# Data Science with R Certification Course

**#75**in category Data Science

Learning Experience | 8.4 |
---|

The Data Science with R certification course will provide you meaningful information based on large amounts of complex data or big data using R.

## About this Course on Data Science with R certification

The Data Science with R certification covers data exploration, data visualization, predictive analytics, and descriptive analytics techniques with the R language. You will learn about R packages, how to import and export data in R, data structures in R, various statistical concepts, cluster analysis, and forecasting.

Moreover, at the end of every course, you will be subjected to a project where you can apply your knowledge thought in the class to solve real-world problems.

## What is Data Science?

*Data science* provides meaningful information based on large amounts of complex data or big data. Data science combines different fields of work in statistics and computation to interpret data for decision-making purposes.

A data scientist collects, analyzes, and interprets large volumes of data, in many cases, to improve a company’s operations. Data science professionals develop statistical models that analyze data and detect patterns, trends, and relationships in data sets.

## About the R programming language

When you see powerful analytics, statistics, and visualizations used by data scientists and business leaders, chances are that the *R language* is behind them. Open-source R is the statistical programming language that data experts the world overuse for everything from mapping broad social and marketing trends online to developing financial and climate models that help drive our economies and communities. *R lets experts quickly, easily interpret and interact with and visualize data.*

## Why Data Science with R programming?

R is the most popular language in the world of Data Science. It is heavily used in analyzing data that is both structured and unstructured. This has made R, the standard language for performing statistical operations. R also allows various features that set it apart from other Data Science languages. R plays a very vital role in Data Science, you will be benefited from the following operations in R programming:

**1. You can run your code without any compiler**

- R is an interpreted language. Hence you can run code without any compiler. R interprets the code and makes the development of code easier.

#### 2. Many calculations done with vectors

- R is a vector language, so anyone can add functions to a single Vector without putting in a loop. Hence, R is powerful and faster than other languages.

#### 3. Statistical Language

- R used in biology, genetics as well as in statistics. R is a turning complete language where any type of task can perform.

## What you will learn from the Data Science with R certification

- Business analytics
- R programming and its packages
- Data structures and data visualization
- Apply functions and DPLYR function
- Graphics in R for data visualization
- Hypothesis testing
- Apriori algorithm
- Kmeans and DBSCAN clustering

*Join the rapidly growing community of R users worldwide to see how open-source R continues to shape the future of statistical analysis and data science.*

## Syllabus

### Lesson 1: Data Science with R certification: Introduction to Business Analytics

- Business Decisions and Analytics, Types of Business Analytics & Applications of Business Analytics

### Lesson 2: Data Science with R certification: Introduction to R Programming

- Importance of R
- Data Types, Variables in R & Operators in R
- Conditional Statements in R, Loops in R, R script & Functions in R

### Lesson 3: Data Structures

- Identifying Data Structures with demo
- Assigning Values to Data Structures & Data Manipulation with demo

### Lesson 4: Data Visualization

- Introduction to Data Visualization
- Data Visualization using Graphics in R
- ggplot2 and File Formats of Graphics Outputs

### Lesson 5: Statistics for Data Science-I

- Introduction to Hypothesis
- Types of Hypothesis
- Data Sampling
- Confidence and Significance Levels

### Lesson 6: Statistics for Data Science-II

- Parametric Test
- Non-Parametric Test
- Hypothesis Tests about Population Means
- Hypothesis Tests about Population Proportions

### Lesson 7: Regression Analysis

- Introduction to Regression Analysis
- Types of Regression Analysis Models Linear Regression
- Demo: Simple Linear Regression and Non-Linear Regression
- Demo: Regression Analysis with Multiple Variables
- Cross-Validation and Non-Linear to Linear Models
- Principal Component Analysis and Factor Analysis

### Lesson 8: Classification

- Classification and Its Types
- Logistic Regression
- Support Vector Machines and Demo: Support Vector Machines
- K-Nearest Neighbours, Naive Bayes Classifier, Naive Bayes Classifier, and Decision Tree Classification
- Decision Tree Classification and Random Forest Classification
- Evaluating Classifier Models and Demo: K-Fold Cross-Validation

### Lesson 9: Clustering

- Introduction to Clustering and Clustering Methods
- Demo: K-means Clustering and Hierarchical Clustering

### Lesson 10: Association

- Association Rule and Apriori Algorithm with demo

## Pre-requisites for Data Science with R certification

There are no prerequisites for this Data Science with R certification course. If you are a beginner in Data Science, this is one of the best courses to start with.

*Note: Your review matters*

*If you have already done this Data Science with R certification 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

- About our policies and review criteria.
- How can you choose and compare online courses?
- How to add Courses to your Wishlist?
- You can suggest courses to add to our website.

