Data Science with R
Course Overview
R is a powerful language used widely for data analysis and statistical computing. In this course, you will be able to learn data science using R. By the end of this course, you will have good exposure to building predictive models using machine learning on your own.
At the end of the training, participants will be able to:
- Explain the concept of Business Decisions and Analytics
- Understand the R Programming
- Describe Data Structures
- Discuss Data Visualization
- Understand Statistics
- Explain the Regression Analysis
- Describe Classification
- Explain Clustering
- Understand Association
Pre-requisite
Some knowledge of programming and statistics
Duration
3 days
Course Outline
- Business Decisions
- Business Analysis
- Types of Analytics
- Applications of Business Analytics
- R for Data Analytics
- Steps to perform Data Analysis in R
- Basic Syntax in R
- Data Types and Variables
- Operators
- Conditional Statements
- Loops
- Loop Control Statements
- Functions
- Components of a Function
- Built-in Functions
- Steps for working with data
- Identifying Data Structures
- Assigning values to Data Structures
- Manipulating Data
- Introduction
- Graphics used for Data Visualization
- ggplot2
- File formats of graphic outputs
- What is Hypothesis?
- Types of Hypothesis
- Types of Data Sampling
- Types of Errors
- Confidence Level
- Critical Region
- Level of Significance
- Accordion Content
- Hypothesis Test
- Types of Hypothesis Test
- Hypothesis Test about population means
- Hypothesis Test about population Variance
- Hypothesis Test about population Proportions
- What is Regression Analysis?
- Types of Regression Models
- Linear Regression
- Non-linear Regression
- Non-linear to Linear Models
- Principle Component Analysis
- What is Classification
- Types of Classification
- Logistic Regression
- Support Vector Machines
- K-Nearest Neighbors(KNN)
- Naïve Bayes Classifier
- Decision Trees
- Random Forest Classification
- Evaluating Classifier Models
- Clustering and its applications
- Clustering Methods
- K-means Clustering
- Hierarchical Clustering
- Density-based Clustering
- Association Rules
- Apriori Algorithm