- 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

- Data Scientist
- Data Analyst
- IT Professionals
- Software Developers

### Data Science with R Programming

#### Lesson 00 - Course Introduction

01:31##### Course Introduction

01:31

#### Lesson 01 - Introduction to Business Analytics

21:06##### 1.001 Overview

00:44##### 1.002 Business Decisions and Analytics

04:33##### 1.003 Types of Business Analytics

03:53##### 1.004 Applications of Business Analytics

08:57##### 1.005 Data Science Overview

01:29##### 1.006 Conclusion

01:30##### Knowledge Check

#### Lesson 02 - Introduction to R Programming

26:35##### 2.001 Overview

00:31##### 2.002 Importance of R

05:20##### 2.003 Data Types and Variables in R

02:14##### 2.004 Operators in R

04:39##### 2.005 Conditional Statements in R

02:45##### 2.006 Loops in R

05:07##### 2.007 R script

01:44##### 2.008 Functions in R

02:58##### 2.009 Conclusion

01:17##### Knowledge Check

#### Lesson 03 - Data Structures

50:57##### 3.001 Overview

01:04##### 3.002 Identifying Data Structures

13:14##### 3.003 Demo Identifying Data Structures

14:05##### 3.004 Assigning Values to Data Structures

04:51##### 3.005 Data Manipulation

09:23##### 3.006 Demo Assigning values and applying functions

07:46##### 3.007 Conclusion

00:34##### Knowledge Check

#### Lesson 04 - Data Visualization

29:40##### 4.001 Overview

00:29##### 4.002 Introduction to Data Visualization

03:03##### 4.003 Data Visualization using Graphics in R

18:50##### 4.004 ggplot2

05:14##### 4.005 File Formats of Graphic Outputs

01:08##### 4.006 Conclusion

00:56##### Knowledge Check

#### Lesson 05 - Statistics for Data Science-I

14:10##### 5.001 Overview

00:21##### 5.002 Introduction to Hypothesis

02:06##### 5.003 Types of Hypothesis

03:13##### 5.004 Data Sampling

02:48##### 5.005 Confidence and Significance Levels

04:33##### 5.006 Conclusion

01:09##### Knowledge Check

#### Lesson 06 - Statistics for Data Science-II

29:55##### 6.001 Overview

00:28##### 6.002 Hypothesis Test

00:47##### 6.003 Parametric Test

14:36##### 6.004 Non-Parametric Test

08:31##### 6.005 Hypothesis Tests about Population Means

02:09##### 6.006 Hypothesis Tests about Population Variance

00:45##### 6.007 Hypothesis Tests about Population Proportions

01:11##### 6.008 Conclusion

01:28##### Knowledge Check

#### Lesson 07 - Regression Analysis

45:04##### 7.001 Overview

00:26##### 7.002 Introduction to Regression Analysis

01:11##### 7.003 Types of Regression Analysis Models

01:38##### 7.004 Linear Regression

08:59##### 7.005 Demo Simple Linear Regression

07:29##### 7.006 Non-Linear Regression

03:49##### 7.007 Demo Regression Analysis with Multiple Variables

13:29##### 7.008 Cross Validation

01:48##### 7.009 Non-Linear to Linear Models

02:06##### 7.010 Principal Component Analysis

02:45##### 7.011 Factor Analysis

00:26##### 7.012 Conclusion

00:58##### Knowledge Check

#### Lesson 08 - Classification

01:05:14##### 8.001 Overview

00:31##### 8.002 Classification and Its Types

04:24##### 8.003 Logistic Regression

03:35##### 8.004 Support Vector Machines

04:26##### 8.005 Demo Support Vector Machines

11:13##### 8.006 K-Nearest Neighbours

02:34##### 8.007 Naive Bayes Classifier

02:53##### 8.008 Demo Naive Bayes Classifier

06:15##### 8.009 Decision Tree Classification

09:47##### 8.010 Demo Decision Tree Classification

06:25##### 8.011 Random Forest Classification

02:01##### 8.012 Evaluating Classifier Models

06:04##### 8.013 Demo K-Fold Cross Validation

04:09##### 8.014 Conclusion

00:57##### Knowledge Check

#### Lesson 09 - Clustering

28:10##### 9.001 Overview

00:17##### 9.002 Introduction to Clustering

02:57##### 9.003 Clustering Methods

07:47##### 9.004 Demo K-means Clustering

11:15##### 9.005 Demo Hierarchical Clustering

05:02##### 9.006 Conclusion

00:52##### Knowledge Check

#### Lesson 10 - Association

23:13##### 10.001 Overview

00:15##### 10.002 Association Rule

06:20##### 10.003 Apriori Algorithm

05:19##### 10.004 Demo Apriori Algorithm

10:37##### 10.005 Conclusion

00:42##### Knowledge Check

expected growth of big data analytics market

than the the rest of the IT industry as per Randstad report

- Average Salary
### $43K - $95K Per Annum

Hiring Companies - Average Salary
### $83K - $154K Per Annum

Hiring Companies

### What are the requirements to learn the Data Science with R basics program?

There are no requirements to take this Data Science with R basics program. However, knowledge of any programming language and core mathematics would be an added advantage.

### How do beginners learn data science basics?

Beginners can learn about data science by getting an overview of business analytics, how data-driven decisions help achieve more profits, and a broad understanding of how data is analyzed.

### How long does it take to learn data science with R?

Professionals with a technical background and some prior knowledge of data science tend to learn progress faster in learning data science. Beginners can also achieve a similar pace by taking this data science R basics course, as it helps you learn the basics from scratch.

### What should I learn first in the Data Science with R fundamentals program?

Data science learners can first get a basic understanding of statistics, core math concepts, data manipulation and data analysis in this program.

### Is the Data Science with R basics program easy to learn?

This data science with R basics program has been designed keeping different learner needs in mind. Learners who don’t have any clear idea about data science can still follow this program easily.

### What are the requirements for this Data Science with R basics training program?

The numerical computations and algorithms in data science involve mathematics concepts like statistics, probability, and calculus at a basic level. Knowledge of databases and programming are also fundamental to learning data science with R.

- Disclaimer
- PMP, PMI, PMBOK, CAPM, PgMP, PfMP, ACP, PBA, RMP, SP, and OPM3 are registered marks of the Project Management Institute, Inc.