Program **Overview:**

The Data Science with R Certification course enables you to take your data science skills

into a variety of companies, helping them analyze data and make more informed business

decisions.The course 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.

Program **Features:**

- 64 hours of applied learning
- 10 real-life industry projects
- 365 days of access

Delivery **Mode:**

**Online Bootcamp –** Online self-paced learning

**Prerequisites:**

There are no prerequisites for this Data Science Certification with R programming course. If you

are a beginner in data science, this is one of the best courses to start with.

Target **Audience:**

There is an increasing demand for skilled data scientists across all industries, making this

data science certification course well-suited for participants at all levels of experience. We

recommend this data science training particularly for the following professionals:

- IT professionals
- Analytics Professionals
- Software developers

Key Learning **Outcomes:**

When you complete this data science course, you will be able to accomplish the following:

- Gain a foundational understanding of business analytics
- Install R, RStudio, workspace setup, and learn about the various R packages
- Master R programming and understand how various statements are executed in R
- Gain an in-depth understanding of data structure used in R and learn to import/export data in R
- Define, understand and use the various apply functions and DPLYR functions
- Understand and use the various graphics in R for data visualization
- Gain a basic understanding of various statistical concepts
- Understand and use the hypothesis testing method to drive business decisions
- Understand and use linear and non-linear regression models, and classification techniques for data analysis
- Learn and use the various association rules with the Apriori algorithm
- Learn and use clustering methods including k-means, DBSCAN, and hierarchical clustering

Certification Details and **Criteria:**

**Online Classroom**

- Attend one complete batch of Data Science Certification with R programming training
- Complete one project

**Online Self-Learning:**

- Complete 85 percent of the course.
- Complete one project

Course **Curriculum:**

**Lesson 01 –** Introduction to Business Analytics

- Overview
- Business Decisions and Analytics
- Types of Business Analytics
- Applications of Business Analytics
- Data Science Overview
- Conclusion
- Knowledge Check

**Lesson 02 –** Introduction to R Programming

- Overview
- Importance of R
- Data Types and Variables in R
- Operators in R
- Conditional Statements in R
- Loops in R
- R script
- Functions in R
- Conclusion
- Knowledge Check

**Lesson 03 –** Data Structures

- Overview
- Identifying Data Structures
- Demo: Identifying Data Structures
- Assigning Values to Data Structures
- Data Manipulation
- Demo: Assigning Values and Applying Functions
- Conclusion
- Knowledge Check

**Lesson 04 –** Data Visualization

- Overview
- Introduction to Data Visualization
- Data Visualization Using Graphics in R
- Ggplot2
- File Formats of Graphic Outputs R
- Conclusion
- Knowledge Check

**Lesson 05 –** Statistics for Data Science-I

- Overview
- Introduction to Hypothesis
- Types of Hypothesis
- Data Sampling
- Confidence and Significance Levels
- Conclusion
- Knowledge Check

**Lesson 06 –** Statistics for Data Science-II

- Overview
- Hypothesis Test
- Parametric Test
- Non-Parametric Test
- Hypothesis Tests about Population Means
- Hypothesis Tests about Population Variance
- Hypothesis Tests about Population Proportions
- Conclusion
- Knowledge Check

**Lesson 07 –** Regression Analysis

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

**Lesson 08 –** Classification

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

**Lesson 09 –** Clustering

- Overview
- Introduction to Clustering
- Clustering Methods
- Demo: K-means Clustering
- Demo: Hierarchical Clustering
- Conclusion
- Knowledge Check

**Lesson 10 –** Association

- Overview
- Association Rule
- Apriori Algorithm
- Demo: Apriori Algorithm
- Conclusion
- Knowledge Check