# R For Data Science with Real Exercises

**#34**in category Data Science

Learning Experience | 9.2 |
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R for Data Science course is packed with real-life analytical challenges, you will learn to solve and also you will learn to find step-by-step solutions.

## R For Data Science with Real Exercises

There are lots of R courses and lectures out there. However, R has a very steep learning curve, and students often get overwhelmed. This course is different! In this course, you will find step-by-step solutions. Before exploring the syllabus and course content, let’s why we should use R for Data Science?

## Why use R for Data Science?

#### 1. R is open-source and free

- R is free to download

#### 2. R is popular – and increasing in popularity

- IEEE publishes a list of the most popular programming languages each year. R was ranked 5th in 2016, up from 6th in 2015. This shows the increasing interest in R as a programming language and in the fields like Data Science and Machine Learning, where R is commonly used.

#### 3. R runs on all platforms

- You can find distributions of R for all popular platforms – Windows, Linux, and Mac.R code that you write on one platform can easily be ported to another without any issues.

#### 4. Learning R will increase your chances of getting a job

- According to the Data Science Salary Survey conducted by O’Reilly Media in 2014, data scientists are paid a median of $98,000 worldwide. The figure is higher in the US – around $144,000.Of course, learning how to write R programs won’t get you a job straight away. A data scientist has to juggle a lot of tools to do their work. Even if you apply for a software developer position, R programming experience can make you stand out from the crowd.

#### 5. R is being used by the biggest tech giants

- Adoption by tech giants is always a sign of a programming language’s potential. Today’s companies don’t make their decisions on a whim. Every major decision has to be backed by a concrete analysis of data.

(Information Source)

## About this Course

After every video, you will learn a new valuable concept that you can apply right away. And the best part is that you learn through live examples. This training is packed with real-life analytical challenges which you will learn to solve. Some of these you will solve together, some you will have as homework exercises.

In summary, this course has been designed for all skill levels, and even if you have no programming or statistical background, you will be successful in this course!

## Who this course is for

This course is for those:

- Who wants to learn how to program in R
- If you are tired of R courses that are too complicated
- If you want to learn R by doing
- Who like exciting challenges

## Syllabus

The course on R for data science is exciting, and, at the same time, it dives deep into Machine Learning. It is structured in the following way.

#### 1. Core Programming Principles

- You will learn Types of variables.
- Logical Variables and Operators.
- Using a while loop, for loop, IF statement including core programming principles.

#### 2. Fundamentals of R

- Will learn about vectors and usage of brackets
- The power of vectorized operations
- Functions and packages in R

#### 3. Matrices

- You will build your First Matrix
- Naming Dimensions and Colnames() and Rownames()
- Matrix Operations and Visualizing With Matplot()
- Subsetting and Visualizing Subsets

#### 4. R for Data Science: Data Frames

- Importing data into R and Exploring your data set
- Basic operations with a Data Frame and Filtering a Data Frame
- Introduction to qplot
- Building Data frames and Merging Data Frames

#### 5. R for Data Science: Advanced Visualization with GGplot2

- You will learn about Factor, Aesthetics, Plotting With Layers, Overriding Aesthetics.
- Mapping vs. Setting and Histograms and Density Charts.
- Starting Layer Tips, Statistical Transformations, Using Facets, Coordinates, Perfecting By Adding Themes and Advanced Visualization With GGPlot2

#### 6. Homework Solutions

In this section, you will go through different Homework Solution Sections such as:

- Law Of Large Numbers
- Financial Statement Analysis
- Basketball Free Throws
- World Trends
- Movie Domestic % Gross (Part 1)
- Movie Domestic % Gross (Part 2)

Along with this, you will have one bonus tutorial, including videos.

## What you will learn from this course on R for data science

- You will learn to program in R at a good level, use R Studio, the core principles of programming, how to create vectors in R, how to create variables.
- About integer, double, logical, character, and other types in R.
- How to create a while() loop and a for() loop in R and how to build and use matrices in R.
- The matrix() function, rbind() and cbind().
- how to install packages in R.
- How to customize R studio to suit your preferences.
- Understand the Law of Large Numbers and Normal distribution.
- Practice working with statistical data in R and financial data in R and sports data in R.

