The Data Science Certification with R programming training covers data exploration, data visualization, predictive analytics, and descriptive analytics techniques with the R language. You will learn about R packages, how to import and export data in R, data structures in R, various statistical concepts, cluster analysis, and forecasting.
Introduction
♦ 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
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