## Description

## About this Course on Data Science with R certification

The Data Science with R certification covers data exploration, data visualization, predictive analytics, and descriptive analytics techniques with the R language. You will learn about R packages, how to import and export data in R, data structures in R, various statistical concepts, cluster analysis, and forecasting.

Moreover, at the end of every course, you will be subjected to a project where you can apply your knowledge thought in the class to solve real-world problems.

## What is Data Science?

*Data science* provides meaningful information based on large amounts of complex data or big data. Data science combines different fields of work in statistics and computation to interpret data for decision-making purposes.

A data scientist collects, analyzes, and interprets large volumes of data, in many cases, to improve a company’s operations. Data science professionals develop statistical models that analyze data and detect patterns, trends, and relationships in data sets.

## About the R programming language

When you see powerful analytics, statistics, and visualizations used by data scientists and business leaders, chances are that the *R language* is behind them. Open-source R is the statistical programming language that data experts the world overuse for everything from mapping broad social and marketing trends online to developing financial and climate models that help drive our economies and communities. *R lets experts quickly, easily interpret and interact with and visualize data.*

## Why Data Science with R programming?

R is the most popular language in the world of Data Science. It is heavily used in analyzing data that is both structured and unstructured. This has made R, the standard language for performing statistical operations. R also allows various features that set it apart from other Data Science languages. R plays a very vital role in Data Science, you will be benefited from the following operations in R programming:

**1. You can run your code without any compiler**

- R is an interpreted language. Hence you can run code without any compiler. R interprets the code and makes the development of code easier.

#### 2. Many calculations done with vectors

- R is a vector language, so anyone can add functions to a single Vector without putting in a loop. Hence, R is powerful and faster than other languages.

#### 3. Statistical Language

- R used in biology, genetics as well as in statistics. R is a turning complete language where any type of task can perform.

## What you will learn from the Data Science with R certification

- Business analytics
- R programming and its packages
- Data structures and data visualization
- Apply functions and DPLYR function
- Graphics in R for data visualization
- Hypothesis testing
- Apriori algorithm
- Kmeans and DBSCAN clustering

*Join the rapidly growing community of R users worldwide to see how open-source R continues to shape the future of statistical analysis and data science.*

## Syllabus

### Lesson 1: Data Science with R certification: Introduction to Business Analytics

- Business Decisions and Analytics, Types of Business Analytics & Applications of Business Analytics

### Lesson 2: Data Science with R certification: Introduction to R Programming

- Importance of R
- Data Types, Variables in R & Operators in R
- Conditional Statements in R, Loops in R, R script & Functions in R

### Lesson 3: Data Structures

- Identifying Data Structures with demo
- Assigning Values to Data Structures & Data Manipulation with demo

### Lesson 4: Data Visualization

- Introduction to Data Visualization
- Data Visualization using Graphics in R
- ggplot2 and File Formats of Graphics Outputs

### Lesson 5: Statistics for Data Science-I

- Introduction to Hypothesis
- Types of Hypothesis
- Data Sampling
- Confidence and Significance Levels

### Lesson 6: Statistics for Data Science-II

- Parametric Test
- Non-Parametric Test
- Hypothesis Tests about Population Means
- Hypothesis Tests about Population Proportions

### Lesson 7: Regression Analysis

- Introduction to Regression Analysis
- Types of Regression Analysis Models Linear Regression
- Demo: Simple Linear Regression and Non-Linear Regression
- Demo: Regression Analysis with Multiple Variables
- Cross-Validation and Non-Linear to Linear Models
- Principal Component Analysis and Factor Analysis

### Lesson 8: Classification

- Classification and Its Types
- Logistic Regression
- Support Vector Machines and Demo: Support Vector Machines
- K-Nearest Neighbours, Naive Bayes Classifier, Naive Bayes Classifier, and Decision Tree Classification
- Decision Tree Classification and Random Forest Classification
- Evaluating Classifier Models and Demo: K-Fold Cross-Validation

### Lesson 9: Clustering

- Introduction to Clustering and Clustering Methods
- Demo: K-means Clustering and Hierarchical Clustering

### Lesson 10: Association

- Association Rule and Apriori Algorithm with demo

## Pre-requisites for Data Science with R certification

There are no prerequisites for this Data Science with R certification course. If you are a beginner in Data Science, this is one of the best courses to start with.

*Note: Your review matters*

*If you have already done this Data Science with R certification 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:

- Simplilearn
- Online Course
- Self-paced
- Advanced
- 1-4 Weeks
- Paid Course (Paid certificate)
- English
- Data Analysis Data Science Data Science with 'R' Machine learning

There are no reviews yet.