Data Science With R Programming

This course is the perfect option for aspiring data analytics that will help them to build a successful career in analytics or data sciences. The data science certification training course will have an all-around knowledge of business analytics and R.

This data analytics with R training teaches students in depth knowledge of various data analytics techniques that can be performed using R.

This course teaches in detail about the r language, r studio, and other r packages. Students will learn various methods to apply the functions of r such as DPYR, gain an understanding of data structure and perform data visualization using the various available graphics in R.

By the end of this course, the students would have mastered advanced statistical concepts such as linear and logistic regression, cluster analysis and forecasting along with data science training course.

Prerequisites: 

The data science certification training course requires absolutely no knowledge of this background. People absolutely new to field of data analytics and science must take up this basic level course to get started.

Target Students:

With the increasing demand for skilled data scientist, students such as IT professionals, software developer and professionals working in data and business analytics can take up this course. Graduates, students or experienced professionals who want to learn more about data sciences can also take up this course.

Outline:

• Lesson 01 – Introduction to Business Analytics
• Overview
• Business Decisions and Analytics
• Types of Business Analytics
• Applications of Business Analytics
• Data Science Overview
• 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
• 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
• Lesson 04 – Data Visualization
• Overview
• Introduction to Data Visualization
• Data Visualization using Graphics in R
• ggplot2
• File Formats of Graphic Outputs
• Lesson 05 – Statistics for Data Science-I
• Overview
• Introduction to Hypothesis
• Types of Hypothesis
• Data Sampling
• Confidence and Significance Levels
• 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
• 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
• 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
• Lesson 09 – Clustering
• Overview
• Introduction to Clustering
• Clustering Methods
• Demo K-means Clustering
• Demo Hierarchical Clustering
• Lesson 10 – Association
• Overview
• Association Rule
• Apriori Algorithm
• Demo Apriori Algorithm