Anonymous Functions | 00:00:00 | ||

Use lapply With Additional Arguments | 00:00:00 | ||

Formative Questions – The apply Family – lapply | Unlimited | ||

Section 17 - The apply Family â€“ sapply And vapply | |||

What Is sapply? | 00:00:00 | ||

How To Use sapply | 00:00:00 | ||

sapply With Your Own Function | 00:00:00 | ||

sapply With A Function Returning A Vector | 00:00:00 | ||

When Can’t sapply Simplify? | 00:00:00 | ||

What Is vapply And Why Is It Used? | 00:00:00 | ||

Formative Questions – The apply Family â€“ sapply And vapply | Unlimited | ||

Section 18 - Useful Functions | |||

Mathematical Functions | 00:00:00 | ||

Data Utilities | 00:00:00 | ||

Formative Questions – Useful Functions | Unlimited | ||

Section 19 - Regular Expressions | |||

grepl And grep | 00:00:00 | ||

Metacharacters | 00:00:00 | ||

sub And gsub | 00:00:00 | ||

More Metacharacters | 00:00:00 | ||

Formative Questions – Regular Expressions | Unlimited | ||

Section 20 - Dates And Times | |||

Today And Now | 00:00:00 | ||

Create And Format Dates | 00:00:00 | ||

Create And Format Times | 00:00:00 | ||

Calculations With Dates | 00:00:00 | ||

Calculations With Times | 00:00:00 | ||

Formative Questions – Dates And Times | Unlimited | ||

Section 21 - Getting And Cleaning Data | |||

Get And Set Current Directory | 00:00:00 | ||

Get Data From The Web | 00:00:00 | ||

Loading Flat Files | 00:00:00 | ||

Loading Excel Files | 00:00:00 | ||

Formative Questions – Getting And Cleaning Data | Unlimited | ||

Section 22 - Data Manipulation With dplyr | |||

Introduction To dplyr Package | 00:00:00 | ||

Using The Pipe Operator (%>%) | 00:00:00 | ||

Columns Component: select() | 00:00:00 | ||

Columns Component: rename() and rename_with() | 00:00:00 | ||

Columns Component: mutate() | 00:00:00 | ||

Columns Component: relocate() | 00:00:00 | ||

Rows Component: filter() | 00:00:00 | ||

Rows Component: slice() | 00:00:00 | ||

Rows Component: arrange() | 00:00:00 | ||

Rows Component: rowwise() | 00:00:00 | ||

Grouping Of Rows: summarise() | 00:00:00 | ||

Grouping Of Rows: across() | 00:00:00 | ||

COVID-19 Analysis Task | 00:00:00 | ||

Formative Questions – Data Manipulation With dplyr | Unlimited | ||

Section 23 - Plotting Data In R | |||

Base Plotting System | 00:00:00 | ||

Base Plots: Histograms | 00:00:00 | ||

Base Plots: Scatterplots | 00:00:00 | ||

Base Plots: Regression Line | 00:00:00 | ||

Base Plots: Boxplot | 00:00:00 | ||

Formative Questions – Plotting Data In R | Unlimited |