*Note: Your review matters*

*If you have already done this course, kindly drop your review in our reviews section. It would help others to get useful information and better insight into the course offered.*

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## Description

## R For Data Science with Real Exercises

There are lots of R courses and lectures out there. However, R has a very steep learning curve, and students often get overwhelmed. This course is different! In this course, you will find step-by-step solutions. Before exploring the syllabus and course content, let’s why we should use R for Data Science?

## Why use R for Data Science?

#### 1. R is open-source and free

- R is free to download

#### 2. R is popular – and increasing in popularity

- IEEE publishes a list of the most popular programming languages each year. R was ranked 5th in 2016, up from 6th in 2015. This shows the increasing interest in R as a programming language and in the fields like Data Science and Machine Learning, where R is commonly used.

#### 3. R runs on all platforms

- You can find distributions of R for all popular platforms – Windows, Linux, and Mac.R code that you write on one platform can easily be ported to another without any issues.

#### 4. Learning R will increase your chances of getting a job

- According to the Data Science Salary Survey conducted by O’Reilly Media in 2014, data scientists are paid a median of $98,000 worldwide. The figure is higher in the US – around $144,000.Of course, learning how to write R programs won’t get you a job straight away. A data scientist has to juggle a lot of tools to do their work. Even if you apply for a software developer position, R programming experience can make you stand out from the crowd.

#### 5. R is being used by the biggest tech giants

- Adoption by tech giants is always a sign of a programming language’s potential. Today’s companies don’t make their decisions on a whim. Every major decision has to be backed by a concrete analysis of data.

(Information Source)

## About this Course

After every video, you will learn a new valuable concept that you can apply right away. And the best part is that you learn through live examples. This training is packed with real-life analytical challenges which you will learn to solve. Some of these you will solve together, some you will have as homework exercises.

In summary, this course has been designed for all skill levels, and even if you have no programming or statistical background, you will be successful in this course!

## Who this course is for

This course is for those:

- Who wants to learn how to program in R
- If you are tired of R courses that are too complicated
- If you want to learn R by doing
- Who like exciting challenges

## Syllabus

The course on R for data science is exciting, and, at the same time, it dives deep into Machine Learning. It is structured in the following way.

#### 1. Core Programming Principles

- You will learn Types of variables.
- Logical Variables and Operators.
- Using a while loop, for loop, IF statement including core programming principles.

#### 2. Fundamentals of R

- Will learn about vectors and usage of brackets
- The power of vectorized operations
- Functions and packages in R

#### 3. Matrices

- You will build your First Matrix
- Naming Dimensions and Colnames() and Rownames()
- Matrix Operations and Visualizing With Matplot()
- Subsetting and Visualizing Subsets

#### 4. R for Data Science: Data Frames

- Importing data into R and Exploring your data set
- Basic operations with a Data Frame and Filtering a Data Frame
- Introduction to qplot
- Building Data frames and Merging Data Frames

#### 5. R for Data Science: Advanced Visualization with GGplot2

- You will learn about Factor, Aesthetics, Plotting With Layers, Overriding Aesthetics.
- Mapping vs. Setting and Histograms and Density Charts.
- Starting Layer Tips, Statistical Transformations, Using Facets, Coordinates, Perfecting By Adding Themes and Advanced Visualization With GGPlot2

#### 6. Homework Solutions

In this section, you will go through different Homework Solution Sections such as:

- Law Of Large Numbers
- Financial Statement Analysis
- Basketball Free Throws
- World Trends
- Movie Domestic % Gross (Part 1)
- Movie Domestic % Gross (Part 2)

Along with this, you will have one bonus tutorial, including videos.

## What you will learn from this course on R for data science

- You will learn to program in R at a good level, use R Studio, the core principles of programming, how to create vectors in R, how to create variables.
- About integer, double, logical, character, and other types in R.
- How to create a while() loop and a for() loop in R and how to build and use matrices in R.
- The matrix() function, rbind() and cbind().
- how to install packages in R.
- How to customize R studio to suit your preferences.
- Understand the Law of Large Numbers and Normal distribution.
- Practice working with statistical data in R and financial data in R and sports data in R.

*Note: Your review matters*

*If you have already done this course, kindly drop your review in our reviews section. It would help others to get useful information and better insight into the course offered.*

FAQ

## Specification:

- Udemy
- SuperDataScience
- Online Course
- Self-paced
- All levels
- 1-4 Weeks
- Paid Course (Paid certificate)
- English
- Data Science Data Science with 'R'

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