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
2.Scalars
3.Vectors
4.Matrices
5.List
6.Data frames
7.Cbind,Rbind, attach and detach functions in R
8.Factors
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
2.Histogram
3.Pie graph
4.Line chart
5.Scatterplot
6.Developing graphs
7.Cover all the current trending packages for Graphs
Machine Learning Algorithm:
1.Sentiment analysis with Machine learning
2.C5.0
3.Support vector Machines
4.K Means
5.Random Forest
6.Naïve Bayes algorithm
Statistics:
1.Correlation
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