Data Science Training in Bangalore

Introduction to R:

• What is R?
• Why R?
• Installing R
• R environment
• How to get help in R
• R Studio Overview

Understanding R data structure:

• Variables in R
• Scalars
• Vectors
• Matrices
• List
• Data frames
• Cbind,Rbind, attach and detach functions in R
• Factors
• Getting a subset of Data
• Missing values
• Converting between vector types

Importing data:

• Reading Tabular Data files
• Reading CSV files
• Importing data from excel
• Loading and storing data with clipboard
• Accessing database
• Saving in R data
• Loading R data objects
• Writing data to file
• Writing text and output from analyses to file

Manipulating Data:

• Selecting rows/observations
• Rounding Number
• Creating string from variable
• Search and Replace a string or Number
• Selecting columns/fields
• Merging data
• Relabeling the column names
• Data sorting
• Data aggregation
• Finding and removing duplicate records

Using functions in R:

• Apply Function Family
• Commonly used Mathematical Functions
• Commonly used Summary Functions
• Commonly used String Functions
• User defined functions
• local and global variable
• Working with dates

R Programming:

• While loop
• If loop
• For loop
• Arithmetic operations

Charts and Plots:

• Box plot
• Histogram
• Pie graph
• Line chart
• Scatterplot
• Developing graphs
• Cover all the current trending packages for Graphs

Machine Learning Algorithm:

• Sentiment analysis with Machine learning
• C 5.0
• Support vector Machines
• K Means
• Random Forest
• Naïve Bayes algorithm

Statistics:

• Correlation
• Linear Regression
• Non Linear Regression
• Predictive time series forecasting
• K means clustering
• P value
• Find outlier
• Neural Network
• Error Measure

Leading Topics:

• Overture of R Shiny
• What is Hadoop
• Integration of Hadoop in R
• Data Mining using R
• Clinical research preface in R
• API in R (Twitter and Facebook)
• Word Cloud in R