Duration
2 days
Outline
Introduction to business analytics
- What is analytics
- Need of Analytics
- Business analytics vs business analysis
- Business intelligence vs Data Science
- Data Analyst Vs Business Analyst
- Types of Analytics
- Tools for Analytics
Introduction to R Language
- What is R?
- Data science & R
- Components of R
- Installing R
- Using command line in R
- Introduction to R Studio (IDE)
R Language Constructs
- Data types in R
- Program Structure in R
- Flow Control: For loop
- If condition
- While conditions and repeat loop
- Debugging tools
- Concatenation of Data
- Combining Vars , cbind, rbind
- Sapply, apply, tapply functions
- Built – in functions in R
R data structures
- Introduction to Data Structure in R
- Vectors
- Lists
- Scalars
- Data Frames
- Matrices
- Arrays
- Factors
File Handling in R
- File operations in R
- Reading file
- Writing to a file
- Importing and exporting a file
Using R for data visualization
- What is Data Visualization?
- Data Visualization tools in the market
- Using graphical functions in R for data visualization
- Line Plots
- Bar Plots
- Plotting with base graphics
- Plotting and coloring in R
Basics of statistics
- Quantitative Vs Qualitative Analysis
- Normal & Standard Deviations
Linear Regression
- What is regression
- Dependency of variables
- What is linear regression
- Understand with examples
Predictive Analytics I
- What is decision Tree
- Need for decision Tree
- Use case (Scenario of usage)
- How does it Work?
Predictive Analytics II
- What is clustering
- Use case (Scenario of usage)
- Clustering Models
- K-means clustering
- Case study
- Agglomerative hierarchical Method
Programme scope, delivery arrangements and suitability are confirmed during enquiry.