Intermediate Data Science

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Introduction to R:
1.What is R?
2.Why R?
3.Installing R
4.R environment
5.How to get help in R
6.R Studio Overview
Understanding R data structure:
1.Variables in R
6.Data frames
7.Cbind,Rbind, attach and detach functions in R
9.Getting a subset of Data
10.Missing values
11.Converting between vector types
Importing data:
1.Reading Tabular Data files
2.Reading CSV files
3.Importing data from excel
4.Loading and storing data with clipboard
5.Accessing database
6.Saving in R data
7.Loading R data objects
8.Writing data to file
9.Writing text and output from analyses to file
Manipulating Data:
1.Selecting rows/observations
2.Rounding Number
3.Creating string from variable
4.Search and Replace a string or Number
5.Selecting columns/fields
6.Merging data
7.Relabeling the column names
8.Data sorting
9.Data aggregation
10.Finding and removing duplicate records
Using functions in R:
1.Apply Function Family
2.Commonly used Mathematical Functions
3.Commonly used Summary Functions
4.Commonly used String Functions
5.User defined functions
6.local and global variable
7.Working with dates
R Programming:
1.While loop
2.If loop
3.For loop
4.Arithmetic operations
Charts and Plots:
1.Box plot
3.Pie graph
4.Line chart
6.Developing graphs
7.Cover all the current trending packages for Graphs
Machine Learning Algorithm:
1.Sentiment analysis with Machine learning
3.Support vector Machines
4.K Means
5.Random Forest
6.Naïve Bayes algorithm
2.Linear Regression
3.Non Linear Regression
4.Predictive time series forecasting
5.K means clustering
6.P value
7.Find outlier
8.Neural Network
9.Error Measure
Leading Topics:
1.Overture of R Shiny
2.What is Hadoop
3.Integration of Hadoop in R
4.Data Mining using R
5.Clinical research preface in R
6.API in R (Twitter and Facebook)
7.Word Cloud in R